LitCovid-sentences  

PMC:7210464 / 27989-54231 JSONTXT 8 Projects

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Id Subject Object Predicate Lexical cue
T220 0-7 Sentence denotes Results
T221 8-99 Sentence denotes Our sample starts from January 19, when the first COVID-19 case was reported outside Wuhan.
T222 100-158 Sentence denotes The sample spans 6 weeks in total and ends on February 29.
T223 159-345 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 346-550 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 551-666 Sentence denotes It is also during these 2 weeks that the Chinese government took actions swiftly to curtail the virus transmission.
T226 667-802 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 803-930 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 931-1099 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 1100-1238 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 1239-1515 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 1516-1618 Sentence denotes As a robustness check, we also consider a simpler lag structure to describe the transmission dynamics.
T232 1619-1754 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 1756-1780 Sentence denotes Within-city transmission
T234 1781-1913 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 1914-2127 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 2128-2342 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 2343-2584 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 2585-2650 Sentence denotes We then compare the transmission rates in different time windows.
T239 2651-2781 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 2782-2968 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 2969-3015 Sentence denotes Similar patterns are also observed in model B.
T242 3016-3060 Sentence denotes Table 3 Within-city transmission of COVID-19
T243 3061-3100 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T244 3101-3124 Sentence denotes (1) (2) (3) (4) (5) (6)
T245 3125-3145 Sentence denotes OLS IV OLS IV OLS IV
T246 3146-3172 Sentence denotes All cities excluding Wuhan
T247 3173-3263 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T248 3264-3340 Sentence denotes Average # of new cases 0.873*** 1.142*** 1.692*** 2.135*** 0.768*** 1.077***
T249 3341-3406 Sentence denotes 1-week lag (0.00949) (0.0345) (0.0312) (0.0549) (0.0120) (0.0203)
T250 3407-3490 Sentence denotes Average # of new cases − 0.415*** − 0.824*** 0.860 − 6.050*** − 0.408*** − 0.796***
T251 3491-3555 Sentence denotes 2-week lag (0.00993) (0.0432) (2.131) (2.314) (0.00695) (0.0546)
T252 3556-3621 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T253 3622-3697 Sentence denotes Average # of new case 0.474*** 0.720*** 3.310*** 3.860*** 0.494*** 1.284***
T254 3698-3765 Sentence denotes Previous 14 days (0.0327) (0.143) (0.223) (0.114) (0.00859) (0.107)
T255 3766-3812 Sentence denotes Observations 12,768 12,768 4256 4256 8512 8512
T256 3813-3853 Sentence denotes Number of cities 304 304 304 304 304 304
T257 3854-3894 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T258 3895-3926 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T259 3927-3958 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T260 3959-4004 Sentence denotes All cities excluding cities in Hubei Province
T261 4005-4095 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T262 4096-4172 Sentence denotes Average # of new cases 0.725*** 1.113*** 1.050*** 1.483*** 0.620*** 0.903***
T263 4173-4234 Sentence denotes 1-week lag (0.141) (0.0802) (0.0828) (0.205) (0.166) (0.0349)
T264 4235-4315 Sentence denotes Average # of new cases − 0.394*** − 0.572*** 0.108 − 3.664 − 0.228*** − 0.341***
T265 4316-4376 Sentence denotes 2-week lag (0.0628) (0.107) (0.675) (2.481) (0.0456) (0.121)
T266 4377-4442 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T267 4443-4519 Sentence denotes Average # of new cases 0.357*** 0.631*** 1.899*** 2.376*** 0.493*** 0.745***
T268 4520-4585 Sentence denotes Previous 14 days (0.0479) (0.208) (0.250) (0.346) (0.122) (0.147)
T269 4586-4632 Sentence denotes Observations 12,096 12,096 4032 4032 8064 8064
T270 4633-4673 Sentence denotes Number of cities 288 288 288 288 288 288
T271 4674-4714 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T272 4715-4746 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T273 4747-4778 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T274 4779-4835 Sentence denotes The dependent variable is the number of daily new cases.
T275 4836-5048 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 5049-5364 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 5365-5464 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T278 5465-5560 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T279 5561-5714 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 5715-5836 Sentence denotes Their overstretched health care system exacerbates the concern over delayed reporting of confirmed cases in these cities.
T281 5837-5973 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 5974-6026 Sentence denotes The bottom panel of Table 3 reports these estimates.
