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T1 0-106 Sentence denotes Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China
T2 108-116 Sentence denotes Abstract
T3 117-245 Sentence denotes This study models local and cross-city transmissions of the novel coronavirus in China between January 19 and February 29, 2020.
T4 246-397 Sentence denotes We examine the role of various socioeconomic mediating factors, including public health measures that encourage social distancing in local communities.
T5 398-492 Sentence denotes Weather characteristics 2 weeks prior are used as instrumental variables for causal inference.
T6 493-641 Sentence denotes Stringent quarantines, city lockdowns, and local public health measures imposed in late January significantly decreased the virus transmission rate.
T7 642-699 Sentence denotes The virus spread was contained by the middle of February.
T8 700-887 Sentence denotes Population outflow from the outbreak source region posed a higher risk to the destination regions than other factors, including geographic proximity and similarity in economic conditions.
T9 888-1017 Sentence denotes We quantify the effects of different public health measures in reducing the number of infections through counterfactual analyses.
T10 1018-1185 Sentence denotes Over 1.4 million infections and 56,000 deaths may have been avoided as a result of the national and provincial public health measures imposed in late January in China.
T11 1187-1199 Sentence denotes Introduction
T12 1200-1351 Sentence denotes The first pneumonia case of unknown cause was found close to a seafood market in Wuhan, the capital city of Hubei province, China, on December 8, 2019.
T13 1352-1445 Sentence denotes Several clusters of patients with similar pneumonia were reported through late December 2019.
T14 1446-1591 Sentence denotes The pneumonia was later identified to be caused by a new coronavirus (severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2) (Zhu et al.
T15 1592-1924 Sentence denotes 2020), later named Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO).1 While the seafood market was closed on January 1, 2020, a massive outflow of travelers during the Chinese Spring Festival travel rush (Chunyun) in mid-January2 led to the rapid spread of COVID-19 throughout China and to other countries.
T16 1925-2022 Sentence denotes The first confirmed case outside Wuhan in China was reported in Shenzhen on January 19 (Li et al.
T17 2023-2029 Sentence denotes 2020).
T18 2030-2134 Sentence denotes As of April 5, over 1.2 million confirmed cases were reported in at least 200 countries or territories.3
T19 2135-2357 Sentence denotes Two fundamental strategies have been taken globally, one focused on mitigating but not necessarily stopping the virus spread and the other relying on more stringent measures to suppress and reverse the growth trajectories.
T20 2358-2687 Sentence denotes While most Western countries initially implemented the former strategy, more and more of them (including most European countries and the USA) have since shifted towards the more stringent suppression strategy, and some other countries such as China, Singapore, and South Korea have adopted the latter strategy from the beginning.
T21 2688-2775 Sentence denotes In particular, China has rolled out one of the most stringent public health strategies.
T22 2776-3010 Sentence denotes That strategy involves city lockdowns and mandatory quarantines to ban or restrict traffic since January 23, social distance–encouraging strategies since January 28, and a centralized treatment and isolation strategy since February 2.
T23 3011-3308 Sentence denotes This study estimates how the number of daily newly confirmed COVID-19 cases in a city is influenced by the number of new COVID-19 cases in the same city, nearby cities, and Wuhan during the preceding 2 weeks using the data on confirmed COVID-19 case counts in China from January 19 to February 29.
T24 3309-3470 Sentence denotes By comparing the estimates before and after February 2, we examine whether the comprehensive set of policies at the national scale delays the spread of COVID-19.
T25 3471-3711 Sentence denotes Besides, we estimate the impacts of social distancing measures in reducing the transmission rate utilizing the closed management of communities and family outdoor restrictions policies that were gradually rolled out across different cities.
T26 3712-3940 Sentence denotes As COVID-19 evolves into a global pandemic and mitigating strategies are faced with growing pressure to flatten the curve of virus transmissions, more and more nations are considering implementing stringent suppression measures.
T27 3941-4277 Sentence denotes Therefore, examining the factors that influence the transmission of COVID-19 and the effectiveness of the large-scale mandatory quarantine and social distancing measures in China not only adds to our understanding of the containment of COVID-19 but also provides insights into future prevention work against similar infectious diseases.
T28 4278-4463 Sentence denotes In a linear equation of the current number of new cases on the number of new cases in the past, the unobserved determinants of new infections may be serially correlated for two reasons.
T29 4464-4570 Sentence denotes First, the number of people infected by a disease usually first increases, reaches a peak, and then drops.
T30 4571-4733 Sentence denotes Second, there are persistent, unobservable variables, such as clusters that generate large numbers of infections, people’s living habits, and government policies.
T31 4734-4905 Sentence denotes Serial correlations in errors give rise to correlations between the lagged numbers of cases and the error term, rendering the ordinary least square (OLS) estimator biased.
T32 4906-5148 Sentence denotes Combining insights in Adda (2016), the existing knowledge of the incubation period of COVID-19 (World Health Organization 2020b), and data on weather conditions that affect the transmission rates of COVID-19 (Lowen and Steel 2014; Wang et al.
T33 5149-5259 Sentence denotes 2020b), we construct instrumental variables for the number of new COVID-19 cases during the preceding 2 weeks.
T34 5260-5497 Sentence denotes Weather characteristics in the previous third and fourth weeks do not directly affect the number of new COVID-19 cases after controlling for the number of new COVID-19 cases and weather conditions in the preceding first and second weeks.
T35 5498-5601 Sentence denotes Therefore, our estimated impacts have causal interpretations and reflect population transmission rates.
T36 5602-5713 Sentence denotes Meanwhile, we estimate the mediating effects of socioeconomic factors on the transmission of COVID-19 in China.
T37 5714-5873 Sentence denotes These factors include population flow out of Wuhan, the distance between cities, GDP per capita, the number of doctors, and contemporaneous weather conditions.
T38 5874-6169 Sentence denotes We examine whether population flows from the origin of the COVID-19 outbreak, which is a major city and an important transportation hub in central China, can explain the spread of the virus using data on real-time travel intensity between cities that have recently become available for research.
T39 6170-6392 Sentence denotes Realizing the urgency of forestalling widespread community transmissions in areas that had not seen many infections, in late January, many Chinese cities implemented public health measures that encourage social distancing.
T40 6393-6477 Sentence denotes We also examine the impacts of these measures on curtailing the spread of the virus.
T41 6478-6613 Sentence denotes We find that transmission rates were lower in February than in January, and cities outside Hubei province had lower transmission rates.
T42 6614-6788 Sentence denotes Preventing the transmission rates in non-Hubei cities from increasing to the level observed in late January in Hubei caused the largest reduction in the number of infections.
T43 6789-6965 Sentence denotes Apart from the policies implemented nationwide, the additional social distancing policies imposed in some cities in late January further helped reduce the number of infections.
T44 6966-7030 Sentence denotes By mid February, the spread of the virus was contained in China.
T45 7031-7264 Sentence denotes While many socioeconomic factors moderated the spread of the virus, the actual population flow from the source posed a higher risk to destinations than other factors such as geographic proximity and similarity in economic conditions.
T46 7265-7334 Sentence denotes Our analysis contributes to the existing literature in three aspects.
T47 7335-7503 Sentence denotes First, our analysis is connected to the economics and epidemiological literature on the determinants of the spread of infectious diseases and prevention of such spread.
T48 7504-7573 Sentence denotes Existing studies find that reductions in population flow (Zhan et al.
T49 7574-7592 Sentence denotes 2020; Zhang et al.
T50 7593-7610 Sentence denotes 2020; Fang et al.
T51 7611-7727 Sentence denotes 2020) and interpersonal contact from holiday school closings (Adda 2016), reactive school closures (Litvinova et al.
T52 7728-8045 Sentence denotes 2019), public transportation strikes (Godzinski and Suarez Castillo 2019), strategic targeting of travelers from high-incidence locations (Milusheva 2017), and paid sick leave to keep contagious workers at home (Barmby and Larguem 2009; Pichler and Ziebarth 2017) can mitigate the prevalence of disease transmissions.
T53 8046-8223 Sentence denotes In addition, studies show viruses spread faster during economic booms (Adda 2016), increases in employment are associated with increased incidence of influenza (Markowitz et al.
T54 8224-8335 Sentence denotes 2019), and growth in trade can significantly increase the spread of influenza (Adda 2016) and HIV (Oster 2012).
T55 8336-8486 Sentence denotes Vaccination (Maurer 2009; White 2019) and sunlight exposure (Slusky and Zeckhauser 2018) are also found effective in reducing the spread of influenza.
T56 8487-8724 Sentence denotes Second, our paper adds to the epidemiological studies on the basic reproduction number (R0) of COVID-19, i.e., the average number of cases directly generated by one case in a population where all individuals are susceptible to infection.
T57 8725-8940 Sentence denotes Given the short time period since the beginning of the COVID-19 outbreak, research is urgently needed to assess the dynamics of transmissions and the implications for how the COVID-19 outbreak will evolve (Wu et al.
T58 8941-8955 Sentence denotes 2020b, 2020c).
T59 8956-9564 Sentence denotes Liu et al. (2020) identify 12 studies that estimated the basic reproductive number in the wide range of 1.4 to 6.5 (with a mean of 3.28 and a median of 2.79) for Wuhan, Hubei, China, or overseas during January 1 through January 28, 2020.4 Our R0 estimate relies on spatially disaggregated data during an extended period (until February 29, 2020) to mitigate potential biases, and the instrumental variable approach we use isolates the causal effect of virus transmissions and imposes fewer restrictions on the relationship between the unobserved determinants of new cases and the number of cases in the past.
T60 9565-9835 Sentence denotes Simultaneously considering a more comprehensive set of factors in the model that may influence virus spread, we find that one case generates 2.992 more cases within 2 weeks (1.876 if cities in Hubei province are excluded) in the sub-sample from January 19 to February 1.
T61 9836-9956 Sentence denotes In the sub-sample from February 2 to February 29, the transmission rates fall to 1.243 (0.614 excluding Hubei province).
T62 9957-10143 Sentence denotes Our estimate of R0 for the period in late January 2020 that overlaps with existing studies falls well within the range of the estimated R0 in the emerging COVID-19 literature (Liu et al.
T63 10144-10150 Sentence denotes 2020).
T64 10151-10278 Sentence denotes Third, our study contributes to the assessments of public health measures aiming at reducing virus transmissions and mortality.
T65 10279-10446 Sentence denotes Through a set of policy simulations, we report initial evidence on the number of avoided infections through the end of February 2020 for cities outside Hubei province.
T66 10447-10793 Sentence denotes Specifically, the stringent health policies at the national and provincial levels reduced the transmission rate and resulted in 1,408,479 (95% CI, 815,585 to 2,001,373) fewer infections and potentially 56,339 fewer deaths.5 In contrast, the effects of the Wuhan lockdown and local non-pharmaceutical interventions (NPIs) are considerably smaller.
T67 10794-11028 Sentence denotes As a result of the Wuhan lockdown, closed management of communities, and family outdoor restrictions, 31,071 (95% CI, 8296 to 53,845), 3803 (95% CI, 1142 to 6465), and 2703 (95% CI, 654 to 4751) fewer cases were avoided, respectively.
T68 11029-11114 Sentence denotes These three policies may respectively avoid 1,243 deaths, 152 deaths, and 108 deaths.
T69 11115-11335 Sentence denotes Making some additional assumptions, such as the value of statistical life and lost productive time, these estimates may provide the basis for more rigorous cost-benefit analysis regarding relevant public health measures.
T70 11336-11371 Sentence denotes This paper is organized as follows.
T71 11372-11413 Sentence denotes Section 2 introduces the empirical model.
T72 11414-11481 Sentence denotes Section 3 discusses our data and the construction of key variables.
T73 11482-11513 Sentence denotes Section 4 presents the results.
T74 11514-11652 Sentence denotes Section 5 documents the public health measures implemented in China, whose impacts are quantified in a series of counterfactual exercises.
T75 11653-11673 Sentence denotes Section 6 concludes.
T76 11674-11799 Sentence denotes The Appendix contains additional details on the instrumental variables, data quality, and the computation of counterfactuals.
T77 11801-11816 Sentence denotes Empirical model
T78 11817-11883 Sentence denotes Our analysis sample includes 304 prefecture-level cities in China.
T79 11884-11972 Sentence denotes We exclude Wuhan, the capital city of Hubei province, from our analysis for two reasons.
T80 11973-12066 Sentence denotes First, the epidemic patterns in Wuhan are significantly different from those in other cities.
T81 12067-12226 Sentence denotes Some confirmed cases in Wuhan contracted the virus through direct exposure to Huanan Seafood Wholesale Market, which is the most probable origin of the virus6.
T82 12227-12295 Sentence denotes In other cities, infections arise from human-to-human transmissions.
T83 12296-12555 Sentence denotes Second, COVID-19 cases were still pneumonia of previously unknown virus infections in people’s perception until early January so that Wuhan’s health care system became overwhelmed as the number of new confirmed cases increased exponentially since mid-January.
T84 12556-12713 Sentence denotes This may have caused severe delay and measurement errors in the number of cases reported in Wuhan, and to a lesser extent, in other cities in Hubei province.
T85 12714-12821 Sentence denotes To alleviate this concern, we also conduct analyses excluding all cities in Hubei province from our sample.
T86 12822-12945 Sentence denotes To model the spread of the virus, we consider within-city spread and between-city transmissions simultaneously (Adda 2016).
T87 12946-13162 Sentence denotes Our starting point is yct=∑s=114αwithin,syc,t−s+∑s=114αbetween,s∑r≠cdcr−1yr,t−s+∑s=114ρszt−s+xctβ+𝜖ct, where c is a city other than Wuhan, and yct is the number of new confirmed cases of COVID-19 in city c on date t.
T88 13163-13344 Sentence denotes Regarding between-city transmissions, dcr is the log of the distance between cities c and r, and ∑r≠cdcr−1yrt is the inverse distance weighted sum of new infections in other cities.
T89 13345-13645 Sentence denotes Considering that COVID-19 epidemic originated from one city (Wuhan) and that most of the early cases outside Wuhan can be traced to previous contacts with persons in Wuhan, we also include the number of new confirmed cases in Wuhan (zt) to model how the virus spreads to other cities from its source.
T90 13646-13909 Sentence denotes We may include lagged yct, yrt, and zt up to 14 days based on the estimates of the durations of the infectious period and the incubation period in the literature7. xct includes contemporaneous weather controls, city, and day fixed effects8. 𝜖ct is the error term.
T91 13910-13952 Sentence denotes Standard errors are clustered by province.
T92 13953-14167 Sentence denotes To make it easier to interpret the coefficients, we assume that the transmission dynamics (αwithin,s, αbetween,s, ρs) are the same within s = 1,⋯ ,7 and s = 8,⋯ ,14, respectively, but can be different across weeks.
T93 14168-14369 Sentence denotes Specifically, we take averages of lagged yct, yrt, and zt by week, as y¯ctτ=17∑s=17yct−7τ−1−s, y¯rtτ=17∑s=17yrt−7τ−1−s, and z¯tτ=17∑s=17zt−7τ−1−s, in which τ denotes the preceding first or second week.
