PMC:7210464 / 78960-82596 JSONTXT

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T175","span":{"begin":359,"end":367},"obj":"Disease"},{"id":"T176","span":{"begin":565,"end":573},"obj":"Disease"}],"attributes":[{"id":"A175","pred":"mondo_id","subj":"T175","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A176","pred":"mondo_id","subj":"T176","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"Weather conditions affect disease transmissions either directly if the virus can more easily survive and spread in certain environment, or indirectly by changing human behavior. Table 9 reports results of the first stage of the IV regressions (Table 4) using the full sample. In columns (1) and (2), the dependent variables are the numbers of newly confirmed COVID-19 cases in the own city in the preceding first and second weeks, respectively. In columns (3) and (4), the dependent variables are the sum of inverse log distance weighted numbers of newly confirmed COVID-19 cases in other cities in the preceding first and second weeks, respectively. These are the endogenous variables in the IV regressions. The weather variables in the preceding first and second weeks are included in the control variables. The weather variables in the preceding third and fourth weeks are the excluded instruments, and their coefficients are reported in the table. Because the variables are averages in 7-day moving windows, the error term may be serially correlated, and we include city by week fixed effects. Also included in the control variables are the average numbers of new cases in Wuhan in the preceding first and second weeks, interacted with the inverse log distance or the population flow.\nTable 9 First stage regressions\nDependent variable Average # of new cases\nOwn city Other cities\n1-week lag 2-week lag 1-week lag 2-week lag\n(1) (2) (3) (4)\nOwn City\nMaximum temperature, 3-week lag 0.200*** − 0.0431 0.564 − 2.022***\n(0.0579) (0.0503) (0.424) (0.417)\nPrecipitation, 3-week lag − 0.685 − 0.865* 4.516 − 1.998\n(0.552) (0.480) (4.045) (3.982)\nWind speed, 3-week lag 0.508** 0.299 − 0.827 3.247*\n(0.256) (0.223) (1.878) (1.849)\nPrecipitation × wind speed, 3-week lag − 0.412** 0.122 − 1.129 − 2.091\n(0.199) (0.173) (1.460) (1.437)\nMaximum temperature, 4-week lag 0.162*** 0.125** 1.379*** 1.181***\n(0.0560) (0.0487) (0.410) (0.404)\nPrecipitation, 4-week lag 0.0250 − 0.503 2.667 8.952***\n(0.440) (0.383) (3.224) (3.174)\nWind speed, 4-week lag 0.179 0.214 − 1.839 1.658\n(0.199) (0.173) (1.458) (1.435)\nPrecipitation × wind speed, 4-week lag − 0.354** − 0.0270 1.107 − 2.159**\n(0.145) (0.126) (1.059) (1.043)\nOther cities, weight = inverse distance\nMaximum temperature, 3-week lag − 0.0809*** − 0.00633 0.0520 1.152***\n(0.0203) (0.0176) (0.149) (0.146)\nPrecipitation, 3-week lag 4.366*** − 2.370*** 17.99*** − 72.68***\n(0.639) (0.556) (4.684) (4.611)\nWind speed, 3-week lag 0.326*** − 0.222** − 1.456 − 11.02***\n(0.126) (0.110) (0.926) (0.912)\nPrecipitation × wind speed, 3-week lag − 1.780*** 0.724*** − 6.750*** 27.73***\n(0.227) (0.197) (1.663) (1.637)\nMaximum temperature, 4-week lag − 0.0929*** − 0.0346* − 0.518*** 0.0407\n(0.0220) (0.0191) (0.161) (0.159)\nPrecipitation, 4-week lag 3.357*** − 0.578 46.57*** − 25.31***\n(0.504) (0.438) (3.691) (3.633)\nWind speed, 4-week lag 0.499*** 0.214** 4.660*** − 4.639***\n(0.107) (0.0934) (0.787) (0.774)\nPrecipitation × wind speed, 4-week lag − 1.358*** − 0.0416 − 17.26*** 8.967***\n(0.178) (0.155) (1.303) (1.282)\nF statistic 11.41 8.46 19.10 36.32\np value 0.0000 0.0000 0.0000 0.0000\nObservations 12,768 12,768 12,768 12,768\nNumber of cities 304 304 304 304\n# cases in Wuhan Yes Yes Yes Yes\nContemporaneous weather controls Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes\nCity by week FE Yes Yes Yes Yes\nThis table shows the results of the first stage IV regressions. The weather variables are weekly averages of daily weather readings. Coefficients of the weather variables which are used as excluded instrumental variables are reported. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1"}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T301","span":{"begin":71,"end":76},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_10239"},{"id":"T302","span":{"begin":162,"end":167},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T303","span":{"begin":889,"end":900},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"},{"id":"T304","span":{"begin":1392,"end":1395},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T305","span":{"begin":1403,"end":1406},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T306","span":{"begin":1414,"end":1417},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T307","span":{"begin":1425,"end":1428},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T308","span":{"begin":1438,"end":1443},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T309","span":{"begin":1482,"end":1485},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T310","span":{"begin":1577,"end":1580},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T311","span":{"begin":1663,"end":1666},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T312","span":{"begin":1763,"end":1766},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T313","span":{"begin":1859,"end":1862},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T314","span":{"begin":1954,"end":1957},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T315","span":{"begin":2039,"end":2042},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T316","span":{"begin":2136,"end":2139},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T317","span":{"begin":2275,"end":2278},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T318","span":{"begin":2373,"end":2376},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T319","span":{"begin":2468,"end":2471},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T320","span":{"begin":2577,"end":2580},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T321","span":{"begin":2681,"end":2684},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T322","span":{"begin":2781,"end":2784},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T323","span":{"begin":2873,"end":2876},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T324","span":{"begin":2982,"end":2985},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T325","span":{"begin":3563,"end":3575},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"}],"text":"Weather