T283 6027-6183 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 6184-6357 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 6358-6431 Sentence denotes We also find a similar pattern when comparing the estimates from model B.
T286 6433-6458 Sentence denotes Between-city transmission
T287 6459-6573 Sentence denotes People may contract the virus from interaction with the infected people who live in the same city or other cities.
T288 6574-6754 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 6755-7000 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 7001-7308 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 7309-7409 Sentence denotes For days before January 25, we use the average destination shares between January 10 and January 24.
T292 7410-7518 Sentence denotes For days on or after January 24, we use the average destination shares between January 25 and February 2314.
T293 7519-7662 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 7663-7849 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 7850-8025 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 8026-8226 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 8227-8522 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 8523-8580 Sentence denotes Table 4 Within- and between-city rransmission of COVID-19
T299 8581-8620 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T300 8621-8644 Sentence denotes (1) (2) (3) (4) (5) (6)
T301 8645-8665 Sentence denotes OLS IV OLS IV OLS IV
T302 8666-8756 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T303 8757-8791 Sentence denotes Average # of new cases, 1-week lag
T304 8792-8854 Sentence denotes Own city 0.862*** 1.387*** 0.939*** 2.456*** 0.786*** 1.127***
T305 8855-8905 Sentence denotes (0.0123) (0.122) (0.102) (0.638) (0.0196) (0.0686)
T306 8906-8968 Sentence denotes Other cities 0.00266 − 0.0248 0.0889 0.0412 − 0.00316 − 0.0212
T307 8969-9041 Sentence denotes wt. = inv. dist. (0.00172) (0.0208) (0.0714) (0.0787) (0.00227) (0.0137)
T308 9042-9096 Sentence denotes Wuhan − 0.0141 0.0303 − 0.879 − 0.957 − 0.00788 0.0236
T309 9097-9166 Sentence denotes wt. = inv. dist. (0.0115) (0.0318) (0.745) (0.955) (0.00782) (0.0200)
T310 9167-9239 Sentence denotes Wuhan 3.74e-05 0.00151*** 0.00462*** 0.00471*** − 0.00211*** − 0.00238**
T311 9240-9320 Sentence denotes wt. = pop. flow (0.000163) (0.000391) (0.000326) (0.000696) (4.01e-05) (0.00113)
T312 9321-9355 Sentence denotes Average # of new cases, 2-week lag
T313 9356-9419 Sentence denotes Own city − 0.425*** − 0.795*** 2.558 − 1.633 − 0.205*** − 0.171
T314 9420-9470 Sentence denotes (0.0318) (0.0643) (2.350) (2.951) (0.0491) (0.224)
T315 9471-9543 Sentence denotes Other cities − 0.00451** − 0.00766 − 0.361 − 0.0404 − 0.00912** − 0.0230
T316 9544-9615 Sentence denotes wt. = inv. dist. (0.00213) (0.00814) (0.371) (0.496) (0.00426) (0.0194)
T317 9616-9669 Sentence denotes Wuhan − 0.0410* 0.0438 3.053 3.031 − 0.0603 − 0.00725
T318 9670-9738 Sentence denotes wt. = inv. dist. (0.0240) (0.0286) (2.834) (3.559) (0.0384) (0.0137)
T319 9739-9808 Sentence denotes Wuhan 0.00261*** 0.00333*** 0.00711*** − 0.00632 0.00167** 0.00368***
T320 9809-9888 Sentence denotes wt. = pop. flow (0.000290) (0.000165) (0.00213) (0.00741) (0.000626) (0.000576)
T321 9889-9954 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T322 9955-10017 Sentence denotes Own city 0.425*** 1.195*** 1.564*** 2.992*** 0.615*** 1.243***
T323 10018-10067 Sentence denotes (0.0771) (0.160) (0.174) (0.892) (0.0544) (0.115)
T324 10068-10139 Sentence denotes Other cities − 0.00901 − 0.0958** 0.0414 0.0704 − 0.0286*** − 0.0821***
T325 10140-10211 Sentence denotes wt. = inv. dist. (0.00641) (0.0428) (0.0305) (0.0523) (0.0101) (0.0246)
T326 10212-10270 Sentence denotes Wuhan − 0.198* − 0.0687** − 0.309 − 0.608 − 0.234* − 0.144
T327 10271-10337 Sentence denotes wt. = inv. dist. (0.104) (0.0268) (0.251) (0.460) (0.121) (0.0994)
T328 10338-10406 Sentence denotes Wuhan 0.00770*** 0.00487*** 0.00779*** 0.00316 0.00829*** 0.00772***
T329 10407-10487 Sentence denotes wt. = pop. flow (0.000121) (0.000706) (0.000518) (0.00276) (0.000367) (0.000517)
T330 10488-10534 Sentence denotes Observations 12,768 12,768 4256 4256 8512 8512
T331 10535-10575 Sentence denotes Number of cities 304 304 304 304 304 304
T332 10576-10616 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T333 10617-10648 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T334 10649-10680 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T335 10681-10737 Sentence denotes The dependent variable is the number of daily new cases.