T94 14370-14472 Sentence denotes Our main model is 1 yct=∑τ=12αwithin,τy¯ctτ+∑τ=12αbetween,τ∑r≠cdcr−1y¯rtτ+∑τ=12ρτz¯tτ+xctβ+𝜖ct.Model A
T95 14473-14808 Sentence denotes We also consider more parsimonious model specifications, such as the model that only considers within-city transmissions, 2 yct=∑τ=12αwithin,τy¯ctτ+xctβ+𝜖ct,and the model where the time lagged variables are averages over the preceding 2 weeks, yct=αwithin114∑s=114yc,t−s+αbetween114∑s=114∑r≠cdcr−1yr,t−s+ρ114∑s=114zt−s+xctβ+𝜖ct.Model B
T96 14809-14905 Sentence denotes There are several reasons that y¯ctτ, y¯rtτ, and z¯tτ may be correlated with the error term 𝜖ct.
T97 14906-15124 Sentence denotes The unobserved determinants of new infections such as local residents’ and government’s preparedness are likely correlated over time, which causes correlations between the error term and the lagged dependent variables.
T98 15125-15264 Sentence denotes As noted by the World Health Organization (2020b), most cases that were locally generated outside Hubei occurred in households or clusters.
T99 15265-15502 Sentence denotes The fact that big clusters give rise to a large number of cases within a short period of time may still be compatible with a general low rate of community transmissions, especially when measures such as social distancing are implemented.
T100 15503-15656 Sentence denotes Therefore, the coefficients are estimated by two-stage least squares in order to obtain consistent estimates on the transmission rates in the population.
T101 15657-15891 Sentence denotes In Eq. 2, the instrumental variables include averages of daily maximum temperature, total precipitation, average wind speed, and the interaction between precipitation and wind speed, for city c in the preceding third and fourth weeks.
T102 15892-15989 Sentence denotes Detailed discussion of the selection of weather characteristics as instruments is in Section 3.2.
T103 15990-16044 Sentence denotes The timeline of key variables are displayed in Fig. 1.
T104 16045-16326 Sentence denotes The primary assumption on the instrumental variables is that weather conditions before 2 weeks do not affect the likelihood that a person susceptible to the virus contracts the disease, conditional on weather conditions and the number of infectious people within the 2-week window.
T105 16327-16503 Sentence denotes On the other hand, they affect the number of other persons who have become infectious within the 2-week window, because they may have contracted the virus earlier than 2 weeks.
T106 16504-16662 Sentence denotes These weather variables are exogenous to the error term and affect the spread of the virus, which have been used by Adda (2016) to instrument flu infections9.
T107 16663-16695 Sentence denotes Fig. 1 Timeline of key variables
T108 16696-16923 Sentence denotes Another objective of this paper is to quantify the effect of various socioeconomic factors in mediating the transmission rates of the virus, which may identify potential behavioral and socioeconomic risk factors for infections.
T109 16924-17209 Sentence denotes For within-city transmissions, we consider the effects of local public health measures (see Section 5 for details) and the mediating effects of population density, level of economic development, number of doctors, and environmental factors such as temperature, wind, and precipitation.
T110 17210-17390 Sentence denotes For between-city transmissions, apart from proximity measures based on geographic distance, we also consider similarity in population density and the level of economic development.
T111 17391-17496 Sentence denotes To measure the spread of the virus from Wuhan, we also include the number of people traveling from Wuhan.
T112 17497-17536 Sentence denotes The full empirical model is as follows:
T113 17537-17898 Sentence denotes 3 yct=∑τ=12∑k=1Kwithinαwithin,τkh¯ctkτy¯ctτ+∑τ=12∑k=1Kbetween∑r≠cαbetween,τkm¯crtkτy¯rtτ+∑τ=12∑k=1KWuhanρτkm¯c,Wuhan,tkτz¯tτ+xctβ+𝜖ct,where h¯ctkτ includes dummies for local public health measures and the mediating factors for local transmissions. m¯crtkτ and m¯c,Wuhan,tkτ are the mediating factors for between-city transmissions and imported cases from Wuhan.
T114 17900-17904 Sentence denotes Data
T115 17906-17915 Sentence denotes Variables
T116 17916-18105 Sentence denotes January 19, 2020, is the first day that COVID-19 cases were reported outside of Wuhan, so we collect the daily number of new cases of COVID-19 for 305 cities from January 19 to February 29.
T117 18106-18187 Sentence denotes All these data are reported by 32 provincial-level Health Commissions in China10.
T118 18188-18338 Sentence denotes Figure 2 shows the time patterns of daily confirmed new cases in Wuhan, in Hubei province outside Wuhan, and in non-Hubei provinces of mainland China.
T119 18339-18559 Sentence denotes Because Hubei province started to include clinically diagnosed cases into new confirmed cases on February 12, we notice a spike in the number of new cases in Wuhan and other cities in Hubei province on this day (Fig. 2).
T120 18560-18669 Sentence denotes The common effects of such changes in case definitions on other cities can be absorbed by time fixed effects.
T121 18670-18759 Sentence denotes As robustness checks, we re-estimate models A and B without the cities in Hubei province.
T122 18760-19041 Sentence denotes In addition, since the number of clinically diagnosed cases at the city level was reported for the days of February 12, 13, and 14, we recalculated the daily number of new cases for the 3 days by removing the clinically diagnosed cases from our data and re-estimate models A and B.
T123 19042-19084 Sentence denotes Our main findings still hold (Appendix B).
T124 19085-19157 Sentence denotes Fig. 2 Number of daily new confirmed cases of COVID-19 in mainland China
T125 19158-19310 Sentence denotes Regarding the explanatory variables, we calculate the number of new cases of COVID-19 in the preceding first and second weeks for each city on each day.
T126 19311-19702 Sentence denotes To estimate the impacts of new COVID-19 cases in other cities, we first calculate the geographic distance between a city and all other cities using the latitudes and longitudes of the centroids of each city and then calculate the weighted sum of the number of COVID-19 new cases in all other cities using the inverse of log distance between a city and each of the other cities as the weight.
T127 19703-19868 Sentence denotes Since the COVID-19 outbreak started from Wuhan, we also calculate the weighted number of COVID-19 new cases in Wuhan using the inverse of log distance as the weight.
T128 19869-20078 Sentence denotes Furthermore, to explore the mediating impact of population flow from Wuhan, we collect the daily population flow index from Baidu that proxies for the total intensity of migration from Wuhan to other cities11.
T129 20079-20195 Sentence denotes Figure 3 plots the Baidu index of population flow out of Wuhan and compares its values this year with those in 2019.
T130 20196-20361 Sentence denotes We then interact the flow index with the share that a destination city takes (Fig. 4) to construct a measure on the population flow from Wuhan to a destination city.
T131 20362-20547 Sentence denotes Other mediating variables include population density, GDP per capita, and the number of doctors at the city level, which we collect from the most recent China city statistical yearbook.
T132 20548-20607 Sentence denotes Table 1 presents the summary statistics of these variables.
T133 20608-20722 Sentence denotes On average, GDP per capita and population density are larger in cities outside Hubei province than those in Hubei.
T134 20723-20802 Sentence denotes Compared with cities in Hubei province, cities outside Hubei have more doctors.
T135 20803-20851 Sentence denotes Fig. 3 Baidu index of population flow from Wuhan
T136 20852-20907 Sentence denotes Fig. 4 Destination shares in population flow from Wuhan
T137 20908-20934 Sentence denotes Table 1 Summary statistics
T138 20935-20959 Sentence denotes Variable N Mean Std dev.
T139 20960-20964 Sentence denotes Min.
T140 20965-20976 Sentence denotes Median Max.
T141 20977-20993 Sentence denotes Non Hubei cities
T142 20994-21014 Sentence denotes City characteristics
T143 21015-21075 Sentence denotes GDP per capita, 10,000RMB 288 5.225 3.025 1.141 4.327 21.549
T144 21076-21146 Sentence denotes Population density, per km2 288 428.881 374.138 9.049 327.115 3444.092
T145 21147-21202 Sentence denotes # of doctors, 10,000 288 1.086 1.138 0.030 0.805 10.938
T146 21203-21239 Sentence denotes Time varying variables, Jan 19–Feb 1
T147 21240-21306 Sentence denotes Daily # of new confirmed cases 4032 1.303 3.608 0.000 0.000 60.000
T148 21307-21381 Sentence denotes Weekly average max. temperature, ∘C 4032 8.520 8.525 − 18.468 7.932 29.833
T149 21382-21449 Sentence denotes Weekly average precipitation, mm 4032 0.238 0.558 0.000 0.033 5.570
T150 21450-21515 Sentence denotes Weekly average wind speed, m/s 4032 2.209 0.842 0.816 2.014 6.386
T151 21516-21552 Sentence denotes Time varying variables, Feb 1–Feb 29
T152 21553-21620 Sentence denotes Daily # of new confirmed cases 8064 0.927 3.461 0.000 0.000 201.000
T153 21621-21697 Sentence denotes Weekly average max. temperature, ∘C 8064 11.909 7.983 − 18.032 12.814 28.791
T154 21698-21765 Sentence denotes Weekly average precipitation, mm 8064 0.193 0.491 0.000 0.027 5.432
T155 21766-21831 Sentence denotes Weekly average wind speed, m/s 8064 2.461 0.913 0.654 2.352 7.129
T156 21832-21873 Sentence denotes Cities in Hubei province, excluding Wuhan
T157 21874-21894 Sentence denotes City characteristics
T158 21895-21953 Sentence denotes GDP per capita, 10,000RMB 16 4.932 1.990 2.389 4.306 8.998
T159 21954-22023 Sentence denotes Population density, per km2 16 416.501 220.834 24.409 438.820 846.263
T160 22024-22077 Sentence denotes # of doctors, 10,000 16 0.698 0.436 0.017 0.702 1.393
T161 22078-22114 Sentence denotes Time varying variables, Jan 19–Feb 1
T162 22115-22183 Sentence denotes Daily # of new confirmed cases 224 22.165 35.555 0.000 7.000 276.000
T163 22184-22254 Sentence denotes Weekly average max. temperature, ∘C 224 8.709 1.602 1.278 8.905 10.889
T164 22255-22321 Sentence denotes Weekly average precipitation, mm 224 0.261 0.313 0.000 0.160 1.633
T165 22322-22386 Sentence denotes Weekly average wind speed, m/s 224 1.970 0.600 0.893 1.975 3.439
T166 22387-22423 Sentence denotes Time varying variables, Feb 1–Feb 29
T167 22424-22492 Sentence denotes Daily # of new confirmed cases 448 28.871 51.793 0.000 8.000 424.000
T168 22493-22565 Sentence denotes Weekly average max. temperature, ∘C 448 14.569 2.985 1.452 14.448 23.413
T169 22566-22632 Sentence denotes Weekly average precipitation, mm 448 0.201 0.233 0.000 0.133 1.535
T170 22633-22697 Sentence denotes Weekly average wind speed, m/s 448 2.063 0.648 0.705 2.070 4.174
T171 22698-22781 Sentence denotes Variables of the city characteristics are obtained from City Statistical Yearbooks.
T172 22782-22838 Sentence denotes Time varying variables are observed daily for each city.
T173 22839-22908 Sentence denotes Weekly average weather variables are averages over the preceding week
T174 22909-23005 Sentence denotes We rely on meteorological data to construct instrumental variables for the endogenous variables.
T175 23006-23264 Sentence denotes The National Oceanic and Atmospheric Administration (NOAA) provides average, maximum, and minimum temperatures, air pressure, average and maximum wind speeds, precipitation, snowfall amount, and dew point for 362 weather stations at the daily level in China.
T176 23265-23533 Sentence denotes To merge the meteorological variables with the number of new cases of COVID-19, we first calculate daily weather variables for each city on each day from 2019 December to 2020 February from station-level weather records following the inverse distance weighting method.
T177 23534-23716 Sentence denotes Specifically, for each city, we draw a circle of 100 km from the city’s centroid and calculate the weighted average daily weather variables using stations within the 100-km circle12.
T178 23717-23811 Sentence denotes We use the inverse of the distance between the city’s centroid and each station as the weight.
T179 23812-23924 Sentence denotes Second, we match the daily weather variables to the number of new cases of COVID-19 based on city name and date.
T180 23926-23961 Sentence denotes Selection of instrumental variables
T181 23962-24042 Sentence denotes The transmission rate of COVID-19 may be affected by many environmental factors.
T182 24043-24168 Sentence denotes Human-to-human transmission of COVID-19 is mostly through droplets and contacts (National Health Commission of the PRC 2020).
T183 24169-24321 Sentence denotes Weather conditions such as rainfall, wind speed, and temperature may shape infections via their influences on social activities and virus transmissions.
T184 24322-24448 Sentence denotes For instance, increased precipitation results in higher humidity, which may weaken virus transmissions (Lowen and Steel 2014).
T185 24449-24513 Sentence denotes The virus may survive longer with lower temperature (Wang et al.
T186 24514-24534 Sentence denotes 2020b; Puhani 2020).
T187 24535-24616 Sentence denotes Greater wind speed and therefore ventilated air may decrease virus transmissions.
T188 24617-24705 Sentence denotes In addition, increased rainfall and lower temperature may also reduce social activities.
T189 24706-24847 Sentence denotes Newly confirmed COVID-19 cases typically arise from contracting the virus within 2 weeks in the past (e.g., World Health Organization 2020b).
T190 24848-25023 Sentence denotes The extent of human-to-human transmission is determined by the number of people who have already contracted the virus and the environmental conditions within the next 2 weeks.
T191 25024-25314 Sentence denotes Conditional on the number of people who are infectious and environmental conditions in the previous first and second weeks, it is plausible that weather conditions further in the past, i.e., in the previous third and fourth weeks, should not directly affect the number of current new cases.
T192 25315-25539 Sentence denotes Based on the existing literature, we select weather characteristics as the instrumental variables, which include daily maximum temperature, precipitation, wind speed, and the interaction between precipitation and wind speed.
T193 25540-25689 Sentence denotes We then regress the endogenous variables on the instrumental variables, contemporaneous weather controls, city, date, and city by week fixed effects.
T194 25690-25877 Sentence denotes Table 2 shows that F-tests on the coefficients of the instrumental variables all reject joint insignificance, which confirms that overall the selected instrumental variables are not weak.
T195 25878-25966 Sentence denotes The coefficients of the first stage regressions are reported in Table 9 in the appendix.
T196 25967-25994 Sentence denotes Table 2 First stage results
T197 25995-26034 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T198 26035-26043 Sentence denotes Own city
T199 26044-26099 Sentence denotes Average # new cases, 1-week lag F stat 11.41 4.02 17.28
T200 26100-26128 Sentence denotes p value 0.0000 0.0000 0.0000
T201 26129-26183 Sentence denotes Average # new cases, 2-week lag F stat 8.46 5.66 10.25
T202 26184-26212 Sentence denotes p value 0.0000 0.0000 0.0000
T203 26213-26274 Sentence denotes Average # new cases, previous 14 days F stat 18.37 7.72 21.69
T204 26275-26303 Sentence denotes p value 0.0000 0.0000 0.0000
T205 26304-26343 Sentence denotes Other cities, inverse distance weighted
T206 26344-26400 Sentence denotes Average # new cases, 1-week lag F stat 19.10 36.29 17.58
T207 26401-26429 Sentence denotes p value 0.0000 0.0000 0.0000
T208 26430-26486 Sentence denotes Average # new cases, 2-week lag F stat 36.32 19.94 37.31
T209 26487-26515 Sentence denotes p value 0.0000 0.0000 0.0000
T210 26516-26578 Sentence denotes Average # new cases, previous 14 days F stat 47.08 33.45 46.22
T211 26579-26607 Sentence denotes p value 0.0000 0.0000 0.0000
T212 26608-26768 Sentence denotes This table reports the F-tests on the joint significance of the coefficients on the instrumental variables (IV) that are excluded from the estimation equations.
