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LitCovid-PD-FMA-UBERON

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T10","span":{"begin":8847,"end":8851},"obj":"Body_part"},{"id":"T11","span":{"begin":10202,"end":10206},"obj":"Body_part"},{"id":"T12","span":{"begin":13271,"end":13275},"obj":"Body_part"}],"attributes":[{"id":"A10","pred":"fma_id","subj":"T10","obj":"http://purl.org/sig/ont/fma/fma12520"},{"id":"A11","pred":"fma_id","subj":"T11","obj":"http://purl.org/sig/ont/fma/fma12520"},{"id":"A12","pred":"fma_id","subj":"T12","obj":"http://purl.org/sig/ont/fma/fma25056"}],"text":"Between-city transmission\nPeople may contract the virus from interaction with the infected people who live in the same city or other cities. 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. 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. 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). For days before January 25, we use the average destination shares between January 10 and January 24. For days on or after January 24, we use the average destination shares between January 25 and February 2314.\nTable 4 reports the estimates from IV regressions of Eq. 1, and Table 5 reports the results from the same regressions excluding Hubei province. 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. 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. 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. 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.\nTable 4 Within- and between-city rransmission of COVID-19\nJan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29\n(1) (2) (3) (4) (5) (6)\nOLS IV OLS IV OLS IV\nModel A: lagged variables are averages over the preceding first and second week separately\nAverage # of new cases, 1-week lag\nOwn city 0.862*** 1.387*** 0.939*** 2.456*** 0.786*** 1.127***\n(0.0123) (0.122) (0.102) (0.638) (0.0196) (0.0686)\nOther cities 0.00266 − 0.0248 0.0889 0.0412 − 0.00316 − 0.0212\nwt. = inv. dist. (0.00172) (0.0208) (0.0714) (0.0787) (0.00227) (0.0137)\nWuhan − 0.0141 0.0303 − 0.879 − 0.957 − 0.00788 0.0236\nwt. = inv. dist. (0.0115) (0.0318) (0.745) (0.955) (0.00782) (0.0200)\nWuhan 3.74e-05 0.00151*** 0.00462*** 0.00471*** − 0.00211*** − 0.00238**\nwt. = pop. flow (0.000163) (0.000391) (0.000326) (0.000696) (4.01e-05) (0.00113)\nAverage # of new cases, 2-week lag\nOwn city − 0.425*** − 0.795*** 2.558 − 1.633 − 0.205*** − 0.171\n(0.0318) (0.0643) (2.350) (2.951) (0.0491) (0.224)\nOther cities − 0.00451** − 0.00766 − 0.361 − 0.0404 − 0.00912** − 0.0230\nwt. = inv. dist. (0.00213) (0.00814) (0.371) (0.496) (0.00426) (0.0194)\nWuhan − 0.0410* 0.0438 3.053 3.031 − 0.0603 − 0.00725\nwt. = inv. dist. (0.0240) (0.0286) (2.834) (3.559) (0.0384) (0.0137)\nWuhan 0.00261*** 0.00333*** 0.00711*** − 0.00632 0.00167** 0.00368***\nwt. = pop. flow (0.000290) (0.000165) (0.00213) (0.00741) (0.000626) (0.000576)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.425*** 1.195*** 1.564*** 2.992*** 0.615*** 1.243***\n(0.0771) (0.160) (0.174) (0.892) (0.0544) (0.115)\nOther cities − 0.00901 − 0.0958** 0.0414 0.0704 − 0.0286*** − 0.0821***\nwt. = inv. dist. (0.00641) (0.0428) (0.0305) (0.0523) (0.0101) (0.0246)\nWuhan − 0.198* − 0.0687** − 0.309 − 0.608 − 0.234* − 0.144\nwt. = inv. dist. (0.104) (0.0268) (0.251) (0.460) (0.121) (0.0994)\nWuhan 0.00770*** 0.00487*** 0.00779*** 0.00316 0.00829*** 0.00772***\nwt. = pop. flow (0.000121) (0.000706) (0.000518) (0.00276) (0.000367) (0.000517)\nObservations 12,768 12,768 4256 4256 8512 8512\nNumber of cities 304 304 304 304 304 304\nWeather controls Yes Yes Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes Yes Yes\nThe dependent variable is the number of daily new cases. 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). 