T336 10738-10958 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 10959-11266 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 11267-11366 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T339 11367-11462 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T340 11463-11556 Sentence denotes Table 5 Within- and between-city transmission of COVID-19, excluding cities in Hubei Province
T341 11557-11596 Sentence denotes Jan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29
T342 11597-11620 Sentence denotes (1) (2) (3) (4) (5) (6)
T343 11621-11641 Sentence denotes OLS IV OLS IV OLS IV
T344 11642-11732 Sentence denotes Model A: lagged variables are averages over the preceding first and second week separately
T345 11733-11767 Sentence denotes Average # of new cases, 1-week lag
T346 11768-11830 Sentence denotes Own city 0.656*** 1.117*** 0.792*** 1.194*** 0.567*** 0.899***
T347 11831-11880 Sentence denotes (0.153) (0.112) (0.0862) (0.302) (0.172) (0.0924)
T348 11881-11950 Sentence denotes Other cities 0.00114 − 0.00213 − 0.0160 − 0.0734 0.000221 − 0.00526**
T349 11951-12027 Sentence denotes wt. = inv. dist. (0.000741) (0.00367) (0.0212) (0.0803) (0.000626) (0.00244)
T350 12028-12083 Sentence denotes Wuhan − 0.000482 0.00420 0.104 0.233 5.89e-05 0.00769**
T351 12084-12156 Sentence denotes wt. = inv. dist. (0.00173) (0.00649) (0.128) (0.156) (0.00194) (0.00379)
T352 12157-12222 Sentence denotes Wuhan 0.00668*** 0.00616*** 0.00641*** 0.00375 − 0.000251 0.00390
T353 12223-12298 Sentence denotes wt. = pop. flow (0.00159) (0.00194) (0.00202) (0.00256) (0.00245) (0.00393)
T354 12299-12333 Sentence denotes Average # of new cases, 2-week lag
T355 12334-12398 Sentence denotes Own city − 0.350*** − 0.580*** 0.230 − 1.541 − 0.157** − 0.250**
T356 12399-12448 Sentence denotes (0.0667) (0.109) (0.572) (1.448) (0.0636) (0.119)
T357 12449-12513 Sentence denotes Other cities − 0.000869 0.00139 0.172 0.584 − 0.00266* − 0.00399
T358 12514-12586 Sentence denotes wt. = inv. dist. (0.00102) (0.00311) (0.122) (0.595) (0.00154) (0.00276)
T359 12587-12646 Sentence denotes Wuhan − 0.00461 0.000894 − 0.447 − 0.970 − 0.00456 0.00478*
T360 12647-12719 Sentence denotes wt. = inv. dist. (0.00304) (0.00592) (0.829) (0.808) (0.00368) (0.00280)
T361 12720-12785 Sentence denotes Wuhan 0.00803*** 0.00203 0.00973*** 0.00734 0.00759*** 0.00466***
T362 12786-12861 Sentence denotes wt. = pop. flow (0.00201) (0.00192) (0.00317) (0.00680) (0.00177) (0.00140)
T363 12862-12927 Sentence denotes Model B: lagged variables are averages over the preceding 2 weeks
T364 12928-12990 Sentence denotes Own city 0.242*** 0.654*** 1.407*** 1.876*** 0.406*** 0.614***
T365 12991-13039 Sentence denotes (0.0535) (0.195) (0.215) (0.376) (0.118) (0.129)
T366 13040-13106 Sentence denotes Other cities 0.000309 − 0.00315 0.00608 0.0194 − 0.00224 − 0.00568
T367 13107-13181 Sentence denotes wt. = inv. dist. (0.00142) (0.00745) (0.0188) (0.0300) (0.00204) (0.00529)
T368 13182-13246 Sentence denotes Wuhan − 0.0133** − 0.0167 − 0.0146 − 0.0362 − 0.0138** − 0.00847
T369 13247-13320 Sentence denotes wt. = inv. dist. (0.00535) (0.0140) (0.0902) (0.0741) (0.00563) (0.00787)
T370 13321-13385 Sentence denotes Wuhan 0.0153*** 0.0133*** 0.00826*** 0.00404 0.0132*** 0.0123***
T371 13386-13461 Sentence denotes wt. = pop. flow (0.00273) (0.00273) (0.00241) (0.00423) (0.00222) (0.00205)
T372 13462-13508 Sentence denotes Observations 12,096 12,096 4032 4032 8064 8064
T373 13509-13549 Sentence denotes Number of cities 288 288 288 288 288 288
T374 13550-13590 Sentence denotes Weather controls Yes Yes Yes Yes Yes Yes
T375 13591-13622 Sentence denotes City FE Yes Yes Yes Yes Yes Yes
T376 13623-13654 Sentence denotes Date FE Yes Yes Yes Yes Yes Yes
T377 13655-13711 Sentence denotes The dependent variable is the number of daily new cases.