T213 26769-27052 Sentence denotes Our IV include weekly averages of daily maximum temperature, precipitation, wind speed, and the interaction between precipitation and wind speed, during the preceding third and fourth weeks, and the averages of these variables in other cities weighted by the inverse of log distance.
T214 27053-27226 Sentence denotes For each F statistic, the variable in the corresponding row is the dependent variable, and the time window in the corresponding column indicates the time span of the sample.
T215 27227-27508 Sentence denotes Each regression also includes 1- and 2-week lags of these weather variables, weekly averages of new infections in the preceding first and second weeks in Wuhan which are interacted with the inverse log distance or the population flow, and city, date and city by week fixed effects.
T216 27509-27612 Sentence denotes Coefficients on the instrumental variables for the full sample are reported in Table 15 in the appendix
T217 27613-27726 Sentence denotes We also need additional weather variables to instrument the adoption of public health measures at the city level.
T218 27727-27950 Sentence denotes Since there is no theoretical guidance from the existing literature, we implement the Cluster-Lasso method of Belloni et al. (2016) and Ahrens et al. (2019) to select weather characteristics that have good predictive power.
T219 27951-27987 Sentence denotes Details are displayed in Appendix A.
T220 27989-27996 Sentence denotes Results
T221 27997-28088 Sentence denotes Our sample starts from January 19, when the first COVID-19 case was reported outside Wuhan.
T222 28089-28147 Sentence denotes The sample spans 6 weeks in total and ends on February 29.
T223 28148-28334 Sentence denotes We divide the whole sample into two sub-samples (January 19 to February 1, and February 2 to February 29) and estimate the model using the whole sample and two sub-samples, respectively.
T224 28335-28539 Sentence denotes In the first 2 weeks, COVID-19 infections quickly spread throughout China with every province reporting at least one confirmed case, and the number of cases also increased at an increasing speed (Fig. 2).
T225 28540-28655 Sentence denotes It is also during these 2 weeks that the Chinese government took actions swiftly to curtail the virus transmission.
T226 28656-28791 Sentence denotes On January 20, COVID-19 was classified as a class B statutory infectious disease and treated as a class A statutory infectious disease.
T227 28792-28919 Sentence denotes The city of Wuhan was placed under lockdown on January 23; roads were closed, and residents were not allowed to leave the city.
T228 28920-29088 Sentence denotes Many other cities also imposed public policies ranging from canceling public events and stopping public transportation to limiting how often residents could leave home.
T229 29089-29227 Sentence denotes By comparing the dynamics of virus transmissions in these two sub-samples, we can infer the effectiveness of these public health measures.
T230 29228-29504 Sentence denotes In this section, we will mostly rely on model A to interpret the results, which estimates the effects of the average number of new cases in the preceding first and second week, respectively, and therefore enables us to examine the transmission dynamics at different time lags.
T231 29505-29607 Sentence denotes As a robustness check, we also consider a simpler lag structure to describe the transmission dynamics.
T232 29608-29743 Sentence denotes In model B, we estimate the effects of the average number of new cases in the past 14 days instead of using two separate lag variables.
T233 29745-29769 Sentence denotes Within-city transmission
T234 29770-29902 Sentence denotes Table 3 reports the estimation results of the OLS and IV regressions of Eq. 2, in which only within-city transmission is considered.
T235 29903-30116 Sentence denotes After controlling for time-invariant city fixed effects and time effects that are common to all cities, on average, one new infection leads to 1.142 more cases in the next week, but 0.824 fewer cases 1 week later.
T236 30117-30331 Sentence denotes The negative effect can be attributed to the fact that both local authorities and residents would have taken more protective measures in response to a higher perceived risk of contracting the virus given more time.
T237 30332-30573 Sentence denotes Information disclosure on newly confirmed cases at the daily level by official media and information dissemination on social media throughout China may have promoted more timely actions by the public, resulting in slower virus transmissions.
T238 30574-30639 Sentence denotes We then compare the transmission rates in different time windows.
T239 30640-30770 Sentence denotes In the first sub-sample, one new infection leads to 2.135 more cases within a week, implying a fast growth in the number of cases.
T240 30771-30957 Sentence denotes However, in the second sub-sample, the effect decreases to 1.077, suggesting that public health measures imposed in late January were effective in limiting a further spread of the virus.
T241 30958-31004 Sentence denotes Similar patterns are also observed in model B.
T242 31005-31049 Sentence denotes Table 3 Within-city transmission of COVID-19
T243 31050-31089 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T244 31090-31113 Sentence denotes (1) (2) (3) (4) (5) (6)
T245 31114-31134 Sentence denotes OLS IV OLS IV OLS IV
T246 31135-31161 Sentence denotes All cities excluding Wuhan
T247 31162-31252 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T248 31253-31329 Sentence denotes Average # of new cases 0.873*** 1.142*** 1.692*** 2.135*** 0.768*** 1.077***
T249 31330-31395 Sentence denotes 1-week lag (0.00949) (0.0345) (0.0312) (0.0549) (0.0120) (0.0203)
T250 31396-31479 Sentence denotes Average # of new cases − 0.415*** − 0.824*** 0.860 − 6.050*** − 0.408*** − 0.796***
T251 31480-31544 Sentence denotes 2-week lag (0.00993) (0.0432) (2.131) (2.314) (0.00695) (0.0546)
T252 31545-31610 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T253 31611-31686 Sentence denotes Average # of new case 0.474*** 0.720*** 3.310*** 3.860*** 0.494*** 1.284***
T254 31687-31754 Sentence denotes Previous 14 days (0.0327) (0.143) (0.223) (0.114) (0.00859) (0.107)
T255 31755-31801 Sentence denotes Observations 12,768 12,768 4256 4256 8512 8512
T256 31802-31842 Sentence denotes Number of cities 304 304 304 304 304 304
T257 31843-31883 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T258 31884-31915 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T259 31916-31947 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T260 31948-31993 Sentence denotes All cities excluding cities in Hubei Province
T261 31994-32084 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T262 32085-32161 Sentence denotes Average # of new cases 0.725*** 1.113*** 1.050*** 1.483*** 0.620*** 0.903***
T263 32162-32223 Sentence denotes 1-week lag (0.141) (0.0802) (0.0828) (0.205) (0.166) (0.0349)
T264 32224-32304 Sentence denotes Average # of new cases − 0.394*** − 0.572*** 0.108 − 3.664 − 0.228*** − 0.341***
T265 32305-32365 Sentence denotes 2-week lag (0.0628) (0.107) (0.675) (2.481) (0.0456) (0.121)
T266 32366-32431 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T267 32432-32508 Sentence denotes Average # of new cases 0.357*** 0.631*** 1.899*** 2.376*** 0.493*** 0.745***
T268 32509-32574 Sentence denotes Previous 14 days (0.0479) (0.208) (0.250) (0.346) (0.122) (0.147)
T269 32575-32621 Sentence denotes Observations 12,096 12,096 4032 4032 8064 8064
T270 32622-32662 Sentence denotes Number of cities 288 288 288 288 288 288
T271 32663-32703 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T272 32704-32735 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T273 32736-32767 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T274 32768-32824 Sentence denotes The dependent variable is the number of daily new cases.
T275 32825-33037 Sentence denotes The endogenous explanatory variables include the average numbers of new confirmed cases in the own city in the preceding first and second weeks (model A) and the average number in the preceding 14 days (model B).
T276 33038-33353 Sentence denotes Weekly averages of daily maximum temperature, precipitation, wind speed, the interaction between precipitation and wind speed, and the inverse log distance weighted sum of each of these variables in other cities, during the preceding third and fourth weeks, are used as instrumental variables in the IV regressions.
T277 33354-33453 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T278 33454-33549 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T279 33550-33703 Sentence denotes Many cases were also reported in other cities in Hubei province apart from Wuhan, where six of them reported over 1000 cumulative cases by February 1513.
T280 33704-33825 Sentence denotes Their overstretched health care system exacerbates the concern over delayed reporting of confirmed cases in these cities.
T281 33826-33962 Sentence denotes To mitigate the effect of such potential measurement errors on our estimates, we re-estimate (2) excluding all cities in Hubei province.
T282 33963-34015 Sentence denotes The bottom panel of Table 3 reports these estimates.
T283 34016-34172 Sentence denotes Comparing the IV estimates in columns (4) and (6) between the upper and lower panels, we find that the transmission rates are lower in cities outside Hubei.
T284 34173-34346 Sentence denotes In the January 19–February 1 sub-sample, one new case leads to 1.483 more cases in the following week, and this is reduced to 0.903 in the February 2–February 29 sub-sample.
T285 34347-34420 Sentence denotes We also find a similar pattern when comparing the estimates from model B.
T286 34422-34447 Sentence denotes Between-city transmission
T287 34448-34562 Sentence denotes People may contract the virus from interaction with the infected people who live in the same city or other cities.
T288 34563-34743 Sentence denotes In Eq. 1, we consider the effects of the number of new infections in other cities and in the epicenter of the epidemic (Wuhan), respectively, using inverse log distance as weights.
T289 34744-34989 Sentence denotes In addition, geographic proximity may not fully describe the level of social interactions between residents in Wuhan and other cities since the lockdown in Wuhan on January 23 significantly reduced the population flow from Wuhan to other cities.
T290 34990-35297 Sentence denotes To alleviate this concern, we also use a measure of the size of population flow from Wuhan to a destination city, which is constructed by multiplying the daily migration index on the population flow out of Wuhan (Fig 3) with the share of the flow that a destination city receives provided by Baidu (Fig. 4).
T291 35298-35398 Sentence denotes For days before January 25, we use the average destination shares between January 10 and January 24.
T292 35399-35507 Sentence denotes For days on or after January 24, we use the average destination shares between January 25 and February 2314.
T293 35508-35651 Sentence denotes Table 4 reports the estimates from IV regressions of Eq. 1, and Table 5 reports the results from the same regressions excluding Hubei province.
T294 35652-35838 Sentence denotes Column (4) of Table 4 indicates that in the first sub-sample, one new case leads to 2.456 more cases within 1 week, and the effect is not statistically significant between 1 and 2 weeks.
T295 35839-36014 Sentence denotes Column (6) suggests that in the second sub-sample, one new case leads to 1.127 more cases within 1 week, and the effect is not statistically significant between 1 and 2 weeks.
T296 36015-36215 Sentence denotes The comparison of the coefficients on own city between different sub-samples indicates that the responses of the government and the public have effectively decreased the risk of additional infections.
T297 36216-36511 Sentence denotes Comparing Table 4 with Table 3, we find that although the number of new cases in the preceding second week turns insignificant and smaller in magnitude, coefficients on the number of new cases in the preceding first week are not sensitive to the inclusion of terms on between-city transmissions.
T298 36512-36569 Sentence denotes Table 4 Within- and between-city rransmission of COVID-19
T299 36570-36609 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T300 36610-36633 Sentence denotes (1) (2) (3) (4) (5) (6)
T301 36634-36654 Sentence denotes OLS IV OLS IV OLS IV
T302 36655-36745 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T303 36746-36780 Sentence denotes Average # of new cases, 1-week lag
T304 36781-36843 Sentence denotes Own city 0.862*** 1.387*** 0.939*** 2.456*** 0.786*** 1.127***
T305 36844-36894 Sentence denotes (0.0123) (0.122) (0.102) (0.638) (0.0196) (0.0686)
T306 36895-36957 Sentence denotes Other cities 0.00266 − 0.0248 0.0889 0.0412 − 0.00316 − 0.0212
T307 36958-37030 Sentence denotes wt. = inv. dist. (0.00172) (0.0208) (0.0714) (0.0787) (0.00227) (0.0137)
T308 37031-37085 Sentence denotes Wuhan − 0.0141 0.0303 − 0.879 − 0.957 − 0.00788 0.0236
T309 37086-37155 Sentence denotes wt. = inv. dist. (0.0115) (0.0318) (0.745) (0.955) (0.00782) (0.0200)
T310 37156-37228 Sentence denotes Wuhan 3.74e-05 0.00151*** 0.00462*** 0.00471*** − 0.00211*** − 0.00238**
T311 37229-37309 Sentence denotes wt. = pop. flow (0.000163) (0.000391) (0.000326) (0.000696) (4.01e-05) (0.00113)
T312 37310-37344 Sentence denotes Average # of new cases, 2-week lag
T313 37345-37408 Sentence denotes Own city − 0.425*** − 0.795*** 2.558 − 1.633 − 0.205*** − 0.171
T314 37409-37459 Sentence denotes (0.0318) (0.0643) (2.350) (2.951) (0.0491) (0.224)
T315 37460-37532 Sentence denotes Other cities − 0.00451** − 0.00766 − 0.361 − 0.0404 − 0.00912** − 0.0230
T316 37533-37604 Sentence denotes wt. = inv. dist. (0.00213) (0.00814) (0.371) (0.496) (0.00426) (0.0194)
T317 37605-37658 Sentence denotes Wuhan − 0.0410* 0.0438 3.053 3.031 − 0.0603 − 0.00725
T318 37659-37727 Sentence denotes wt. = inv. dist. (0.0240) (0.0286) (2.834) (3.559) (0.0384) (0.0137)
T319 37728-37797 Sentence denotes Wuhan 0.00261*** 0.00333*** 0.00711*** − 0.00632 0.00167** 0.00368***
T320 37798-37877 Sentence denotes wt. = pop. flow (0.000290) (0.000165) (0.00213) (0.00741) (0.000626) (0.000576)
T321 37878-37943 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T322 37944-38006 Sentence denotes Own city 0.425*** 1.195*** 1.564*** 2.992*** 0.615*** 1.243***
T323 38007-38056 Sentence denotes (0.0771) (0.160) (0.174) (0.892) (0.0544) (0.115)
T324 38057-38128 Sentence denotes Other cities − 0.00901 − 0.0958** 0.0414 0.0704 − 0.0286*** − 0.0821***
T325 38129-38200 Sentence denotes wt. = inv. dist. (0.00641) (0.0428) (0.0305) (0.0523) (0.0101) (0.0246)
T326 38201-38259 Sentence denotes Wuhan − 0.198* − 0.0687** − 0.309 − 0.608 − 0.234* − 0.144
T327 38260-38326 Sentence denotes wt. = inv. dist. (0.104) (0.0268) (0.251) (0.460) (0.121) (0.0994)
T328 38327-38395 Sentence denotes Wuhan 0.00770*** 0.00487*** 0.00779*** 0.00316 0.00829*** 0.00772***
T329 38396-38476 Sentence denotes wt. = pop. flow (0.000121) (0.000706) (0.000518) (0.00276) (0.000367) (0.000517)
T330 38477-38523 Sentence denotes Observations 12,768 12,768 4256 4256 8512 8512
T331 38524-38564 Sentence denotes Number of cities 304 304 304 304 304 304
T332 38565-38605 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T333 38606-38637 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T334 38638-38669 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T335 38670-38726 Sentence denotes The dependent variable is the number of daily new cases.
T336 38727-38947 Sentence denotes The endogenous explanatory variables include the average numbers of new confirmed cases in the own city and nearby cities in the preceding first and second weeks (model A) and averages in the preceding 14 days (model B).
