PMC:7210464 / 35508-42425
Annnotations
LitCovid-PD-MONDO
{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T84","span":{"begin":696,"end":706},"obj":"Disease"},{"id":"T85","span":{"begin":1053,"end":1061},"obj":"Disease"},{"id":"T86","span":{"begin":1456,"end":1459},"obj":"Disease"},{"id":"T87","span":{"begin":1584,"end":1587},"obj":"Disease"},{"id":"T88","span":{"begin":2031,"end":2034},"obj":"Disease"},{"id":"T89","span":{"begin":2157,"end":2160},"obj":"Disease"},{"id":"T90","span":{"begin":2627,"end":2630},"obj":"Disease"},{"id":"T91","span":{"begin":2758,"end":2761},"obj":"Disease"},{"id":"T92","span":{"begin":3993,"end":4001},"obj":"Disease"},{"id":"T93","span":{"begin":4438,"end":4441},"obj":"Disease"},{"id":"T94","span":{"begin":4571,"end":4574},"obj":"Disease"},{"id":"T95","span":{"begin":5001,"end":5004},"obj":"Disease"},{"id":"T96","span":{"begin":5134,"end":5137},"obj":"Disease"},{"id":"T97","span":{"begin":5594,"end":5597},"obj":"Disease"},{"id":"T98","span":{"begin":5734,"end":5737},"obj":"Disease"}],"attributes":[{"id":"A84","pred":"mondo_id","subj":"T84","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A85","pred":"mondo_id","subj":"T85","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A86","pred":"mondo_id","subj":"T86","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A87","pred":"mondo_id","subj":"T87","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A88","pred":"mondo_id","subj":"T88","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A89","pred":"mondo_id","subj":"T89","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A90","pred":"mondo_id","subj":"T90","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A91","pred":"mondo_id","subj":"T91","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A92","pred":"mondo_id","subj":"T92","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A93","pred":"mondo_id","subj":"T93","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A94","pred":"mondo_id","subj":"T94","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A95","pred":"mondo_id","subj":"T95","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A96","pred":"mondo_id","subj":"T96","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A97","pred":"mondo_id","subj":"T97","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A98","pred":"mondo_id","subj":"T98","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"}],"text":"Table 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"}
LitCovid-PD-CLO
{"project":"LitCovid-PD-CLO","denotations":[{"id":"T168","span":{"begin":1111,"end":1116},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T169","span":{"begin":1153,"end":1154},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T170","span":{"begin":1269,"end":1272},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T171","span":{"begin":1833,"end":1836},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T172","span":{"begin":2376,"end":2377},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T173","span":{"begin":3388,"end":3389},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T174","span":{"begin":3436,"end":3437},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T175","span":{"begin":3702,"end":3714},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"},{"id":"T176","span":{"begin":4087,"end":4092},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T177","span":{"begin":4129,"end":4130},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T178","span":{"begin":4245,"end":4248},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T179","span":{"begin":4811,"end":4814},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T180","span":{"begin":5349,"end":5350},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T181","span":{"begin":6362,"end":6363},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T182","span":{"begin":6410,"end":6411},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T183","span":{"begin":6676,"end":6688},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"}],"text":"Table 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"}