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. Weather controls include contemporaneous weather variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1\nTable 5 Within- and between-city transmission of COVID-19, excluding cities in Hubei Province\nJan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29\n(1) (2) (3) (4) (5) (6)\nOLS IV OLS IV OLS IV\nModel A: lagged variables are averages over the preceding first and second week separately\nAverage # of new cases, 1-week lag\nOwn city 0.656*** 1.117*** 0.792*** 1.194*** 0.567*** 0.899***\n(0.153) (0.112) (0.0862) (0.302) (0.172) (0.0924)\nOther cities 0.00114 − 0.00213 − 0.0160 − 0.0734 0.000221 − 0.00526**\nwt. = inv. dist. (0.000741) (0.00367) (0.0212) (0.0803) (0.000626) (0.00244)\nWuhan − 0.000482 0.00420 0.104 0.233 5.89e-05 0.00769**\nwt. = inv. dist. (0.00173) (0.00649) (0.128) (0.156) (0.00194) (0.00379)\nWuhan 0.00668*** 0.00616*** 0.00641*** 0.00375 − 0.000251 0.00390\nwt. = pop. flow (0.00159) (0.00194) (0.00202) (0.00256) (0.00245) (0.00393)\nAverage # of new cases, 2-week lag\nOwn city − 0.350*** − 0.580*** 0.230 − 1.541 − 0.157** − 0.250**\n(0.0667) (0.109) (0.572) (1.448) (0.0636) (0.119)\nOther cities − 0.000869 0.00139 0.172 0.584 − 0.00266* − 0.00399\nwt. = inv. dist. (0.00102) (0.00311) (0.122) (0.595) (0.00154) (0.00276)\nWuhan − 0.00461 0.000894 − 0.447 − 0.970 − 0.00456 0.00478*\nwt. = inv. dist. (0.00304) (0.00592) (0.829) (0.808) (0.00368) (0.00280)\nWuhan 0.00803*** 0.00203 0.00973*** 0.00734 0.00759*** 0.00466***\nwt. = pop. flow (0.00201) (0.00192) (0.00317) (0.00680) (0.00177) (0.00140)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.242*** 0.654*** 1.407*** 1.876*** 0.406*** 0.614***\n(0.0535) (0.195) (0.215) (0.376) (0.118) (0.129)\nOther cities 0.000309 − 0.00315 0.00608 0.0194 − 0.00224 − 0.00568\nwt. = inv. dist. (0.00142) (0.00745) (0.0188) (0.0300) (0.00204) (0.00529)\nWuhan − 0.0133** − 0.0167 − 0.0146 − 0.0362 − 0.0138** − 0.00847\nwt. = inv. dist. (0.00535) (0.0140) (0.0902) (0.0741) (0.00563) (0.00787)\nWuhan 0.0153*** 0.0133*** 0.00826*** 0.00404 0.0132*** 0.0123***\nwt. = pop. flow (0.00273) (0.00273) (0.00241) (0.00423) (0.00222) (0.00205)\nObservations 12,096 12,096 4032 4032 8064 8064\nNumber of cities 288 288 288 288 288 288\nWeather controls Yes Yes Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes Yes Yes\nThe dependent variable is the number of daily new cases. 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). 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. Weather controls include contemporaneous weather variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1\nAs a robustness test, Table 5 reports the estimation results excluding the cities in Hubei province. 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. 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).\nThe time varying patterns in local transmissions are evident using the rolling window analysis (Fig. 5). 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. 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. 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. 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. 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). 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. Overall, the spread of the virus has been effectively contained by mid February, particularly for cities outside Hubei province.\nFig. 5 Rolling window analysis of within- and between-city transmission of COVID-19. This figure shows the estimated coefficients and 95% CIs from the instrumental variable regressions. The specification is the same as the IV regression models in Table 4. Each estimation sample contains 14 days with the starting date indicated on the horizontal axis\nIn 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. 2020). 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). Intuitively, it can be interpreted as measuring the expected number of new cases that are generated by one existing case. It is of interest to note that our estimates are within this range. 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). 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.\nWhile 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. 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. 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.\nRegarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). 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. 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. 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. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. 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.\nA city may also be affected by infections in nearby cities apart from spillovers from Wuhan. 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. 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. 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."}

LitCovid-PD-MONDO

LitCovid-PD-CLO