T337 38948-39255 Sentence denotes Weekly averages of daily maximum temperature, precipitation, wind speed, the interaction between precipitation and wind speed, and the inverse log distance weighted sum of these variables in other cities, during the preceding third and fourth weeks, are used as instrumental variables in the IV regressions.
T338 39256-39355 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T339 39356-39451 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T340 39452-39545 Sentence denotes Table 5 Within- and between-city transmission of COVID-19, excluding cities in Hubei Province
T341 39546-39585 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T342 39586-39609 Sentence denotes (1) (2) (3) (4) (5) (6)
T343 39610-39630 Sentence denotes OLS IV OLS IV OLS IV
T344 39631-39721 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T345 39722-39756 Sentence denotes Average # of new cases, 1-week lag
T346 39757-39819 Sentence denotes Own city 0.656*** 1.117*** 0.792*** 1.194*** 0.567*** 0.899***
T347 39820-39869 Sentence denotes (0.153) (0.112) (0.0862) (0.302) (0.172) (0.0924)
T348 39870-39939 Sentence denotes Other cities 0.00114 − 0.00213 − 0.0160 − 0.0734 0.000221 − 0.00526**
T349 39940-40016 Sentence denotes wt. = inv. dist. (0.000741) (0.00367) (0.0212) (0.0803) (0.000626) (0.00244)
T350 40017-40072 Sentence denotes Wuhan − 0.000482 0.00420 0.104 0.233 5.89e-05 0.00769**
T351 40073-40145 Sentence denotes wt. = inv. dist. (0.00173) (0.00649) (0.128) (0.156) (0.00194) (0.00379)
T352 40146-40211 Sentence denotes Wuhan 0.00668*** 0.00616*** 0.00641*** 0.00375 − 0.000251 0.00390
T353 40212-40287 Sentence denotes wt. = pop. flow (0.00159) (0.00194) (0.00202) (0.00256) (0.00245) (0.00393)
T354 40288-40322 Sentence denotes Average # of new cases, 2-week lag
T355 40323-40387 Sentence denotes Own city − 0.350*** − 0.580*** 0.230 − 1.541 − 0.157** − 0.250**
T356 40388-40437 Sentence denotes (0.0667) (0.109) (0.572) (1.448) (0.0636) (0.119)
T357 40438-40502 Sentence denotes Other cities − 0.000869 0.00139 0.172 0.584 − 0.00266* − 0.00399
T358 40503-40575 Sentence denotes wt. = inv. dist. (0.00102) (0.00311) (0.122) (0.595) (0.00154) (0.00276)
T359 40576-40635 Sentence denotes Wuhan − 0.00461 0.000894 − 0.447 − 0.970 − 0.00456 0.00478*
T360 40636-40708 Sentence denotes wt. = inv. dist. (0.00304) (0.00592) (0.829) (0.808) (0.00368) (0.00280)
T361 40709-40774 Sentence denotes Wuhan 0.00803*** 0.00203 0.00973*** 0.00734 0.00759*** 0.00466***
T362 40775-40850 Sentence denotes wt. = pop. flow (0.00201) (0.00192) (0.00317) (0.00680) (0.00177) (0.00140)
T363 40851-40916 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T364 40917-40979 Sentence denotes Own city 0.242*** 0.654*** 1.407*** 1.876*** 0.406*** 0.614***
T365 40980-41028 Sentence denotes (0.0535) (0.195) (0.215) (0.376) (0.118) (0.129)
T366 41029-41095 Sentence denotes Other cities 0.000309 − 0.00315 0.00608 0.0194 − 0.00224 − 0.00568
T367 41096-41170 Sentence denotes wt. = inv. dist. (0.00142) (0.00745) (0.0188) (0.0300) (0.00204) (0.00529)
T368 41171-41235 Sentence denotes Wuhan − 0.0133** − 0.0167 − 0.0146 − 0.0362 − 0.0138** − 0.00847
T369 41236-41309 Sentence denotes wt. = inv. dist. (0.00535) (0.0140) (0.0902) (0.0741) (0.00563) (0.00787)
T370 41310-41374 Sentence denotes Wuhan 0.0153*** 0.0133*** 0.00826*** 0.00404 0.0132*** 0.0123***
T371 41375-41450 Sentence denotes wt. = pop. flow (0.00273) (0.00273) (0.00241) (0.00423) (0.00222) (0.00205)
T372 41451-41497 Sentence denotes Observations 12,096 12,096 4032 4032 8064 8064
T373 41498-41538 Sentence denotes Number of cities 288 288 288 288 288 288
T374 41539-41579 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T375 41580-41611 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T376 41612-41643 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T377 41644-41700 Sentence denotes The dependent variable is the number of daily new cases.
T378 41701-41921 Sentence denotes The endogenous explanatory variables include the average numbers of new confirmed cases in the own city and nearby cities in the preceding first and second weeks (model A) and averages in the preceding 14 days (model B).
T379 41922-42229 Sentence denotes Weekly averages of daily maximum temperature, precipitation, wind speed, the interaction between precipitation and wind speed, and the inverse log distance weighted sum of these variables in other cities, during the preceding third and fourth weeks, are used as instrumental variables in the IV regressions.
T380 42230-42329 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T381 42330-42425 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T382 42426-42526 Sentence denotes As a robustness test, Table 5 reports the estimation results excluding the cities in Hubei province.
T383 42527-42733 Sentence denotes Column (4) of Table 5 indicates that in the first sub-sample, one new case leads to 1.194 more cases within a week, while in the second sub-sample, one new case only leads to 0.899 more cases within a week.
T384 42734-43002 Sentence denotes Besides, in the second subsample, one new case results in 0.250 fewer new infections between 1 and 2 weeks, which is larger in magnitude and more significant than the estimate (− 0.171) when cities in Hubei province are included for estimation (column (6) of Table 4).
T385 43003-43107 Sentence denotes The time varying patterns in local transmissions are evident using the rolling window analysis (Fig. 5).
T386 43108-43274 Sentence denotes The upper left panel displays the estimated coefficients on local transmissions for various 14-day sub-samples with the starting date labelled on the horizontal axis.
T387 43275-43421 Sentence denotes After a slight increase in the local transmission rates, one case generally leads to fewer and fewer additional cases a few days after January 19.
T388 43422-43647 Sentence denotes Besides, the transmission rate displays a slight increase beginning around February 4, which corresponds to the return travels and work resumption after Chinese Spring Festival, but eventually decreases at around February 12.
T389 43648-43802 Sentence denotes Such decrease may be partly attributed to the social distancing strategies at the city level, so we examine the impacts of relevant policies in Section 5.
T390 43803-43966 Sentence denotes Moreover, the transmission rates in cities outside Hubei province have been kept at low levels throughout the whole sample period (columns (4) and (6) of Table 5).
T391 43967-44147 Sentence denotes These results suggest that the policies adopted at the national and provincial levels soon after January 19 prevented cities outside Hubei from becoming new hotspots of infections.
T392 44148-44276 Sentence denotes Overall, the spread of the virus has been effectively contained by mid February, particularly for cities outside Hubei province.
T393 44277-44361 Sentence denotes Fig. 5 Rolling window analysis of within- and between-city transmission of COVID-19.
T394 44362-44462 Sentence denotes This figure shows the estimated coefficients and 95% CIs from the instrumental variable regressions.
T395 44463-44532 Sentence denotes The specification is the same as the IV regression models in Table 4.
T396 44533-44628 Sentence denotes Each estimation sample contains 14 days with the starting date indicated on the horizontal axis
T397 44629-44782 Sentence denotes In the epidemiology literature, the estimates on the basic reproduction number of COVID-19 are approximately within the wide range of 1.4∼6.5 (Liu et al.
T398 44783-44789 Sentence denotes 2020).
T399 44790-45104 Sentence denotes Its value depends on the estimation method used, underlying assumptions of modeling, time period covered, geographic regions (with varying preparedness of health care systems), and factors considered in the models that affect disease transmissions (such as the behavior of the susceptible and infected population).
T400 45105-45226 Sentence denotes Intuitively, it can be interpreted as measuring the expected number of new cases that are generated by one existing case.
T401 45227-45294 Sentence denotes It is of interest to note that our estimates are within this range.
T402 45295-45469 Sentence denotes Based on the results from model B in Tables 4 and 5, one case leads to 2.992 more cases in the same city in the next 14 days (1.876 if cities in Hubei province are excluded).
T403 45470-45728 Sentence denotes In the second sub-sample (February 2–February 29), these numbers are reduced to 1.243 and 0.614, respectively, suggesting that factors such as public health measures and people’s behavior may play an important role in containing the transmission of COVID-19.
T404 45729-45954 Sentence denotes While our basic reproduction number estimate (R0) is within the range of estimates in the literature and is close to its median, five features may distinguish our estimates from some of the existing epidemiological estimates.
T405 45955-46891 Sentence denotes First, our instrumental variable approach helps isolate the causal effect of virus transmissions from other confounded factors; second, our estimate is based on an extended time period of the COVID-19 pandemic (until the end of February 2020) that may mitigate potential biases in the literature that relies on a shorter sampling period within 1–28 January 2020; third, our modeling makes minimum assumptions of virus transmissions, such as imposing fewer restrictions on the relationship between the unobserved determinants of new cases and the number of cases in the past; fourth, our model simultaneously considers comprehensive factors that may affect virus transmissions, including multiple policy instruments (such as closed management of communities and shelter-at-home order), population flow, within- and between-city transmissions, economic and demographic conditions, weather patterns, and preparedness of health care system.
T406 46892-47080 Sentence denotes Fifth, our study uses spatially disaggregated data that cover China (except its Hubei province), while some other studies examine Wuhan city, Hubei province, China as a whole, or overseas.
T407 47081-47242 Sentence denotes Regarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4).
T408 47243-47383 Sentence denotes In the first sub-sample, one new case in Wuhan leads to more cases in other cities receiving more population flows from Wuhan within 1 week.
T409 47384-47690 Sentence denotes Interestingly, in the second sub-sample, population flow from Wuhan significantly decreases the transmission rate within 1 week, suggesting that people have been taking more cautious measures from high COVID-19 risk areas; however, more arrivals from Wuhan in the preceding second week can still be a risk.
T410 47691-47936 Sentence denotes A back of the envelope calculation indicates that one new case in Wuhan leads to 0.064 (0.050) more cases in the destination city per 10,000 travelers from Wuhan within 1 (2) week between January 19 and February 1 (February 2 and February 29)15.
T411 47937-48030 Sentence denotes Note that while the effect is statistically significant, it should be interpreted in context.
T412 48031-48133 Sentence denotes It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16.
T413 48134-48229 Sentence denotes If all had gone to one city, this would have directly generated about 171 cases within 2 weeks.
T414 48230-48508 Sentence denotes The risk of infection is likely very low for most travelers except for few who have previous contacts with sources of infection, and person-specific history of past contacts may be an essential predictor for infection risk, in addition to the total number of population flows17.
T415 48509-48601 Sentence denotes A city may also be affected by infections in nearby cities apart from spillovers from Wuhan.
T416 48602-48829 Sentence denotes We find that the coefficients that represent the infectious effects from nearby cities are generally small and not statistically significant (Table 4), implying that few cities outside Wuhan are themselves exporting infections.
T417 48830-49037 Sentence denotes This is consistent with the findings in the World Health Organization (2020b) that other than cases that are imported from Hubei, additional human-to-human transmissions are limited for cities outside Hubei.
T418 49038-49203 Sentence denotes Restricting to cities outside Hubei province, the results are similar (Table 5), except that the transmission from Wuhan is not significant in the first half sample.
T419 49205-49242 Sentence denotes Social and economic mediating factors
T420 49243-49371 Sentence denotes We also investigate the mediating impacts of some socioeconomic and environmental characteristics on the transmission rates (3).
T421 49372-49531 Sentence denotes To ease the comparison between different moderators, we consider the mediating impacts on the influence of the average number of new cases in the past 2 weeks.
T422 49532-49760 Sentence denotes Regarding own-city transmissions, we examine the mediating effects of population density, GDP per capita, number of doctors, and average temperature, wind speed, precipitation, and a dummy variable of adverse weather conditions.
T423 49761-50011 Sentence denotes Regarding between-city transmissions, we consider the mediating effects of distance, difference in population density, and difference in GDP per capita since cities that are similar in density or economic development level may be more closely linked.
T424 50012-50069 Sentence denotes We also include a measure of population flows from Wuhan.
T425 50070-50131 Sentence denotes Table 6 reports the estimation results of the IV regressions.
T426 50132-50444 Sentence denotes To ease the comparison across various moderators, for the mediating variables of within-city transmissions that are significant at 10%, we compute the changes in the variables so that the effect of new confirmed infections in the past 14 days on current new confirmed cases is reduced by 1 (columns (2) and (4)).
T427 50445-50519 Sentence denotes Table 6 Social and economic factors mediating the transmission of COVID-19
T428 50520-50535 Sentence denotes (1) (2) (3) (4)
T429 50536-50561 Sentence denotes Jan 19–Feb 1 Feb 2–Feb 29
T430 50562-50571 Sentence denotes IV Coeff.
T431 50572-50581 Sentence denotes IV Coeff.
T432 50582-50622 Sentence denotes Average # of new cases, previous 14 days
T433 50623-50648 Sentence denotes Own city − 0.251 0.672***
T434 50649-50664 Sentence denotes (0.977) (0.219)
T435 50665-50721 Sentence denotes × population density 0.000164 − 0.000202** + 495 per km2
T436 50722-50743 Sentence denotes (0.000171) (8.91e-05)
T437 50744-50790 Sentence denotes × per capita GDP 0.150*** − 66, 667 RMB 0.0102
T438 50791-50808 Sentence denotes (0.0422) (0.0196)
T439 50809-50849 Sentence denotes × # of doctors − 0.108* + 92, 593 0.0179
T440 50850-50867 Sentence denotes (0.0622) (0.0236)
T441 50868-50909 Sentence denotes × temperature 0.0849* − 11.78∘C − 0.00945
T442 50910-50927 Sentence denotes (0.0438) (0.0126)
T443 50928-50954 Sentence denotes × wind speed − 0.109 0.128
T444 50955-50970 Sentence denotes (0.131) (0.114)
T445 50971-51020 Sentence denotes × precipitation 0.965* − 1.04 mm 0.433* − 2.31 mm
T446 51021-51036 Sentence denotes (0.555) (0.229)
T447 51037-51079 Sentence denotes × adverse weather 0.0846 − 0.614*** + 163%
T448 51080-51095 Sentence denotes (0.801) (0.208)
T449 51096-51125 Sentence denotes Other cities 0.0356 − 0.00429
T450 51126-51164 Sentence denotes wt. = inv. distance (0.0375) (0.00343)
T451 51165-51194 Sentence denotes Other cities 0.00222 0.000192
T452 51195-51240 Sentence denotes wt. = inv. density ratio (0.00147) (0.000891)
T453 51241-51269 Sentence denotes Other cities 0.00232 0.00107
T454 51270-51321 Sentence denotes wt. = inv. per capita GDP ratio (0.00497) (0.00165)
T455 51322-51345 Sentence denotes Wuhan − 0.165 − 0.00377
T456 51346-51383 Sentence denotes wt. = inv. distance (0.150) (0.00981)
T457 51384-51410 Sentence denotes Wuhan − 0.00336 − 0.000849
T458 51411-51455 Sentence denotes wt. = inv. density ratio (0.00435) (0.00111)
T459 51456-51478 Sentence denotes Wuhan − 0.440 − 0.0696
T460 51479-51527 Sentence denotes wt. = inv. per capita GDP ratio (0.318) (0.0699)
T461 51528-51554 Sentence denotes Wuhan 0.00729*** 0.0125***
T462 51555-51596 Sentence denotes wt. = population flow (0.00202) (0.00187)
T463 51597-51619 Sentence denotes Observations 4032 8064
T464 51620-51644 Sentence denotes Number of cities 288 288
T465 51645-51669 Sentence denotes Weather controls Yes Yes
T466 51670-51685 Sentence denotes City FE Yes Yes
T467 51686-51701 Sentence denotes Date FE Yes Yes
T468 51702-51768 Sentence denotes The dependent variable is the number of daily new confirmed cases.