LitCovid-sentences
{"project":"LitCovid-sentences","denotations":[{"id":"T293","span":{"begin":0,"end":143},"obj":"Sentence"},{"id":"T294","span":{"begin":144,"end":330},"obj":"Sentence"},{"id":"T295","span":{"begin":331,"end":506},"obj":"Sentence"},{"id":"T296","span":{"begin":507,"end":707},"obj":"Sentence"},{"id":"T297","span":{"begin":708,"end":1003},"obj":"Sentence"},{"id":"T298","span":{"begin":1004,"end":1061},"obj":"Sentence"},{"id":"T299","span":{"begin":1062,"end":1101},"obj":"Sentence"},{"id":"T300","span":{"begin":1102,"end":1125},"obj":"Sentence"},{"id":"T301","span":{"begin":1126,"end":1146},"obj":"Sentence"},{"id":"T302","span":{"begin":1147,"end":1237},"obj":"Sentence"},{"id":"T303","span":{"begin":1238,"end":1272},"obj":"Sentence"},{"id":"T304","span":{"begin":1273,"end":1335},"obj":"Sentence"},{"id":"T305","span":{"begin":1336,"end":1386},"obj":"Sentence"},{"id":"T306","span":{"begin":1387,"end":1449},"obj":"Sentence"},{"id":"T307","span":{"begin":1450,"end":1522},"obj":"Sentence"},{"id":"T308","span":{"begin":1523,"end":1577},"obj":"Sentence"},{"id":"T309","span":{"begin":1578,"end":1647},"obj":"Sentence"},{"id":"T310","span":{"begin":1648,"end":1720},"obj":"Sentence"},{"id":"T311","span":{"begin":1721,"end":1801},"obj":"Sentence"},{"id":"T312","span":{"begin":1802,"end":1836},"obj":"Sentence"},{"id":"T313","span":{"begin":1837,"end":1900},"obj":"Sentence"},{"id":"T314","span":{"begin":1901,"end":1951},"obj":"Sentence"},{"id":"T315","span":{"begin":1952,"end":2024},"obj":"Sentence"},{"id":"T316","span":{"begin":2025,"end":2096},"obj":"Sentence"},{"id":"T317","span":{"begin":2097,"end":2150},"obj":"Sentence"},{"id":"T318","span":{"begin":2151,"end":2219},"obj":"Sentence"},{"id":"T319","span":{"begin":2220,"end":2289},"obj":"Sentence"},{"id":"T320","span":{"begin":2290,"end":2369},"obj":"Sentence"},{"id":"T321","span":{"begin":2370,"end":2435},"obj":"Sentence"},{"id":"T322","span":{"begin":2436,"end":2498},"obj":"Sentence"},{"id":"T323","span":{"begin":2499,"end":2548},"obj":"Sentence"},{"id":"T324","span":{"begin":2549,"end":2620},"obj":"Sentence"},{"id":"T325","span":{"begin":2621,"end":2692},"obj":"Sentence"},{"id":"T326","span":{"begin":2693,"end":2751},"obj":"Sentence"},{"id":"T327","span":{"begin":2752,"end":2818},"obj":"Sentence"},{"id":"T328","span":{"begin":2819,"end":2887},"obj":"Sentence"},{"id":"T329","span":{"begin":2888,"end":2968},"obj":"Sentence"},{"id":"T330","span":{"begin":2969,"end":3015},"obj":"Sentence"},{"id":"T331","span":{"begin":3016,"end":3056},"obj":"Sentence"},{"id":"T332","span":{"begin":3057,"end":3097},"obj":"Sentence"},{"id":"T333","span":{"begin":3098,"end":3129},"obj":"Sentence"},{"id":"T334","span":{"begin":3130,"end":3161},"obj":"Sentence"},{"id":"T335","span":{"begin":3162,"end":3218},"obj":"Sentence"},{"id":"T336","span":{"begin":3219,"end":3439},"obj":"Sentence"},{"id":"T337","span":{"begin":3440,"end":3747},"obj":"Sentence"},{"id":"T338","span":{"begin":3748,"end":3847},"obj":"Sentence"},{"id":"T339","span":{"begin":3848,"end":3943},"obj":"Sentence"},{"id":"T340","span":{"begin":3944,"end":4037},"obj":"Sentence"},{"id":"T341","span":{"begin":4038,"end":4077},"obj":"Sentence"},{"id":"T342","span":{"begin":4078,"end":4101},"obj":"Sentence"},{"id":"T343","span":{"begin":4102,"end":4122},"obj":"Sentence"},{"id":"T344","span":{"begin":4123,"end":4213},"obj":"Sentence"},{"id":"T345","span":{"begin":4214,"end":4248},"obj":"Sentence"},{"id":"T346","span":{"begin":4249,"end":4311},"obj":"Sentence"},{"id":"T347","span":{"begin":4312,"end":4361},"obj":"Sentence"},{"id":"T348","span":{"begin":4362,"end":4431},"obj":"Sentence"},{"id":"T349","span":{"begin":4432,"end":4508},"obj":"Sentence"},{"id":"T350","span":{"begin":4509,"end":4564},"obj":"Sentence"},{"id":"T351","span":{"begin":4565,"end":4637},"obj":"Sentence"},{"id":"T352","span":{"begin":4638,"end":4703},"obj":"Sentence"},{"id":"T353","span":{"begin":4704,"end":4779},"obj":"Sentence"},{"id":"T354","span":{"begin":4780,"end":4814},"obj":"Sentence"},{"id":"T355","span":{"begin":4815,"end":4879},"obj":"Sentence"},{"id":"T356","span":{"begin":4880,"end":4929},"obj":"Sentence"},{"id":"T357","span":{"begin":4930,"end":4994},"obj":"Sentence"},{"id":"T358","span":{"begin":4995,"end":5067},"obj":"Sentence"},{"id":"T359","span":{"begin":5068,"end":5127},"obj":"Sentence"},{"id":"T360","span":{"begin":5128,"end":5200},"obj":"Sentence"},{"id":"T361","span":{"begin":5201,"end":5266},"obj":"Sentence"},{"id":"T362","span":{"begin":5267,"end":5342},"obj":"Sentence"},{"id":"T363","span":{"begin":5343,"end":5408},"obj":"Sentence"},{"id":"T364","span":{"begin":5409,"end":5471},"obj":"Sentence"},{"id":"T365","span":{"begin":5472,"end":5520},"obj":"Sentence"},{"id":"T366","span":{"begin":5521,"end":5587},"obj":"Sentence"},{"id":"T367","span":{"begin":5588,"end":5662},"obj":"Sentence"},{"id":"T368","span":{"begin":5663,"end":5727},"obj":"Sentence"},{"id":"T369","span":{"begin":5728,"end":5801},"obj":"Sentence"},{"id":"T370","span":{"begin":5802,"end":5866},"obj":"Sentence"},{"id":"T371","span":{"begin":5867,"end":5942},"obj":"Sentence"},{"id":"T372","span":{"begin":5943,"end":5989},"obj":"Sentence"},{"id":"T373","span":{"begin":5990,"end":6030},"obj":"Sentence"},{"id":"T374","span":{"begin":6031,"end":6071},"obj":"Sentence"},{"id":"T375","span":{"begin":6072,"end":6103},"obj":"Sentence"},{"id":"T376","span":{"begin":6104,"end":6135},"obj":"Sentence"},{"id":"T377","span":{"begin":6136,"end":6192},"obj":"Sentence"},{"id":"T378","span":{"begin":6193,"end":6413},"obj":"Sentence"},{"id":"T379","span":{"begin":6414,"end":6721},"obj":"Sentence"},{"id":"T380","span":{"begin":6722,"end":6821},"obj":"Sentence"},{"id":"T381","span":{"begin":6822,"end":6917},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Table 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"}
LitCovid-PubTator
{"project":"LitCovid-PubTator","denotations":[{"id":"229","span":{"begin":1083,"end":1094},"obj":"Gene"},{"id":"231","span":{"begin":1053,"end":1061},"obj":"Disease"},{"id":"233","span":{"begin":4059,"end":4070},"obj":"Gene"},{"id":"235","span":{"begin":3993,"end":4001},"obj":"Disease"},{"id":"237","span":{"begin":696,"end":706},"obj":"Disease"}],"attributes":[{"id":"A229","pred":"tao:has_database_id","subj":"229","obj":"Gene:2233"},{"id":"A231","pred":"tao:has_database_id","subj":"231","obj":"MESH:C000657245"},{"id":"A233","pred":"tao:has_database_id","subj":"233","obj":"Gene:2233"},{"id":"A235","pred":"tao:has_database_id","subj":"235","obj":"MESH:C000657245"},{"id":"A237","pred":"tao:has_database_id","subj":"237","obj":"MESH:D007239"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Table 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"}