T378 13712-13932 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 13933-14240 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 14241-14340 Sentence denotes Weather controls include contemporaneous weather variables in the preceding first and second weeks.
T381 14341-14436 Sentence denotes Standard errors in parentheses are clustered by provinces. *** p < 0.01, ** p < 0.05, * p < 0.1
T382 14437-14537 Sentence denotes As a robustness test, Table 5 reports the estimation results excluding the cities in Hubei province.
T383 14538-14744 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 14745-15013 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 15014-15118 Sentence denotes The time varying patterns in local transmissions are evident using the rolling window analysis (Fig. 5).
T386 15119-15285 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 15286-15432 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 15433-15658 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 15659-15813 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 15814-15977 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 15978-16158 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 16159-16287 Sentence denotes Overall, the spread of the virus has been effectively contained by mid February, particularly for cities outside Hubei province.
T393 16288-16372 Sentence denotes Fig. 5 Rolling window analysis of within- and between-city transmission of COVID-19.
T394 16373-16473 Sentence denotes This figure shows the estimated coefficients and 95% CIs from the instrumental variable regressions.
T395 16474-16543 Sentence denotes The specification is the same as the IV regression models in Table 4.
T396 16544-16639 Sentence denotes Each estimation sample contains 14 days with the starting date indicated on the horizontal axis
T397 16640-16793 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 16794-16800 Sentence denotes 2020).
T399 16801-17115 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 17116-17237 Sentence denotes Intuitively, it can be interpreted as measuring the expected number of new cases that are generated by one existing case.
T401 17238-17305 Sentence denotes It is of interest to note that our estimates are within this range.
T402 17306-17480 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 17481-17739 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 17740-17965 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 17966-18902 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 18903-19091 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 19092-19253 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 19254-19394 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 19395-19701 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 19702-19947 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 19948-20041 Sentence denotes Note that while the effect is statistically significant, it should be interpreted in context.
T412 20042-20144 Sentence denotes It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16.
T413 20145-20240 Sentence denotes If all had gone to one city, this would have directly generated about 171 cases within 2 weeks.
T414 20241-20519 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 20520-20612 Sentence denotes A city may also be affected by infections in nearby cities apart from spillovers from Wuhan.
T416 20613-20840 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 20841-21048 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 21049-21214 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 21216-21253 Sentence denotes Social and economic mediating factors
T420 21254-21382 Sentence denotes We also investigate the mediating impacts of some socioeconomic and environmental characteristics on the transmission rates (3).
T421 21383-21542 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 21543-21771 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 21772-22022 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 22023-22080 Sentence denotes We also include a measure of population flows from Wuhan.
T425 22081-22142 Sentence denotes Table 6 reports the estimation results of the IV regressions.
T426 22143-22455 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 22456-22530 Sentence denotes Table 6 Social and economic factors mediating the transmission of COVID-19
T428 22531-22546 Sentence denotes (1) (2) (3) (4)
T429 22547-22572 Sentence denotes Jan 19–Feb 1 Feb 2–Feb 29
T430 22573-22582 Sentence denotes IV Coeff.
T431 22583-22592 Sentence denotes IV Coeff.