T469 51769-51814 Sentence denotes The sample excludes cities in Hubei province.
T470 51815-52066 Sentence denotes Columns (2) and (4) report the changes in the mediating variables that are needed to reduce the impact of new confirmed cases in the preceding 2 weeks by 1, using estimates with significance levels of at least 0.1 in columns (1) and (3), respectively.
T471 52067-52244 Sentence denotes The endogenous variables include the average numbers of new cases in the own city and nearby cities in the preceding 14 days and their interactions with the mediating variables.
T472 52245-52558 Sentence denotes Weekly averages of daily maximum temperature, precipitation, wind speed, the interaction between precipitation and wind speed, and the inverse log distance weighted sum of these variables in neighboring cities, during the preceding third and fourth weeks, are used as instrumental variables in the IV regressions.
T473 52559-52658 Sentence denotes Additional instrumental variables are constructed by interacting them with the mediating variables.
T474 52659-52740 Sentence denotes Weather controls include these variables in the preceding first and second weeks.
T475 52741-52798 Sentence denotes Standard errors in parentheses are clustered by provinces
T476 52799-52835 Sentence denotes *** p < 0.01, ** p < 0.05, * p < 0.1
T477 52836-53010 Sentence denotes In the early phase of the epidemic (January 19 to February 1), cities with more medical resources, which are measured by the number of doctors, have lower transmission rates.
T478 53011-53106 Sentence denotes One standard deviation increase in the number of doctors reduces the transmission rate by 0.12.
T479 53107-53266 Sentence denotes Cities with higher GDP per capita have higher transmission rates, which can be ascribed to the increased social interactions as economic activities increase18.
T480 53267-53413 Sentence denotes In the second sub-sample, these effects become insignificant probably because public health measures and inter-city resource sharing take effects.
T481 53414-53516 Sentence denotes In fact, cities with higher population density have lower transmission rates in the second sub-sample.
T482 53517-53646 Sentence denotes Regarding the environmental factors, we notice different significant mediating variables across the first and second sub-samples.
T483 53647-53745 Sentence denotes The transmission rates are lower with adverse weather conditions, lower temperature, or less rain.
T484 53746-53802 Sentence denotes Further research is needed to identify clear mechanisms.
T485 53803-53976 Sentence denotes In addition, population flow from Wuhan still poses a risk of new infections for other cities even after we account for the above mediating effects on own-city transmission.
T486 53977-54127 Sentence denotes This effect is robust to the inclusion of the proximity measures based on economic similarity and geographic proximity between Wuhan and other cities.
T487 54128-54231 Sentence denotes Nevertheless, we do not find much evidence on between-city transmissions among cities other than Wuhan.
T488 54233-54282 Sentence denotes Policy response to the COVID-19 outbreak in China
T489 54283-54475 Sentence denotes As the 2002–2004 SARS outbreak has shown, non-pharmaceutical interventions (NPIs) or public health measures may decrease or effectively stop the transmission of COVID-19 even without vaccines.
T490 54476-54656 Sentence denotes Although the effectiveness of a single intervention strategy can be limited, multiple interventions together may generate substantial impacts on containing the spread of the virus.
T491 54657-54791 Sentence denotes Figure 6 depicts the timeline for a series of policies enacted at the national, provincial, and city levels in China since January 19.
T492 54792-54966 Sentence denotes After the official confirmation of human-to-human transmission by the Chinese authorities on January 20, China has adopted a variety of NPIs to contain the COVID-19 outbreak.
T493 54967-55155 Sentence denotes At the national level, COVID-19 was classified as a statutory class B infectious disease on January 20, and prevention and control measures for class A infectious diseases have been taken.
T494 55156-55210 Sentence denotes Government agencies across the country were mobilized.
T495 55211-55391 Sentence denotes The Joint Prevention and Control Mechanism of the State Council was established on January 20, and the Central Leadership Group for Epidemic Response was established on January 25.
T496 55392-55638 Sentence denotes On January 23, National Healthcare Security Administration announced that expenses related to COVID-19 treatments would be covered by the medical insurance and the government if necessary, in order that all COVID-19 cases could be hospitalized19.
T497 55639-55827 Sentence denotes At the provincial level, 30 provinces declared level I responses to major public health emergencies from January 23 to 25, and all provinces had declared level I responses by January 2920.
T498 55828-55905 Sentence denotes Level I responses in China are designed for the highest state of emergencies.
T499 55906-56130 Sentence denotes Measures taken include enhanced isolation and contact tracing of cases, suspension of public transport, cancelling public events, closing schools and entertainment venues, and establishment of health checkpoints (Tian et al.
T500 56131-56137 Sentence denotes 2020).
T501 56138-56241 Sentence denotes These policies together represent population-wide social distancing and case isolation (Ferguson et al.
T502 56242-56248 Sentence denotes 2020).
T503 56249-56335 Sentence denotes Fig. 6 Timeline of China’s public health policies in curtailing the spread of COVID-19
T504 56337-56382 Sentence denotes Policy response to COVID-19 in Hubei Province
T505 56383-56561 Sentence denotes Early detection of COVID-19 importation and prevention of onward transmission are crucial to all areas at risk of importation from areas with active transmissions (Gilbert et al.
T506 56562-56568 Sentence denotes 2020).
T507 56569-56698 Sentence denotes To contain the virus at the epicenter, Wuhan was placed under lockdown with traffic ban for all residents starting on January 23.
T508 56699-56755 Sentence denotes The lockdown is not expected to be lifted until April 8.
T509 56756-56807 Sentence denotes Local buses, subways, and ferries ceased operation.
T510 56808-56917 Sentence denotes Ride-hailing services were prohibited, and only a limited number of taxis were allowed on road by January 24.
T511 56918-56964 Sentence denotes Residents are not permitted to leave the city.
T512 56965-57047 Sentence denotes Departure flights and trains were canceled at the city airport and train stations.
T513 57048-57131 Sentence denotes Checkpoints were set up at highway entrances to prevent cars from leaving the city.
T514 57132-57212 Sentence denotes Since January 22, it became mandatory to wear masks at work or in public places.
T515 57213-57398 Sentence denotes In addition, all cities in Hubei province implemented the lockdown policy, and most Hubei cities had also adopted measures commensurate with class A infectious diseases by January 2821.
T516 57399-57523 Sentence denotes Residents in those areas were strongly encouraged to stay at home and not to attend any activity involving public gathering.
T517 57524-58025 Sentence denotes Health facilities in Wuhan had been extremely overstretched with shortage in medical supplies and high rates of nosocomial infections until February 2 when (1) two new hospitals, i.e., Huoshenshan and Leishenshan, were built to treat patients of COVID-19 with severe symptoms, and (2) 14 makeshift health facilities were converted to isolate patients with mild symptoms and to quarantine people suspected of contracting COVID-19, patients with fever symptoms, and close contacts of confirmed patients.
T518 58026-58151 Sentence denotes This centralized treatment and isolation strategy since February 2 has substantially reduced transmission and incident cases.
T519 58152-58369 Sentence denotes However, stringent public health measures within Hubei province enforced after the massive lockdown may have little to do with virus transmissions out of Hubei province due to the complete travel ban since January 23.
T520 58371-58407 Sentence denotes Reducing inter-city population flows
T521 58408-58575 Sentence denotes Quarantine measures have been implemented in other provinces that aim at restricting population mobility across cities and reducing the risk of importing infections22.
T522 58576-58712 Sentence denotes Seven cities in Zhejiang, Henan, Heilongjiang, and Fujian provinces had adopted the partial shutdown strategy by February 4 (Fang et al.
T523 58713-58721 Sentence denotes 2020)23.
T524 58722-58828 Sentence denotes In Wenzhou, most public transportation was shut down, and traffic leaving the city was banned temporarily.
T525 58829-59010 Sentence denotes On January 21, the Ministry of Transport of China launched level 2 responses to emergencies in order to cooperate with the National Health Commission in preventing the virus spread.
T526 59011-59265 Sentence denotes On January 23, the Ministry of Transport of China, Civil Aviation Administration of China, and China State Railway Group Company, Ltd. (CSRGC) declared to waive the change fees for flight, train, bus, and ferry tickets that were bought before January 24.
T527 59266-59366 Sentence denotes Later, the CSRGC extended the fee waiver policy to train tickets that were bought before February 6.
T528 59367-59502 Sentence denotes By February 2, all railway stations in China had started to monitor body temperature of travelers when they enter and exit the station.
T529 59503-59785 Sentence denotes Across the whole country, Transportation Departments set up 14,000 health checkpoints at bus and ferry terminals, at service centers and toll gates on highways, monitoring the body temperature of passengers and controlling the inflow of population (World Health Organization 2020b).
T530 59786-59910 Sentence denotes Recent visitors to high COVID-19 risk areas are required to self-quarantine for 14 days at home or in designated facilities.
T531 59911-60057 Sentence denotes On February 2, China’s Exit and Entry Administration temporarily suspended the approval and issuance of the travel permits to Hong Kong and Macau.
T532 60058-60178 Sentence denotes On January 23, Wuhan Municipal Administration of Culture and Tourism ordered all tour groups to cancel travels to Wuhan.
T533 60179-60405 Sentence denotes On January 27, the Ministry of Education of China postponed start of the spring semester in 2020, and on February 7, it further announced that students were not allowed to return to school campus without approvals from school.
T534 60407-60457 Sentence denotes Encouraging social distancing in local communities
T535 60458-60599 Sentence denotes Recent studies suggest that there is a large proportion of asymptomatic or mild-symptomatic cases, who can also spread the virus (Dong et al.
T536 60600-60621 Sentence denotes 2020; Mizumoto et al.
T537 60622-60643 Sentence denotes 2020; Nishiura et al.
T538 60644-60661 Sentence denotes 2020; Wang et al.
T539 60662-60669 Sentence denotes 2020a).
T540 60670-60785 Sentence denotes Thus, maintaining social distance is of crucial importance in order to curtail the local transmission of the virus.
T541 60786-60968 Sentence denotes The period from January 24 to 31, 2020, is the traditional Chinese Spring Festival holiday, when families are supposed to get together so that inter-city travel is usually much less.
T542 60969-61116 Sentence denotes People were frequently reminded by official media (via TV news and phone messages) and social media to stay at home and avoid gathering activities.
T543 61117-61249 Sentence denotes On January 26, China State Council extended this holiday to February 2 to delay people’s return travel and curtail the virus spread.
T544 61250-61420 Sentence denotes Nevertheless, economic activities are still supposed to resume after the spring festival, bringing people back to workplaces, which may increase the risk of virus spread.
T545 61421-61712 Sentence denotes To help local residents keep social distance and decrease the risk of virus transmissions, many cities started to implement the “closed management of communities” and “family outdoor restrictions” policies since late January (Table 7), encouraging residents to restrict nonessential travels.
T546 61713-62169 Sentence denotes From January 28 to February 20, more than 250 prefecture-level cities in China implemented “closed management of communities,” which typically includes (1) keeping only one entrance for each community, (2) allowing only community residents to enter and exit the community, (3) checking body temperature for each entrant, (4) testing and quarantining cases that exhibit fever immediately, and (5) tracing and quarantining close contacts of suspicious cases.
T547 62170-62334 Sentence denotes Meanwhile, residents who had symptoms of fever or dry cough were required to report to the community and were quarantined and treated in special medical facilities.
T548 62335-62622 Sentence denotes Furthermore, local governments of 127 cities also imposed more stringent “family outdoor restrictions”—residents are confined or strongly encouraged to stay at home with limited exceptions, e.g., only one person in each family may go out for shopping for necessities once every 2 days24.
T549 62623-62740 Sentence denotes Exit permits were usually distributed to each family in advance and recollected when residents reenter the community.
T550 62741-62801 Sentence denotes Contacts of those patients were also traced and quarantined.
T551 62802-62958 Sentence denotes Table 7 summarizes the number of cities that had imposed “closed management of communities” or “family outdoor restrictions” by different dates in February.
T552 62959-63033 Sentence denotes Table 7 Number of cities with local quarantine measures by different dates
T553 63034-63099 Sentence denotes Date Closed management of communities Family outdoor restrictions
T554 63100-63115 Sentence denotes 2020-02-01 10 1
T555 63116-63131 Sentence denotes 2020-02-02 20 6
T556 63132-63148 Sentence denotes 2020-02-03 33 16
T557 63149-63165 Sentence denotes 2020-02-04 63 38
T558 63166-63183 Sentence denotes 2020-02-05 111 63
T559 63184-63201 Sentence denotes 2020-02-06 155 88
T560 63202-63219 Sentence denotes 2020-02-07 179 92
T561 63220-63237 Sentence denotes 2020-02-08 187 98
T562 63238-63256 Sentence denotes 2020-02-09 196 102
T563 63257-63275 Sentence denotes 2020-02-10 215 104
T564 63276-63294 Sentence denotes 2020-02-11 227 105
T565 63295-63313 Sentence denotes 2020-02-12 234 108
T566 63314-63332 Sentence denotes 2020-02-13 234 109
T567 63333-63351 Sentence denotes 2020-02-14 235 111
T568 63352-63370 Sentence denotes 2020-02-15 237 111
T569 63371-63389 Sentence denotes 2020-02-16 237 122
T570 63390-63408 Sentence denotes 2020-02-17 237 122
T571 63409-63427 Sentence denotes 2020-02-18 238 122
T572 63428-63446 Sentence denotes 2020-02-19 238 122
T573 63447-63466 Sentence denotes 2020-02-20‡ 241 123
T574 63467-63520 Sentence denotes ‡No new cities adopt these measures after February 20
T575 63521-63685 Sentence denotes In order to help inform evidence-based COVID-19 control measures, we examine the effect of these local quarantine measures in reducing the virus transmission rates.
T576 63686-63878 Sentence denotes Dummy variables for the presence of closed management of communities or family outdoor restrictions are created, and they are interacted with the number of infections in the preceding 2 weeks.
T577 63880-63941 Sentence denotes Assessment of the effects of non-pharmaceutical interventions
T578 63942-64008 Sentence denotes Several factors may contribute to the containment of the epidemic.
T579 64009-64204 Sentence denotes The transmission dynamics may change during the course of this epidemic because of improved medical treatments, more effective case isolation and contact tracing, increased public awareness, etc.
T580 64205-64346 Sentence denotes Therefore, we have split the sample into two sub-samples, and the estimated coefficients can be different across the sub-samples (Section 4).
T581 64347-64533 Sentence denotes NPIs such as closed management of communities, city lockdowns, and restrictions on population flow out of areas with high infection risks may also directly affect the transmission rates.