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T5","span":{"begin":10266,"end":10278},"obj":"http://purl.obolibrary.org/obo/GO_0000003"},{"id":"T6","span":{"begin":10629,"end":10637},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T7","span":{"begin":11227,"end":11235},"obj":"http://purl.obolibrary.org/obo/GO_0007610"},{"id":"T8","span":{"begin":11323,"end":11335},"obj":"http://purl.obolibrary.org/obo/GO_0000003"}],"text":"Between-city transmission\nPeople may contract the virus from interaction with the infected people who live in the same city or other cities. 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. 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. 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). For days before January 25, we use the average destination shares between January 10 and January 24. For days on or after January 24, we use the average destination shares between January 25 and February 2314.\nTable 4 reports the estimates from IV regressions of Eq. 1, and Table 5 reports the results from the same regressions excluding Hubei province. 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. 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. 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. 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.\nTable 4 Within- and between-city rransmission of COVID-19\nJan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29\n(1) (2) (3) (4) (5) (6)\nOLS IV OLS IV OLS IV\nModel A: lagged variables are averages over the preceding first and second week separately\nAverage # of new cases, 1-week lag\nOwn city 0.862*** 1.387*** 0.939*** 2.456*** 0.786*** 1.127***\n(0.0123) (0.122) (0.102) (0.638) (0.0196) (0.0686)\nOther cities 0.00266 − 0.0248 0.0889 0.0412 − 0.00316 − 0.0212\nwt. = inv. dist. (0.00172) (0.0208) (0.0714) (0.0787) (0.00227) (0.0137)\nWuhan − 0.0141 0.0303 − 0.879 − 0.957 − 0.00788 0.0236\nwt. = inv. dist. (0.0115) (0.0318) (0.745) (0.955) (0.00782) (0.0200)\nWuhan 3.74e-05 0.00151*** 0.00462*** 0.00471*** − 0.00211*** − 0.00238**\nwt. = pop. flow (0.000163) (0.000391) (0.000326) (0.000696) (4.01e-05) (0.00113)\nAverage # of new cases, 2-week lag\nOwn city − 0.425*** − 0.795*** 2.558 − 1.633 − 0.205*** − 0.171\n(0.0318) (0.0643) (2.350) (2.951) (0.0491) (0.224)\nOther cities − 0.00451** − 0.00766 − 0.361 − 0.0404 − 0.00912** − 0.0230\nwt. = inv. dist. (0.00213) (0.00814) (0.371) (0.496) (0.00426) (0.0194)\nWuhan − 0.0410* 0.0438 3.053 3.031 − 0.0603 − 0.00725\nwt. = inv. dist. (0.0240) (0.0286) (2.834) (3.559) (0.0384) (0.0137)\nWuhan 0.00261*** 0.00333*** 0.00711*** − 0.00632 0.00167** 0.00368***\nwt. = pop. flow (0.000290) (0.000165) (0.00213) (0.00741) (0.000626) (0.000576)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.425*** 1.195*** 1.564*** 2.992*** 0.615*** 1.243***\n(0.0771) (0.160) (0.174) (0.892) (0.0544) (0.115)\nOther cities − 0.00901 − 0.0958** 0.0414 0.0704 − 0.0286*** − 0.0821***\nwt. = inv. dist. (0.00641) (0.0428) (0.0305) (0.0523) (0.0101) (0.0246)\nWuhan − 0.198* − 0.0687** − 0.309 − 0.608 − 0.234* − 0.144\nwt. = inv. dist. (0.104) (0.0268) (0.251) (0.460) (0.121) (0.0994)\nWuhan 0.00770*** 0.00487*** 0.00779*** 0.00316 0.00829*** 0.00772***\nwt. = pop. flow (0.000121) (0.000706) (0.000518) (0.00276) (0.000367) (0.000517)\nObservations 12,768 12,768 4256 4256 8512 8512\nNumber of cities 304 304 304 304 304 304\nWeather controls Yes Yes Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes Yes Yes\nThe dependent variable is the number of daily new cases. 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). 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. Weather controls include contemporaneous weather variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1\nTable 5 Within- and between-city transmission of COVID-19, excluding cities in Hubei Province\nJan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29\n(1) (2) (3) (4) (5) (6)\nOLS IV OLS IV OLS IV\nModel A: lagged variables are averages over the preceding first and second week separately\nAverage # of new cases, 1-week lag\nOwn city 0.656*** 1.117*** 0.792*** 1.194*** 0.567*** 0.899***\n(0.153) (0.112) (0.0862) (0.302) (0.172) (0.0924)\nOther cities 0.00114 − 0.00213 − 0.0160 − 0.0734 0.000221 − 0.00526**\nwt. = inv. dist. (0.000741) (0.00367) (0.0212) (0.0803) (0.000626) (0.00244)\nWuhan − 0.000482 