T432 22593-22633 Sentence denotes Average # of new cases, previous 14 days
T433 22634-22659 Sentence denotes Own city − 0.251 0.672***
T434 22660-22675 Sentence denotes (0.977) (0.219)
T435 22676-22732 Sentence denotes × population density 0.000164 − 0.000202** + 495 per km2
T436 22733-22754 Sentence denotes (0.000171) (8.91e-05)
T437 22755-22801 Sentence denotes × per capita GDP 0.150*** − 66, 667 RMB 0.0102
T438 22802-22819 Sentence denotes (0.0422) (0.0196)
T439 22820-22860 Sentence denotes × # of doctors − 0.108* + 92, 593 0.0179
T440 22861-22878 Sentence denotes (0.0622) (0.0236)
T441 22879-22920 Sentence denotes × temperature 0.0849* − 11.78∘C − 0.00945
T442 22921-22938 Sentence denotes (0.0438) (0.0126)
T443 22939-22965 Sentence denotes × wind speed − 0.109 0.128
T444 22966-22981 Sentence denotes (0.131) (0.114)
T445 22982-23031 Sentence denotes × precipitation 0.965* − 1.04 mm 0.433* − 2.31 mm
T446 23032-23047 Sentence denotes (0.555) (0.229)
T447 23048-23090 Sentence denotes × adverse weather 0.0846 − 0.614*** + 163%
T448 23091-23106 Sentence denotes (0.801) (0.208)
T449 23107-23136 Sentence denotes Other cities 0.0356 − 0.00429
T450 23137-23175 Sentence denotes wt. = inv. distance (0.0375) (0.00343)
T451 23176-23205 Sentence denotes Other cities 0.00222 0.000192
T452 23206-23251 Sentence denotes wt. = inv. density ratio (0.00147) (0.000891)
T453 23252-23280 Sentence denotes Other cities 0.00232 0.00107
T454 23281-23332 Sentence denotes wt. = inv. per capita GDP ratio (0.00497) (0.00165)
T455 23333-23356 Sentence denotes Wuhan − 0.165 − 0.00377
T456 23357-23394 Sentence denotes wt. = inv. distance (0.150) (0.00981)
T457 23395-23421 Sentence denotes Wuhan − 0.00336 − 0.000849
T458 23422-23466 Sentence denotes wt. = inv. density ratio (0.00435) (0.00111)
T459 23467-23489 Sentence denotes Wuhan − 0.440 − 0.0696
T460 23490-23538 Sentence denotes wt. = inv. per capita GDP ratio (0.318) (0.0699)
T461 23539-23565 Sentence denotes Wuhan 0.00729*** 0.0125***
T462 23566-23607 Sentence denotes wt. = population flow (0.00202) (0.00187)
T463 23608-23630 Sentence denotes Observations 4032 8064
T464 23631-23655 Sentence denotes Number of cities 288 288
T465 23656-23680 Sentence denotes Weather controls Yes Yes
T466 23681-23696 Sentence denotes City FE Yes Yes
T467 23697-23712 Sentence denotes Date FE Yes Yes
T468 23713-23779 Sentence denotes The dependent variable is the number of daily new confirmed cases.
T469 23780-23825 Sentence denotes The sample excludes cities in Hubei province.
T470 23826-24077 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 24078-24255 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 24256-24569 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 24570-24669 Sentence denotes Additional instrumental variables are constructed by interacting them with the mediating variables.
T474 24670-24751 Sentence denotes Weather controls include these variables in the preceding first and second weeks.
T475 24752-24809 Sentence denotes Standard errors in parentheses are clustered by provinces
T476 24810-24846 Sentence denotes *** p < 0.01, ** p < 0.05, * p < 0.1
T477 24847-25021 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 25022-25117 Sentence denotes One standard deviation increase in the number of doctors reduces the transmission rate by 0.12.
T479 25118-25277 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 25278-25424 Sentence denotes In the second sub-sample, these effects become insignificant probably because public health measures and inter-city resource sharing take effects.
T481 25425-25527 Sentence denotes In fact, cities with higher population density have lower transmission rates in the second sub-sample.
T482 25528-25657 Sentence denotes Regarding the environmental factors, we notice different significant mediating variables across the first and second sub-samples.
T483 25658-25756 Sentence denotes The transmission rates are lower with adverse weather conditions, lower temperature, or less rain.
T484 25757-25813 Sentence denotes Further research is needed to identify clear mechanisms.
T485 25814-25987 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 25988-26138 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 26139-26242 Sentence denotes Nevertheless, we do not find much evidence on between-city transmissions among cities other than Wuhan.