T582 64534-64867 Sentence denotes While many public health measures are implemented nationwide, spatial variations exist in the adoption of two types of measures: closed management of communities (denoted by closed management) and family outdoor restrictions (denoted by stay at home), which allow us to quantify the effect of these NPIs on the transmission dynamics.
T583 64868-65080 Sentence denotes Because most of these local NPIs are adopted in February and our earlier results indicate that the transmission of COVID-19 declines during late January, we restrict the analysis sample to February 2–February 29.
T584 65081-65340 Sentence denotes We also exclude cities in Hubei province, which modified the case definition related to clinically diagnosed cases on February 12 and changed the case definition related to reduced backlogs from increased capacity of molecular diagnostic tests on February 20.
T585 65341-65472 Sentence denotes These modifications coincide with the adoption of local NPIs and can significantly affect the observed dynamics of confirmed cases.
T586 65473-65612 Sentence denotes The adoption of closed management or stay at home is likely affected by the severity of the epidemic and correlated with the unobservables.
T587 65613-65778 Sentence denotes Additional weather controls that have a good predictive power for these NPIs are selected as the instrumental variables based on the method of Belloni et al. (2016).
T588 65779-65815 Sentence denotes Details are displayed in Appendix A.
T589 65816-65889 Sentence denotes The estimation results of OLS and IV regressions are reported in Table 8.
T590 65890-65947 Sentence denotes Table 8 Effects of local non-pharmaceutical interventions
T591 65948-65971 Sentence denotes (1) (2) (3) (4) (5) (6)
T592 65972-65992 Sentence denotes OLS IV OLS IV OLS IV
T593 65993-66027 Sentence denotes Average # of new cases, 1-week lag
T594 66028-66090 Sentence denotes Own city 0.642*** 0.780*** 0.684*** 0.805*** 0.654*** 0.805***
T595 66091-66144 Sentence denotes (0.0644) (0.0432) (0.0496) (0.0324) (0.0566) (0.0439)
T596 66145-66206 Sentence denotes × closed management − 0.593*** − 0.244*** − 0.547*** − 0.193*
T597 66207-66239 Sentence denotes (0.162) (0.0619) (0.135) (0.111)
T598 66240-66293 Sentence denotes × stay at home − 0.597*** − 0.278*** − 0.0688 − 0.110
T599 66294-66326 Sentence denotes (0.186) (0.0800) (0.121) (0.143)
T600 66327-66393 Sentence denotes Other cities 0.00121 − 0.00159 0.00167 − 0.00108 0.00129 − 0.00142
T601 66394-66472 Sentence denotes wt. = inv. dist. (0.000852) (0.00167) (0.00114) (0.00160) (0.000946) (0.00183)
T602 66473-66527 Sentence denotes Wuhan 0.00184 0.00382 0.00325* 0.00443 0.00211 0.00418
T603 66528-66604 Sentence denotes wt. = inv. dist. (0.00178) (0.00302) (0.00179) (0.00314) (0.00170) (0.00305)
T604 66605-66666 Sentence denotes Wuhan 0.00298 0.00110 − 0.00187 − 0.000887 0.00224 − 3.26e-07
T605 66667-66742 Sentence denotes wt. = pop. flow (0.00264) (0.00252) (0.00304) (0.00239) (0.00254) (0.00260)
T606 66743-66777 Sentence denotes Average # of new cases, 2-week lag
T607 66778-66836 Sentence denotes Own city 0.0345 − 0.0701 − 0.0103 − 0.0818 0.0396 − 0.0533
T608 66837-66890 Sentence denotes (0.0841) (0.0550) (0.0921) (0.0523) (0.0804) (0.0678)
T609 66891-66946 Sentence denotes × closed management − 0.367*** − 0.103 − 0.259** 0.0344
T610 66947-66979 Sentence denotes (0.0941) (0.136) (0.111) (0.222)
T611 66980-67030 Sentence denotes × stay at home − 0.294*** − 0.102 − 0.124* − 0.162
T612 67031-67064 Sentence denotes (0.0839) (0.136) (0.0720) (0.212)
T613 67065-67143 Sentence denotes Other cities − 0.00224 − 0.00412** − 0.00190 − 0.00381** − 0.00218 − 0.00397**
T614 67144-67220 Sentence denotes wt. = inv. dist. (0.00135) (0.00195) (0.00118) (0.00177) (0.00129) (0.00192)
T615 67221-67280 Sentence denotes Wuhan − 0.00512 0.00197 − 0.00445 0.00231 − 0.00483 0.00227
T616 67281-67357 Sentence denotes wt. = inv. dist. (0.00353) (0.00367) (0.00328) (0.00348) (0.00340) (0.00376)
T617 67358-67429 Sentence denotes Wuhan 0.00585*** 0.00554*** 0.00534*** 0.00523*** 0.00564*** 0.00516***
T618 67430-67506 Sentence denotes wt. = pop. flow (0.00110) (0.000929) (0.00112) (0.00104) (0.00109) (0.00116)
T619 67507-67549 Sentence denotes Observations 8064 8064 8064 8064 8064 8064
T620 67550-67590 Sentence denotes Number of cities 288 288 288 288 288 288
T621 67591-67631 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T622 67632-67663 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T623 67664-67695 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T624 67696-67777 Sentence denotes The sample is from February 2 to February 29, excluding cities in Hubei province.
T625 67778-67844 Sentence denotes The dependent variable is the number of daily new confirmed cases.
T626 67845-68134 Sentence denotes The instrumental variables include weekly averages of daily maximum temperature, wind speed, precipitation, and the interaction between wind speed and precipitation, in the preceding third and fourth weeks, and the inverse log distance weighted averages of these variables in other cities.
T627 68135-68346 Sentence denotes Additional instrumental variables are constructed by interacting these excluded instruments with variables that predict the adoption of closed management of communities or family outdoor restrictions (Table 10).
T628 68347-68440 Sentence denotes The weather controls include weather characteristics in the preceding first and second weeks.
T629 68441-68536 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T630 68537-68631 Sentence denotes We find that closed management and stay at home significantly decrease the transmission rates.
T631 68632-68778 Sentence denotes As a result of closed management of communities, one infection will generate 0.244 (95% CI, −0.366∼−0.123) fewer new infections in the first week.
T632 68779-68863 Sentence denotes The effect in the second week is also negative though not statistically significant.
T633 68864-69068 Sentence denotes Family outdoor restrictions (stay at home) are more restrictive than closing communities to visitors and reduce additional infections from one infection by 0.278 (95% CI, −0.435∼−0.121) in the first week.
T634 69069-69132 Sentence denotes The effect in the second week is not statistically significant.
T635 69133-69298 Sentence denotes To interpret the magnitude of the effect, it is noted that the reproduction number of SARS-CoV-2 is estimated to be around 1.4∼6.5 as of January 28, 2020 (Liu et al.
T636 69299-69305 Sentence denotes 2020).
T637 69306-69342 Sentence denotes Many cities implement both policies.
T638 69343-69506 Sentence denotes However, it is not conclusive to ascertain the effect of further imposing family outdoor restrictions in cities that have adopted closed management of communities.
T639 69507-69882 Sentence denotes When both policies are included in the model, the OLS coefficients (column (5)) indicate that closed management reduces the transmission rate by 0.547 (95% CI, −0.824∼−0.270) in the first week, and by 0.259 (95% CI, −0.485∼−0.032) in the second week, while the additional benefit from stay at home is marginally significant in the second week (− 0.124, 95% CI, −0.272∼0.023).
T640 69883-70115 Sentence denotes The IV estimates indicate that closed management reduces the transmission rate in the first week by 0.193 (95% CI, −0.411∼0.025), while the effect in the second week and the effects of stay at home are not statistically significant.
T641 70116-70333 Sentence denotes Additional research that examines the decision process of health authorities or documents the local differences in the actual implementation of the policies may offer insights into the relative merits of the policies.
T642 70334-70423 Sentence denotes We further assess the effects of NPIs by conducting a series of counterfactual exercises.
T643 70424-70478 Sentence denotes After estimating (3) by 2SLS, we obtain the residuals.
T644 70479-70690 Sentence denotes Then, the changes in yct are predicted for counterfactual changes in the transmission dynamics (i.e., coefficients αwithin,τk) and the impositions of NPIs (i.e., h¯ctkτ, and the lockdown of Wuhan m¯c,Wuhan,tkτ).
T645 70691-70767 Sentence denotes In scenario A, no cities adopted family outdoor restrictions (stay at home).
T646 70768-70849 Sentence denotes Similarly, in scenario B, no cities implemented closed management of communities.
T647 70850-70980 Sentence denotes We use the estimates in columns (2) and (4) of Table 8 to conduct the counterfactual analyses for scenarios A and B, respectively.
T648 70981-71241 Sentence denotes In scenario C, we assume that the index of population flows out of Wuhan after the Wuhan lockdown (January 23) took the value that was observed in 2019 for the same lunar calendar date (Fig. 3), which would be plausible had there been no lockdown around Wuhan.
T649 71242-71381 Sentence denotes It is also likely that in the absence of lockdown but with the epidemic, more people would leave Wuhan compared with last year (Fang et al.
T650 71382-71425 Sentence denotes 2020), and the effect would then be larger.
T651 71426-71849 Sentence denotes In scenario D, we assume that the within-city transmission dynamics were the same as those observed between January 19 and February 1, i.e., the coefficient of 1-week lag own-city infections was 2.456 and the coefficient of 2-week lag own-city infections was − 1.633 (column (4) of Table 4), which may happen if the transmission rates in cities outside Hubei increased in the same way as those observed for cities in Hubei.
T652 71850-71930 Sentence denotes Appendix C contains the technical details on the computation of counterfactuals.
T653 71931-72103 Sentence denotes In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province.
T654 72104-72219 Sentence denotes We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7.
T655 72220-72422 Sentence denotes Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D).
T656 72423-72489 Sentence denotes Assuming a fatality rate of 4%, there would be 56,339 more deaths.
T657 72490-72577 Sentence denotes The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller.
T658 72578-72721 Sentence denotes As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C).
T659 72722-72960 Sentence denotes Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively.
T660 72961-73151 Sentence denotes These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.
T661 73152-73193 Sentence denotes Fig. 7 Counterfactual policy simulations.
T662 73194-73420 Sentence denotes This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China.
T663 73421-73606 Sentence denotes The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)
T664 73607-73857 Sentence denotes Our counterfactual simulations indicate that suppressing local virus transmissions so that transmission rates are kept well below those observed in Hubei in late January is crucial in forestalling large numbers of infections for cities outside Hubei.
T665 73858-73967 Sentence denotes Our retrospective analysis of the data from China complements the simulation study of Ferguson et al. (2020).
T666 73968-74106 Sentence denotes Our estimates indicate that suppressing local transmission rates at low levels might have avoided one million or more infections in China.
T667 74107-74230 Sentence denotes Chinazzi et al. (2020) also find that reducing local transmission rates is necessary for effective containment of COVID-19.
T668 74231-74389 Sentence denotes The public health policies announced by the national and provincial authorities in the last 2 weeks in January may have played a determinant role (Tian et al.
T669 74390-74502 Sentence denotes 2020) in keeping local transmission rates in cities outside Hubei at low levels throughout January and February.
T670 74503-74786 Sentence denotes Among the measures implemented following provincial level I responses, Shen et al. (2020) highlight the importance of contact tracing and isolation of close contacts before onset of symptoms in preventing a resurgence of infections once the COVID-19 suppression measures are relaxed.
T671 74787-74994 Sentence denotes We also find that travel restrictions on high-risk areas (the lockdown in Wuhan), and to a lesser extent, closed management of communities and family outdoor restrictions, further reduce the number of cases.
T672 74995-75063 Sentence denotes It should be noted that these factors may overlap in the real world.
T673 75064-75234 Sentence denotes In the absence of the lockdown in Wuhan, the health care systems in cities outside Hubei could face much more pressure, and local transmissions may have been much higher.
T674 75235-75332 Sentence denotes In China, the arrival of the COVID-19 epidemic coincided with the Lunar New Year for many cities.
T675 75333-75445 Sentence denotes Had the outbreak started at a different time, the effects and costs of these policies would likely be different.
T676 75447-75457 Sentence denotes Conclusion
T677 75458-75602 Sentence denotes This paper examines the transmission dynamics of the coronavirus disease 2019 in China, considering both within- and between-city transmissions.
T678 75603-75833 Sentence denotes Our sample is from January 19 to February 29 and covers key episodes such as the initial spread of the virus across China, the peak of infections in terms of domestic case counts, and the gradual containment of the virus in China.
T679 75834-75994 Sentence denotes Changes in weather conditions induce exogenous variations in past infection rates, which allow us to identify the causal impact of past infections on new cases.
T680 75995-76156 Sentence denotes The estimates suggest that the infectious effect of the existing cases is mostly observed within 1 week and people’s responses can break the chain of infections.
T681 76157-76329 Sentence denotes Comparing estimates in two sub-samples, we observe that the spread of COVID-19 has been effectively contained by mid February, especially for cities outside Hubei province.
T682 76330-76418 Sentence denotes Data on real-time population flows between cities have become available in recent years.
T683 76419-76607 Sentence denotes We show that this new source of data is valuable in explaining between-city transmissions of COVID-19, even after controlling for traditional measures of geographic and economic proximity.
T684 76608-76747 Sentence denotes By April 5 of 2020, COVID-19 infections have been reported in more than 200 countries or territories and more than 64,700 people have died.
T685 76748-76854 Sentence denotes Behind the grim statistics, more and more national and local governments are implementing countermeasures.
T686 76855-76948 Sentence denotes Cross border travel restrictions are imposed in order to reduce the risk of case importation.
T687 76949-77103 Sentence denotes In areas with risks of community transmissions, public health measures such as social distancing, mandatory quarantine, and city lockdown are implemented.
T688 77104-77381 Sentence denotes In a series of counterfactual simulations, we find that based on the experience in China, preventing sustained community transmissions from taking hold in the first place has the largest impact, followed by restricting population flows from areas with high risks of infections.
T689 77382-77528 Sentence denotes Local public health measures such as closed management of communities and family outdoor restrictions can further reduce the number of infections.
T690 77529-77821 Sentence denotes A key limitation of the paper is that we are not able to disentangle the effects from each of the stringent measures taken, as within this 6-week sampling period, China enforced such a large number of densely timed policies to contain the virus spreading, often simultaneously in many cities.
T691 77822-78170 Sentence denotes A second limitation is that shortly after the starting date of the official data release for confirmed infected cases throughout China, i.e., January 19, 2020, many stringent measures were implemented, which prevents researchers to compare the post treatment sub-sample with a pre treatment sub-sample during which no strict policies were enforced.
T692 78171-78365 Sentence denotes Key knowledge gaps remain in the understanding of the epidemiological characteristics of COVID-19, such as individual risk factors for contracting the virus and infections from asymptotic cases.
T693 78366-78512 Sentence denotes Data on the demographics and exposure history for those who have shown symptoms as well as those who have not will help facilitate these research.
T694 78514-78522 Sentence denotes Appendix
T695 78523-78563 Sentence denotes The Appendix consists of three sections.
T696 78564-78717 Sentence denotes Section A provides details on the first stage of the IV regressions and the selection of the instrumental variables for the local public health policies.
T697 78718-78852 Sentence denotes Section B shows that our main findings are not sensitive to the adjustment in COVID-19 case definitions in Hubei province in February.
T698 78853-78922 Sentence denotes Section A contains details on the computation of the counterfactuals.