0.00420 0.104 0.233 5.89e-05 0.00769**\nwt. = inv. dist. (0.00173) (0.00649) (0.128) (0.156) (0.00194) (0.00379)\nWuhan 0.00668*** 0.00616*** 0.00641*** 0.00375 − 0.000251 0.00390\nwt. = pop. flow (0.00159) (0.00194) (0.00202) (0.00256) (0.00245) (0.00393)\nAverage # of new cases, 2-week lag\nOwn city − 0.350*** − 0.580*** 0.230 − 1.541 − 0.157** − 0.250**\n(0.0667) (0.109) (0.572) (1.448) (0.0636) (0.119)\nOther cities − 0.000869 0.00139 0.172 0.584 − 0.00266* − 0.00399\nwt. = inv. dist. (0.00102) (0.00311) (0.122) (0.595) (0.00154) (0.00276)\nWuhan − 0.00461 0.000894 − 0.447 − 0.970 − 0.00456 0.00478*\nwt. = inv. dist. (0.00304) (0.00592) (0.829) (0.808) (0.00368) (0.00280)\nWuhan 0.00803*** 0.00203 0.00973*** 0.00734 0.00759*** 0.00466***\nwt. = pop. flow (0.00201) (0.00192) (0.00317) (0.00680) (0.00177) (0.00140)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.242*** 0.654*** 1.407*** 1.876*** 0.406*** 0.614***\n(0.0535) (0.195) (0.215) (0.376) (0.118) (0.129)\nOther cities 0.000309 − 0.00315 0.00608 0.0194 − 0.00224 − 0.00568\nwt. = inv. dist. (0.00142) (0.00745) (0.0188) (0.0300) (0.00204) (0.00529)\nWuhan − 0.0133** − 0.0167 − 0.0146 − 0.0362 − 0.0138** − 0.00847\nwt. = inv. dist. (0.00535) (0.0140) (0.0902) (0.0741) (0.00563) (0.00787)\nWuhan 0.0153*** 0.0133*** 0.00826*** 0.00404 0.0132*** 0.0123***\nwt. = pop. flow (0.00273) (0.00273) (0.00241) (0.00423) (0.00222) (0.00205)\nObservations 12,096 12,096 4032 4032 8064 8064\nNumber of cities 288 288 288 288 288 288\nWeather controls Yes Yes Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes Yes Yes\nThe dependent variable is the number of daily new cases. 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). 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. Weather controls include contemporaneous weather variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1\nAs a robustness test, Table 5 reports the estimation results excluding the cities in Hubei province. 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. 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).\nThe time varying patterns in local transmissions are evident using the rolling window analysis (Fig. 5). 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. 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. 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. 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. 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). 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. Overall, the spread of the virus has been effectively contained by mid February, particularly for cities outside Hubei province.\nFig. 5 Rolling window analysis of within- and between-city transmission of COVID-19. This figure shows the estimated coefficients and 95% CIs from the instrumental variable regressions. The specification is the same as the IV regression models in Table 4. Each estimation sample contains 14 days with the starting date indicated on the horizontal axis\nIn 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. 2020). 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). Intuitively, it can be interpreted as measuring the expected number of new cases that are generated by one existing case. It is of interest to note that our estimates are within this range. 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). 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.\nWhile 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. 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. 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.\nRegarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). 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. 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. 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. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. 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.\nA city may also be affected by infections in nearby cities apart from spillovers from Wuhan. 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. 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. 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."}

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T7","span":{"begin":392,"end":411},"obj":"Phenotype"},{"id":"T7","span":{"begin":392,"end":411},"obj":"Phenotype"}],"attributes":[{"id":"A7","pred":"hp_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/HP_0008763"},{"id":"A7","pred":"hp_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/HP_0008763"}],"text":"Between-city transmission\nPeople may contract the virus from interaction with the infected people who live in the same city or other cities. 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. 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. 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). For days before January 25, we use the average destination shares between January 10 and January 24. For days on or after January 24, we use the average destination shares between January 25 and February 2314.\nTable 4 reports the estimates from IV regressions of Eq. 1, and Table 5 reports the results from the same regressions excluding Hubei province. 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. 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. 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. 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.\nTable 4 Within- and between-city rransmission of COVID-19\nJan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29\n(1) (2) (3) (4) (5) (6)\nOLS IV OLS IV OLS IV\nModel A: lagged variables are averages over the preceding first and second week separately\nAverage # of new cases, 1-week lag\nOwn city 0.862*** 1.387*** 0.939*** 2.456*** 0.786*** 1.127***\n(0.0123) (0.122) (0.102) (0.638) (0.0196) (0.0686)\nOther cities 0.00266 − 0.0248 0.0889 0.0412 − 0.00316 − 0.0212\nwt. = inv. dist. (0.00172) (0.0208) (0.0714) (0.0787) (0.00227) (0.0137)\nWuhan − 0.0141 0.0303 − 0.879 − 0.957 − 0.00788 0.0236\nwt. = inv. dist. (0.0115) (0.0318) (0.745) (0.955) (0.00782) (0.0200)\nWuhan 3.74e-05 0.00151*** 0.00462*** 0.00471*** − 0.00211*** − 0.00238**\nwt. = pop. flow (0.000163) (0.000391) (0.000326) (0.000696) (4.01e-05) (0.00113)\nAverage # of new cases, 2-week lag\nOwn city − 0.425*** − 0.795*** 2.558 − 1.633 − 0.205*** − 0.171\n(0.0318) (0.0643) (2.350) (2.951) (0.0491) (0.224)\nOther cities − 0.00451** − 0.00766 − 0.361 − 0.0404 − 0.00912** − 0.0230\nwt. = inv. dist. (0.00213) (0.00814) (0.371) (0.496) (0.00426) (0.0194)\nWuhan − 0.0410* 0.0438 3.053 3.031 − 0.0603 − 0.00725\nwt. = inv. dist. (0.0240) (0.0286) (2.834) (3.559) (0.0384) (0.0137)\nWuhan 0.00261*** 0.00333*** 0.00711*** − 0.00632 0.00167** 0.00368***\nwt. = pop. flow (0.000290) (0.000165) (0.00213) (0.00741) (0.000626) (0.000576)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.425*** 1.195*** 1.564*** 2.992*** 0.615*** 1.243***\n(0.0771) (0.160) (0.174) (0.892) (0.0544) (0.115)\nOther cities − 0.00901 − 0.0958** 0.0414 0.0704 − 0.0286*** − 0.0821***\nwt. = inv. dist. (0.00641) (0.0428) (0.0305) (0.0523) (0.0101) (0.0246)\nWuhan − 0.198* − 0.0687** − 0.309 − 0.608 − 0.234* − 0.144\nwt. = inv. dist. (0.104) (0.0268) (0.251) (0.460) (0.121) (0.0994)\nWuhan 0.00770*** 0.00487*** 0.00779*** 0.00316 0.00829*** 0.00772***\nwt. = pop. flow (0.000121) (0.000706) (0.000518) (0.00276) (0.000367) (0.000517)\nObservations 12,768 12,768 4256 4256 8512 8512\nNumber of cities 304 304 304 304 304 304\nWeather controls Yes Yes Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes Yes Yes\nThe dependent variable is the number of daily new cases. 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). 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. Weather controls include contemporaneous weather variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1\nTable 5 Within- and between-city transmission of COVID-19, excluding cities in Hubei Province\nJan 19–Feb 29 Jan 19–Feb 1 Feb 2–Feb 29\n(1) (2) (3) (4) (5) (6)\nOLS IV OLS IV OLS IV\nModel A: lagged variables are averages over the preceding first and second week separately\nAverage # of new cases, 1-week lag\nOwn city 0.656*** 1.117*** 0.792*** 1.194*** 0.567*** 0.899***\n(0.153) (0.112) (0.0862) (0.302) (0.172) (0.0924)\nOther cities 0.00114 − 0.00213 − 0.0160 − 0.0734 0.000221 − 0.00526**\nwt. = inv. dist. (0.000741) (0.00367) (0.0212) (0.0803) (0.000626) (0.00244)\nWuhan − 0.000482 0.00420 0.104 0.233 5.89e-05 0.00769**\nwt. = inv. dist. (0.00173) (0.00649) (0.128) (0.156) (0.00194) (0.00379)\nWuhan 0.00668*** 0.00616*** 0.00641*** 0.00375 − 0.000251 0.00390\nwt. = pop. flow (0.00159) (0.00194) (0.00202) (0.00256) (0.00245) (0.00393)\nAverage # of new cases, 2-week