T699 78924-78935 Sentence denotes Appendix A.
T700 78936-78959 Sentence denotes First stage regressions
T701 78960-79137 Sentence denotes Weather conditions affect disease transmissions either directly if the virus can more easily survive and spread in certain environment, or indirectly by changing human behavior.
T702 79138-79235 Sentence denotes Table 9 reports results of the first stage of the IV regressions (Table 4) using the full sample.
T703 79236-79404 Sentence denotes In columns (1) and (2), the dependent variables are the numbers of newly confirmed COVID-19 cases in the own city in the preceding first and second weeks, respectively.
T704 79405-79610 Sentence denotes In columns (3) and (4), the dependent variables are the sum of inverse log distance weighted numbers of newly confirmed COVID-19 cases in other cities in the preceding first and second weeks, respectively.
T705 79611-79668 Sentence denotes These are the endogenous variables in the IV regressions.
T706 79669-79769 Sentence denotes The weather variables in the preceding first and second weeks are included in the control variables.
T707 79770-79911 Sentence denotes The weather variables in the preceding third and fourth weeks are the excluded instruments, and their coefficients are reported in the table.
T708 79912-80057 Sentence denotes Because the variables are averages in 7-day moving windows, the error term may be serially correlated, and we include city by week fixed effects.
T709 80058-80248 Sentence denotes Also included in the control variables are the average numbers of new cases in Wuhan in the preceding first and second weeks, interacted with the inverse log distance or the population flow.
T710 80249-80280 Sentence denotes Table 9 First stage regressions
T711 80281-80322 Sentence denotes Dependent variable Average # of new cases
T712 80323-80344 Sentence denotes Own city Other cities
T713 80345-80388 Sentence denotes 1-week lag 2-week lag 1-week lag 2-week lag
T714 80389-80404 Sentence denotes (1) (2) (3) (4)
T715 80405-80413 Sentence denotes Own City
T716 80414-80480 Sentence denotes Maximum temperature, 3-week lag 0.200*** − 0.0431 0.564 − 2.022***
T717 80481-80514 Sentence denotes (0.0579) (0.0503) (0.424) (0.417)
T718 80515-80571 Sentence denotes Precipitation, 3-week lag − 0.685 − 0.865* 4.516 − 1.998
T719 80572-80603 Sentence denotes (0.552) (0.480) (4.045) (3.982)
T720 80604-80655 Sentence denotes Wind speed, 3-week lag 0.508** 0.299 − 0.827 3.247*
T721 80656-80687 Sentence denotes (0.256) (0.223) (1.878) (1.849)
T722 80688-80758 Sentence denotes Precipitation × wind speed, 3-week lag − 0.412** 0.122 − 1.129 − 2.091
T723 80759-80790 Sentence denotes (0.199) (0.173) (1.460) (1.437)
T724 80791-80857 Sentence denotes Maximum temperature, 4-week lag 0.162*** 0.125** 1.379*** 1.181***
T725 80858-80891 Sentence denotes (0.0560) (0.0487) (0.410) (0.404)
T726 80892-80947 Sentence denotes Precipitation, 4-week lag 0.0250 − 0.503 2.667 8.952***
T727 80948-80979 Sentence denotes (0.440) (0.383) (3.224) (3.174)
T728 80980-81028 Sentence denotes Wind speed, 4-week lag 0.179 0.214 − 1.839 1.658
T729 81029-81060 Sentence denotes (0.199) (0.173) (1.458) (1.435)
T730 81061-81134 Sentence denotes Precipitation × wind speed, 4-week lag − 0.354** − 0.0270 1.107 − 2.159**
T731 81135-81166 Sentence denotes (0.145) (0.126) (1.059) (1.043)
T732 81167-81206 Sentence denotes Other cities, weight = inverse distance
T733 81207-81276 Sentence denotes Maximum temperature, 3-week lag − 0.0809*** − 0.00633 0.0520 1.152***
T734 81277-81310 Sentence denotes (0.0203) (0.0176) (0.149) (0.146)
T735 81311-81376 Sentence denotes Precipitation, 3-week lag 4.366*** − 2.370*** 17.99*** − 72.68***
T736 81377-81408 Sentence denotes (0.639) (0.556) (4.684) (4.611)
T737 81409-81469 Sentence denotes Wind speed, 3-week lag 0.326*** − 0.222** − 1.456 − 11.02***
T738 81470-81501 Sentence denotes (0.126) (0.110) (0.926) (0.912)
T739 81502-81580 Sentence denotes Precipitation × wind speed, 3-week lag − 1.780*** 0.724*** − 6.750*** 27.73***
T740 81581-81612 Sentence denotes (0.227) (0.197) (1.663) (1.637)
T741 81613-81684 Sentence denotes Maximum temperature, 4-week lag − 0.0929*** − 0.0346* − 0.518*** 0.0407
T742 81685-81718 Sentence denotes (0.0220) (0.0191) (0.161) (0.159)
T743 81719-81781 Sentence denotes Precipitation, 4-week lag 3.357*** − 0.578 46.57*** − 25.31***
T744 81782-81813 Sentence denotes (0.504) (0.438) (3.691) (3.633)
T745 81814-81873 Sentence denotes Wind speed, 4-week lag 0.499*** 0.214** 4.660*** − 4.639***
T746 81874-81906 Sentence denotes (0.107) (0.0934) (0.787) (0.774)
T747 81907-81985 Sentence denotes Precipitation × wind speed, 4-week lag − 1.358*** − 0.0416 − 17.26*** 8.967***
T748 81986-82017 Sentence denotes (0.178) (0.155) (1.303) (1.282)
T749 82018-82052 Sentence denotes F statistic 11.41 8.46 19.10 36.32
T750 82053-82088 Sentence denotes p value 0.0000 0.0000 0.0000 0.0000
T751 82089-82129 Sentence denotes Observations 12,768 12,768 12,768 12,768
T752 82130-82162 Sentence denotes Number of cities 304 304 304 304
T753 82163-82195 Sentence denotes # cases in Wuhan Yes Yes Yes Yes
T754 82196-82244 Sentence denotes Contemporaneous weather controls Yes Yes Yes Yes
T755 82245-82268 Sentence denotes City FE Yes Yes Yes Yes
T756 82269-82292 Sentence denotes Date FE Yes Yes Yes Yes
T757 82293-82324 Sentence denotes City by week FE Yes Yes Yes Yes
T758 82325-82388 Sentence denotes This table shows the results of the first stage IV regressions.
T759 82389-82457 Sentence denotes The weather variables are weekly averages of daily weather readings.
T760 82458-82596 Sentence denotes Coefficients of the weather variables which are used as excluded instrumental variables are reported. *** p < 0.01, ** p < 0.05, * p < 0.1
T761 82597-82792 Sentence denotes Because the spread of the virus depends on both the number of infectious people and the weather conditions, the coefficients in the first stage regressions do not have structural interpretations.
T762 82793-82912 Sentence denotes The Wald tests on the joint significance of the excluded instruments are conducted and their F statistics are reported.
T763 82913-82965 Sentence denotes The excluded instruments have good predictive power.
T764 82966-83187 Sentence denotes The implementation of local public health measures is likely correlated with the extent of the virus spread, so weather conditions that affect virus transmissions may also affect the likelihood that the policy is adopted.
T765 83188-83470 Sentence denotes The influence of weather conditions on policy adoption may be complicated, so we use the Cluster-Lasso method of Belloni et al. (2016) to select the weather variables that have good predictive power on the adoption of closed management of communities or family outdoor restrictions.
T766 83471-83982 Sentence denotes Let dct be the dummy variable of the adoption of the local public health measure, i.e., dct = 1 if the policy is in place in city c at day t. qct is a vector of candidate weather variables, including weekly averages of daily mean temperature, maximum temperature, minimum temperature, dew point, station-level pressure, sea-level pressure, visibility, wind speed, maximum wind speed, snow depth, precipitation, dummy for adverse weather conditions, squared terms of these variables, and interactions among them.
T767 83983-84095 Sentence denotes First, city and day fixed effects are removed. d¨ct=dct−1n∑cdct−1T∑tdct+1nT∑ctdct and q¨ct is defined similarly.
T768 84096-84163 Sentence denotes The Cluster-Lasso method solves the following minimization problem:
T769 84164-84226 Sentence denotes 1nT∑ctd¨ct−q¨ct′b2+λnT∑kϕk|bk|.λ and ϕ are penalty parameters.
T770 84227-84283 Sentence denotes A larger penalty value forces more coefficients to zero.
T771 84284-84372 Sentence denotes The penalty parameters are picked using the theoretical result of Belloni et al. (2016).
T772 84373-84435 Sentence denotes The estimation uses the Stata package by Ahrens et al. (2019).
T773 84436-84528 Sentence denotes Table 10 lists the selected weather variables, which are used as the instruments in Table 8.
T774 84529-84556 Sentence denotes Table 10 Variables selected
T775 84557-84609 Sentence denotes Dependent variable: closed management of communities
T776 84610-84630 Sentence denotes Dew point 1-week lag
T777 84631-84667 Sentence denotes Diurnal temperature range 1-week lag
T778 84668-84688 Sentence denotes Dew point 2-week lag
T779 84689-84718 Sentence denotes Sea-level pressure 2-week lag
T780 84719-84739 Sentence denotes Dew point 3-week lag
T781 84740-84761 Sentence denotes Visibility 4-week lag
T782 84762-84786 Sentence denotes Precipitation 4-week lag
T783 84787-84834 Sentence denotes Dependent variable: family outdoor restrictions
T784 84835-84862 Sentence denotes Station pressure 1-week lag
T785 84863-84941 Sentence denotes Dummy for adverse weather conditions such as fog, rain, and drizzle 1-week lag
T786 84942-84972 Sentence denotes Maximum temperature 2-week lag
T787 84973-85002 Sentence denotes Sea-level pressure 2-week lag
T788 85003-85033 Sentence denotes Average temperature 3-week lag
T789 85034-85064 Sentence denotes Minimum temperature 3-week lag
T790 85065-85086 Sentence denotes Visibility 3-week lag
T791 85087-85162 Sentence denotes This table shows the weather variables selected by lassopack (Ahrens et al.
T792 85163-85237 Sentence denotes 2019), which implements the Cluster-Lasso method of Belloni et al. (2016).
T793 85238-85279 Sentence denotes City and date fixed effects are included.
T794 85280-85618 Sentence denotes Candidate variables include weekly averages of daily mean temperature, maximum temperature, minimum temperature, dew point, station-level pressure, sea-level pressure, visibility, wind speed, maximum wind speed, snow depth, precipitation, dummy for adverse weather conditions, squared terms of these variables, and interactions among them
T795 85620-85631 Sentence denotes Appendix B.
T796 85632-85675 Sentence denotes Exclude clinically diagnosed cases in Hubei
T797 85676-85764 Sentence denotes COVID-19 case definitions were changed in Hubei province on February 12 and February 20.
T798 85765-85918 Sentence denotes Starting on February 12, COVID-19 cases could also be confirmed based on clinical diagnosis in Hubei province, in addition to molecular diagnostic tests.
T799 85919-86038 Sentence denotes This resulted in a sharp increase in the number of daily new cases reported in Hubei, and in particular Wuhan (Fig. 2).
T800 86039-86110 Sentence denotes The use of clinical diagnosis in confirming cases ended on February 20.
T801 86111-86293 Sentence denotes The numbers of cases that are confirmed based on clinical diagnosis for February 12, 13, and 14 are reported by the Health Commission of Hubei Province and are displayed in Table 11.
T802 86294-86405 Sentence denotes As a robustness check, we re-estimate the model after removing these cases from the daily case counts (Fig. 8).
T803 86406-86446 Sentence denotes Our main findings still hold (Table 12).
T804 86447-86528 Sentence denotes The transmission rates are significantly lower in February compared with January.
T805 86529-86660 Sentence denotes Population flow from the epidemic source increases the infections in destinations, and this effect is slightly delayed in February.
T806 86661-86775 Sentence denotes Fig. 8 Number of daily new confirmed cases of COVID-19 in mainland China and revised case counts in Hubei Province
T807 86776-86841 Sentence denotes Table 11 Number of cumulative clinically diagnosed cases in Hubei
T808 86842-86867 Sentence denotes City Feb 12 Feb 13 Feb 14
T809 86868-86885 Sentence denotes Ezhou 155 168 189
T810 86886-86900 Sentence denotes Enshi 19 21 27
T811 86901-86922 Sentence denotes Huanggang 221 306 306
T812 86923-86940 Sentence denotes Huangshi 12 26 42
T813 86941-86962 Sentence denotes Jingmen 202 155‡ 150‡
T814 86963-86985 Sentence denotes Jingzhou 287 269‡ 257‡
T815 86986-87002 Sentence denotes Qianjiang 0 9 19
T816 87003-87016 Sentence denotes Shiyan 3 4 3‡
T817 87017-87031 Sentence denotes Suizhou 0 6 4‡
T818 87032-87049 Sentence denotes Tianmen 26 67 65‡
T819 87050-87073 Sentence denotes Wuhan 12364 14031 14953
T820 87074-87087 Sentence denotes Xiantao 2 2 2
T821 87088-87106 Sentence denotes Xianning 6 189 286
T822 87107-87122 Sentence denotes Xiangyang 0 0 4
T823 87123-87140 Sentence denotes Xiaogan 35 80 148
T824 87141-87156 Sentence denotes Yichang 0 51 67
T825 87157-87246 Sentence denotes ‡The reductions in cumulative case counts are due to revised diagnosis from further tests
T826 87247-87344 Sentence denotes Table 12 Within- and between-city transmission of COVID-19, revised case counts in Hubei Province
T827 87345-87384 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T828 87385-87408 Sentence denotes (1) (2) (3) (4) (5) (6)
T829 87409-87429 Sentence denotes OLS IV OLS IV OLS IV
T830 87430-87520 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T831 87521-87555 Sentence denotes Average # of new cases, 1-week lag
T832 87556-87618 Sentence denotes Own city 0.747*** 0.840*** 0.939*** 2.456*** 0.790*** 1.199***
T833 87619-87670 Sentence denotes (0.0182) (0.0431) (0.102) (0.638) (0.0211) (0.0904)
T834 87671-87733 Sentence denotes Other cities 0.00631** 0.0124 0.0889 0.0412 − 0.00333 − 0.0328
T835 87734-87807 Sentence denotes wt. = inv. dist. (0.00289) (0.00897) (0.0714) (0.0787) (0.00601) (0.0230)
T836 87808-87861 Sentence denotes Wuhan 0.0331*** 0.0277 − 0.879 − 0.957 0.0543* 0.0840
T837 87862-87930 Sentence denotes wt. = inv. dist. (0.0116) (0.0284) (0.745) (0.955) (0.0271) (0.0684)
T838 87931-88004 Sentence denotes Wuhan 0.00365*** 0.00408*** 0.00462*** 0.00471*** − 0.000882 − 0.00880***
T839 88005-88085 Sentence denotes wt. = pop. flow (0.000282) (0.000287) (0.000326) (0.000696) (0.000797) (0.00252)
T840 88086-88120 Sentence denotes Average # of new cases, 2-week lag
T841 88121-88184 Sentence denotes Own city − 0.519*** − 0.673*** 2.558 − 1.633 − 0.286*** − 0.141
T842 88185-88236 Sentence denotes (0.0138) (0.0532) (2.350) (2.951) (0.0361) (0.0899)
T843 88237-88306 Sentence denotes Other cities − 0.00466 − 0.0208 − 0.361 − 0.0404 − 0.00291 − 0.0235**
T844 88307-88377 Sentence denotes wt. = inv. dist. (0.00350) (0.0143) (0.371) (0.496) (0.00566) (0.0113)
T845 88378-88427 Sentence denotes Wuhan − 0.0914* 0.0308 3.053 3.031 − 0.154 0.0110
T846 88428-88496 Sentence denotes wt. = inv. dist. (0.0465) (0.0438) (2.834) (3.559) (0.0965) (0.0244)
T847 88497-88565 Sentence denotes Wuhan 0.00827*** 0.00807*** 0.00711*** − 0.00632 0.0119*** 0.0112***
T848 88566-88645 Sentence denotes wt. = pop. flow (0.000264) (0.000185) (0.00213) (0.00741) (0.000523) (0.000627)
T849 88646-88711 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T850 88712-88774 Sentence denotes Own city 0.235*** 0.983*** 1.564*** 2.992*** 0.391*** 0.725***
T851 88775-88824 Sentence denotes (0.0355) (0.158) (0.174) (0.892) (0.0114) (0.101)
T852 88825-88886 Sentence denotes Other cities 0.00812 − 0.0925* 0.0414 0.0704 0.0181 − 0.00494
T853 88887-88958 Sentence denotes wt. = inv. dist. (0.00899) (0.0480) (0.0305) (0.0523) (0.0172) (0.0228)
T854 88959-89016 Sentence denotes Wuhan − 0.172* − 0.114** − 0.309 − 0.608 − 0.262 − 0.299*
T855 89017-89082 Sentence denotes wt. = inv. dist. (0.101) (0.0472) (0.251) (0.460) (0.161) (0.169)
T856 89083-89147 Sentence denotes Wuhan 0.0133*** 0.0107*** 0.00779*** 0.00316 0.0152*** 0.0143***
T857 89148-89228 Sentence denotes wt. = pop. flow (0.000226) (0.000509) (0.000518) (0.00276) (0.000155) (0.000447)
T858 89229-89279 Sentence denotes Observations 12,768 12,768 4,256 4,256 8,512 8,512
T859 89280-89320 Sentence denotes Number of cities 304 304 304 304 304 304
T860 89321-89361 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T861 89362-89393 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T862 89394-89425 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T863 89426-89482 Sentence denotes The dependent variable is the number of daily new cases.
T864 89483-89703 Sentence denotes The endogenous explanatory variables include the average numbers of new confirmed cases in the own city and nearby cities in the preceding first and second weeks (model A) and averages in the preceding 14 days (model B).
T865 89704-90011 Sentence denotes Weekly averages of daily maximum temperature, precipitation, wind speed, the interaction between precipitation and wind speed, and the inverse log distance weighted sum of these variables in other cities, during the preceding third and fourth weeks, are used as instrumental variables in the IV regressions.
T866 90012-90111 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T867 90112-90207 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T868 90209-90220 Sentence denotes Appendix C.
T869 90221-90251 Sentence denotes Computation of counterfactuals
T870 90252-90269 Sentence denotes Our main model is
T871 90270-90404 Sentence denotes 4 yct=∑τ=12∑k=1Kwithinαwithin,τkh¯ctkτy¯ctτ+∑τ=12∑k=1Kbetween∑r≠cαbetween,τkm¯crtkτy¯rtτ+∑τ=12∑k=1KWuhanρτkm¯c,Wuhan,tkτz¯tτ+xctβ+𝜖ct.
T872 90405-90520 Sentence denotes It is convenient to write it in vector form, 5 Ynt=∑s=114Hnt,s(αwithin)+Mnt,s(αbetween)Yn,t−s+∑τ=12Zntτρτ+Xntβ+𝜖nt,
T873 90521-90566 Sentence denotes where Ynt=y1t⋯ynt′ and 𝜖nt are n × 1 vectors.
T874 90567-90728 Sentence denotes Assuming that Yns = 0 if s ≤ 0, because our sample starts on January 19, and no laboratory confirmed case was reported before January 19 in cities outside Wuhan.
T875 90729-90788 Sentence denotes Xnt=x1t′⋯xnt′′ is an n × k matrix of the control variables.
T876 90789-90925 Sentence denotes Hnt,s(αwithin) is an n × n diagonal matrix corresponding to the s-day time lag, with parameters αwithin={αwithin,τk}k=1,⋯,Kwithin,τ=1,2.
T877 90926-91137 Sentence denotes For example, for s = 1,⋯ , 7, the i th diagonal element of Hnt,s(αwithin) is 17∑k=1Kwithinαwithin,1kh¯ct,ik1, and for s = 8,⋯ , 14, the i th diagonal element of Hnt,s(αwithin) is 17∑k=1Kwithinαwithin,2kh¯ct,ik2.
T878 91138-91179 Sentence denotes Mnt,s(αbetween) is constructed similarly.
T879 91180-91291 Sentence denotes For example, for s = 1,⋯ , 7 and i≠j, the ij th element of Mnt,s(αbetween) is 17∑k=1Kbetweenαbetween,1km¯ijtk1.
T880 91292-91366 Sentence denotes Zntτ is an n × KWuhan matrix corresponding to the transmission from Wuhan.
T881 91367-91427 Sentence denotes For example, the ik th element of Znt1 is m¯i,Wuhan,tk1z¯t1.
T882 91428-91515 Sentence denotes We first estimate the parameters in Eq. 4 by 2SLS and obtain the residuals 𝜖^n1,⋯,𝜖^nT.
T883 91516-91605 Sentence denotes Let ⋅^ denote the estimated value of parameters and ⋅~ denote the counterfactual changes.
T884 91606-91848 Sentence denotes The counterfactual value of Ynt is computed recursively, Y~n1=∑τ=12Z~n1τρ^τ+Xn1β^+𝜖^n1,Y~n2=∑s=11H~n2,s(α^within)+M~n2,s(α^between)Y~n,2−s+∑τ=12Z~n2τρ^τ+Xn2β^+𝜖^n2,Y~n3=∑s=12H~n3,s(α^within)+M~n3,s(α^between)Y~n,3−s+∑τ=12Z~n3τρ^τ+Xn3β^+𝜖^n3,⋮
T885 91849-91903 Sentence denotes The counterfactual change for date t is ΔYnt=Y~nt−Ynt.
T886 91904-91974 Sentence denotes The standard error of ΔYnt is obtained from 1000 bootstrap iterations.
T887 91975-92092 Sentence denotes In each bootstrap iteration, cities are sampled with replacement and the model is estimated to obtain the parameters.
T888 92093-92260 Sentence denotes The counterfactual predictions are obtained using the above equations with the estimated parameters and the counterfactual scenario (e.g., no cities adopted lockdown).
T889 92261-92357 Sentence denotes 1 COVID-19 is also known as novel coronavirus pneumonia or 2019-nCoV acute respiratory disease.
T890 92358-92407 Sentence denotes 2 In 2020, the Lunar New Year was on January 25.
T891 92408-92630 Sentence denotes 3 Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6).
T892 92631-92770 Sentence denotes 4 For instance, using data on the first 425 COVID-19 patients by January 22, Li et al. (2020) estimate a basic reproduction number of 2.2.
T893 92771-92965 Sentence denotes Based on time-series data on the number of COVID-19 cases in mainland China from January 10 to January 24, Zhao et al. (2020) estimate that the mean reproduction number ranges from 2.24 to 3.58.
T894 92966-93049 Sentence denotes 5 We assume a case fatality rate of 4%, the same as China’s current average level.
T895 93050-93323 Sentence denotes Of course, the eventual case fatality rate may be different from the current value, and it depends on many key factors, such as the preparedness of health care systems and the demographic structure of the population outside Hubei province in comparison to China as a whole.
T896 93324-93544 Sentence denotes Also importantly, among patients who have died from COVID-19, the time from symptom onset to outcome ranges from 2 to 8 weeks (World Health Organization 2020b), which is partially beyond the time window of this analysis.
T897 93545-93630 Sentence denotes Therefore, we defer more rigorous estimates about avoided fatality to future studies.
T898 93631-93704 Sentence denotes 6 Li et al. (2020) document the exposure history of the first 425 cases.
T899 93705-93804 Sentence denotes It is suspected that the initial cases were linked to the Huanan Seafood Wholesale Market in Wuhan.
T900 93805-93966 Sentence denotes 7 The COVID-19 epidemic is still ongoing at the time of writing, and the estimates are revised from time to time in the literature as new data become available.
T901 93967-94011 Sentence denotes The current estimates include the following.
T902 94012-94129 Sentence denotes The incubation period is estimated to be between 2 and 10 days (World Health Organization 2020a), 5.2 days (Li et al.
T903 94130-94176 Sentence denotes 2020), or 3 days (median, Guan et al. (2020)).
T904 94177-94245 Sentence denotes The average infectious period is estimated to be 1.4 days (Wu et al.
T905 94246-94253 Sentence denotes 2020a).
T906 94254-94522 Sentence denotes 8 On February 12, cities in Hubei province include clinically diagnosed cases in the confirmed cases, in addition to cases that are confirmed by nucleic acid tests, which results in a sharp increase in the number of confirmed cases for cities in Hubei on February 12.
T907 94523-94599 Sentence denotes The common effect on other cities is controlled for by the day fixed effect.
T908 94600-94653 Sentence denotes 9 Flu viruses are easier to survive in cold weather.
T909 94654-94766 Sentence denotes Adverse weather conditions also limit outdoor activities which can decrease the chance of contracting the virus.
T910 94767-94812 Sentence denotes For details, see Adda (2016) and Section 3.2.
T911 94813-94916 Sentence denotes 10 Hong Kong and Macao are excluded from our analysis due to the lack of some socioeconomic variables.
T912 94917-94964 Sentence denotes 11 Baidu migration (https://qianxi.baidu.com).
T913 94965-95030 Sentence denotes 12 The 100-km circle is consistent with the existing literature.
T914 95031-95192 Sentence denotes Most studies on the socioeconomic impacts of climate change have found that estimation results are insensitive to the choice of the cutoff distance (Zhang et al.
T915 95193-95199 Sentence denotes 2017).
T916 95200-95281 Sentence denotes 13 These cities are Xiaogan, Huanggang, Jingzhou, Suizhou, Ezhou, and Xiangyang.
T917 95282-95335 Sentence denotes 14 The shares of top 100 destinations are available.
T918 95336-95463 Sentence denotes The starting and ending dates of the average shares released by Baidu do not precisely match the period of the analysis sample.
T919 95464-95609 Sentence denotes 15 It is estimated that 14,925,000 people traveled out of Wuhan in 2019 during the Lunar New Year holiday (http://www.whtv.com.cn/p/17571.html).
T920 95610-95798 Sentence denotes The sum of Baidu’s migration index for population flow out of Wuhan during the 40 days around the 2019 Lunar New Year is 203.3, which means one index unit represents 0.000013621 travelers.
T921 95799-95838 Sentence denotes The destination share is in percentage.
T922 95839-95974 Sentence denotes With one more case in Wuhan, the effect on a city receiving 10,000 travelers from Wuhan is 0.00471 × 0.000013621 × 100 × 10000 = 0.064.
T923 95975-96014 Sentence denotes 16 http://www.whtv.com.cn/p/17571.html
T924 96015-96177 Sentence denotes 17 From mid February, individual specific health codes such as Alipay Health Code and WeChat Health Code are being used in many cities to aid quarantine efforts.
T925 96178-96238 Sentence denotes 18 Disease prevalence can also affect economic development.
T926 96239-96386 Sentence denotes One channel is the fertility decision which leads to changes in the demographic structure (e.g., Durevall and Lindskog 2011; Chin and Wilson 2018).
T927 96387-96661 Sentence denotes Fogli and Veldkamp (forthcoming) show that because a dense network spreads diseases faster and higher income is positively correlated with more closely connected social network, infectious diseases can reduce long-run economic growth by limiting the size of social networks.
T928 96662-96758 Sentence denotes 19 There was insufficient hospital capacity in Hubei (and Wuhan in particular) in late January.
T929 96759-96931 Sentence denotes Most patients in Wuhan were hospitalized and isolated around mid February with the completion of new hospitals, makeshift health facilities, and increased testing capacity.
T930 96932-96960 Sentence denotes See Section 5.1 for details.
T931 96961-97058 Sentence denotes 20 We should note that the summary of China’s policy responses here is not a comprehensive list.
T932 97059-97136 Sentence denotes Other entities have also made efforts to help curtail the spread of COVID-19.
T933 97137-97314 Sentence denotes For example, on January 27, the State Grid Corporation of China declared that it would continue supplying electricity to resident users even if payment was not received on time.
T934 97315-97394 Sentence denotes School and universities were closed already because of Lunar New Year holidays.
T935 97395-97562 Sentence denotes 21 According to Law of the People’s Republic of China on Prevention and Treatment of Infectious Diseases, class A infectious diseases only include plague and cholera.
T936 97563-97737 Sentence denotes 22 For a list of quarantine measures, see 2020 Hubei lockdowns (https://en.wikipedia.org/w/index.php?title=2020_Hubei_lockdowns&oldid=946423465), last visited April 2, 2020.
T937 97738-97805 Sentence denotes 23 Wenzhou, Zhengzhou, Hangzhou, Zhumadian, Ningbo, Harbin, Fuzhou
T938 97806-97865 Sentence denotes 24 This restriction varies from 1 to 5 days across cities.
T939 97866-98049 Sentence denotes In most cities, such restrictions are once every 2 days. “Closed management of communities” and “family outdoor restrictions” were mostly announced in city-level government documents.
T940 98050-98205 Sentence denotes There are some cities in which only part of their counties declared to implement “closed management of communities” or “family outdoor restriction” policy.
T941 98206-98282 Sentence denotes However, other counties in the same city may have quickly learned from them.
T942 98283-98460 Sentence denotes Thus, as long as one county in a city has implemented “closed management of communities” or “family outdoor restrictions,” we treat the whole city as having the policy in place.
T943 98461-98477 Sentence denotes Publisher’s note
T944 98478-98596 Sentence denotes Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
T945 98598-98752 Sentence denotes We are grateful to Editor Klaus Zimmermann and three anonymous referees for valuable comments and suggestions which have helped greatly improve the paper.
T946 98753-98975 Sentence denotes We received helpful comments and suggestions from Hanming Fang and seminar participants at Institute for Economic and Social Research of Jinan University and VoxChina Covid-19 Public Health and Public Policy Virtual Forum.
T947 98976-99036 Sentence denotes Pei Yu and Wenjie Wu provided excellent research assistance.
T948 99037-99060 Sentence denotes All errors are our own.
T949 99062-99081 Sentence denotes Funding Information
T950 99082-99166 Sentence denotes Qiu and Shi acknowledge the support from the 111 Project of China (Grant No.B18026).
T951 99167-99301 Sentence denotes Chen thanks the following funding sources: US PEPPER Center Scholar Award (P30AG021342) and NIH/NIA grants (R03AG048920; K01AG053408).
T952 99302-99466 Sentence denotes Shi thanks the National Natural Science Foundation of China (Grant No.71803062) and the Ministry of Education of China (Grant No.18YJC790138) for financial support.
T953 99468-99501 Sentence denotes Compliance with ethical standards
T954 99503-99524 Sentence denotes Conflict of interests
T955 99525-99584 Sentence denotes The authors declare that they have no conflict of interest.