lag\nOwn city − 0.350*** − 0.580*** 0.230 − 1.541 − 0.157** − 0.250**\n(0.0667) (0.109) (0.572) (1.448) (0.0636) (0.119)\nOther cities − 0.000869 0.00139 0.172 0.584 − 0.00266* − 0.00399\nwt. = inv. dist. (0.00102) (0.00311) (0.122) (0.595) (0.00154) (0.00276)\nWuhan − 0.00461 0.000894 − 0.447 − 0.970 − 0.00456 0.00478*\nwt. = inv. dist. (0.00304) (0.00592) (0.829) (0.808) (0.00368) (0.00280)\nWuhan 0.00803*** 0.00203 0.00973*** 0.00734 0.00759*** 0.00466***\nwt. = pop. flow (0.00201) (0.00192) (0.00317) (0.00680) (0.00177) (0.00140)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.242*** 0.654*** 1.407*** 1.876*** 0.406*** 0.614***\n(0.0535) (0.195) (0.215) (0.376) (0.118) (0.129)\nOther cities 0.000309 − 0.00315 0.00608 0.0194 − 0.00224 − 0.00568\nwt. = inv. dist. (0.00142) (0.00745) (0.0188) (0.0300) (0.00204) (0.00529)\nWuhan − 0.0133** − 0.0167 − 0.0146 − 0.0362 − 0.0138** − 0.00847\nwt. = inv. dist. (0.00535) (0.0140) (0.0902) (0.0741) (0.00563) (0.00787)\nWuhan 0.0153*** 0.0133*** 0.00826*** 0.00404 0.0132*** 0.0123***\nwt. = pop. flow (0.00273) (0.00273) (0.00241) (0.00423) (0.00222) (0.00205)\nObservations 12,096 12,096 4032 4032 8064 8064\nNumber of cities 288 288 288 288 288 288\nWeather controls Yes Yes Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes Yes Yes\nThe dependent variable is the number of daily new cases. 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). 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. Weather controls include contemporaneous weather variables in the preceding first and second weeks. Standard errors in parentheses are clustered by provinces. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1\nAs a robustness test, Table 5 reports the estimation results excluding the cities in Hubei province. 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. 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).\nThe time varying patterns in local transmissions are evident using the rolling window analysis (Fig. 5). 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. 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. 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. 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. 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). 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. Overall, the spread of the virus has been effectively contained by mid February, particularly for cities outside Hubei province.\nFig. 5 Rolling window analysis of within- and between-city transmission of COVID-19. This figure shows the estimated coefficients and 95% CIs from the instrumental variable regressions. The specification is the same as the IV regression models in Table 4. Each estimation sample contains 14 days with the starting date indicated on the horizontal axis\nIn 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. 2020). 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). Intuitively, it can be interpreted as measuring the expected number of new cases that are generated by one existing case. It is of interest to note that our estimates are within this range. 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). 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.\nWhile 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. 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. 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.\nRegarding the between-city transmission from Wuhan, we observe that the population flow better explains the contagion effect than geographic proximity (Table 4). 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. 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. 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. Note that while the effect is statistically significant, it should be interpreted in context. It was estimated that 15,000,000 people would travel out of Wuhan during the Lunar New Year holiday16. If all had gone to one city, this would have directly generated about 171 cases within 2 weeks. 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.\nA city may also be affected by infections in nearby cities apart from spillovers from Wuhan. 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. 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. 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."}

LitCovid-sentences

LitCovid-PubTator