conditions affect disease transmissions either directly if the virus can more easily survive and spread in certain environment, or indirectly by changing human behavior. Table 9 reports results of the first stage of the IV regressions (Table 4) using the full sample. In columns (1) and (2), the dependent variables are the numbers of newly confirmed COVID-19 cases in the own city in the preceding first and second weeks, respectively. In columns (3) and (4), the dependent variables are the sum of inverse log distance weighted numbers of newly confirmed COVID-19 cases in other cities in the preceding first and second weeks, respectively. These are the endogenous variables in the IV regressions. The weather variables in the preceding first and second weeks are included in the control variables. The weather variables in the preceding third and fourth weeks are the excluded instruments, and their coefficients are reported in the table. Because the variables are averages in 7-day moving windows, the error term may be serially correlated, and we include city by week fixed effects. Also included in the control variables are the average numbers of new cases in Wuhan in the preceding first and second weeks, interacted with the inverse log distance or the population flow.\nTable 9 First stage regressions\nDependent variable Average # of new cases\nOwn city Other cities\n1-week lag 2-week lag 1-week lag 2-week lag\n(1) (2) (3) (4)\nOwn City\nMaximum temperature, 3-week lag 0.200*** − 0.0431 0.564 − 2.022***\n(0.0579) (0.0503) (0.424) (0.417)\nPrecipitation, 3-week lag − 0.685 − 0.865* 4.516 − 1.998\n(0.552) (0.480) (4.045) (3.982)\nWind speed, 3-week lag 0.508** 0.299 − 0.827 3.247*\n(0.256) (0.223) (1.878) (1.849)\nPrecipitation × wind speed, 3-week lag − 0.412** 0.122 − 1.129 − 2.091\n(0.199) (0.173) (1.460) (1.437)\nMaximum temperature, 4-week lag 0.162*** 0.125** 1.379*** 1.181***\n(0.0560) (0.0487) (0.410) (0.404)\nPrecipitation, 4-week lag 0.0250 − 0.503 2.667 8.952***\n(0.440) (0.383) (3.224) (3.174)\nWind speed, 4-week lag 0.179 0.214 − 1.839 1.658\n(0.199) (0.173) (1.458) (1.435)\nPrecipitation × wind speed, 4-week lag − 0.354** − 0.0270 1.107 − 2.159**\n(0.145) (0.126) (1.059) (1.043)\nOther cities, weight = inverse distance\nMaximum temperature, 3-week lag − 0.0809*** − 0.00633 0.0520 1.152***\n(0.0203) (0.0176) (0.149) (0.146)\nPrecipitation, 3-week lag 4.366*** − 2.370*** 17.99*** − 72.68***\n(0.639) (0.556) (4.684) (4.611)\nWind speed, 3-week lag 0.326*** − 0.222** − 1.456 − 11.02***\n(0.126) (0.110) (0.926) (0.912)\nPrecipitation × wind speed, 3-week lag − 1.780*** 0.724*** − 6.750*** 27.73***\n(0.227) (0.197) (1.663) (1.637)\nMaximum temperature, 4-week lag − 0.0929*** − 0.0346* − 0.518*** 0.0407\n(0.0220) (0.0191) (0.161) (0.159)\nPrecipitation, 4-week lag 3.357*** − 0.578 46.57*** − 25.31***\n(0.504) (0.438) (3.691) (3.633)\nWind speed, 4-week lag 0.499*** 0.214** 4.660*** − 4.639***\n(0.107) (0.0934) (0.787) (0.774)\nPrecipitation × wind speed, 4-week lag − 1.358*** − 0.0416 − 17.26*** 8.967***\n(0.178) (0.155) (1.303) (1.282)\nF statistic 11.41 8.46 19.10 36.32\np value 0.0000 0.0000 0.0000 0.0000\nObservations 12,768 12,768 12,768 12,768\nNumber of cities 304 304 304 304\n# cases in Wuhan Yes Yes Yes Yes\nContemporaneous weather controls Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes\nCity by week FE Yes Yes Yes Yes\nThis table shows the results of the first stage IV regressions. The weather variables are weekly averages of daily weather readings. Coefficients of the weather variables which are used as excluded instrumental variables are reported. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1"}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T15","span":{"begin":168,"end":176},"obj":"http://purl.obolibrary.org/obo/GO_0007610"}],"text":"Weather conditions affect disease transmissions either directly if the virus can more easily survive and spread in certain environment, or indirectly by changing human behavior. Table 9 reports results of the first stage of the IV regressions (Table 4) using the full sample. In columns (1) and (2), the dependent variables are the numbers of newly confirmed COVID-19 cases in the own city in the preceding first and second weeks, respectively. In columns (3) and (4), the dependent variables are the sum of inverse log distance weighted numbers of newly confirmed COVID-19 cases in other cities in the preceding first and second weeks, respectively. These are the endogenous variables in the IV regressions. The weather variables in the preceding first and second weeks are included in the control variables. The weather variables in the preceding third and fourth weeks are the excluded instruments, and their coefficients are reported in the table. Because the variables are averages in 7-day moving windows, the error term may be serially correlated, and we include city by week fixed effects. Also included in the control variables are the average numbers of new cases in Wuhan in the preceding first and second weeks, interacted with the inverse log distance or the population flow.\nTable 9 First stage regressions\nDependent variable Average # of new cases\nOwn city Other cities\n1-week lag 2-week lag 1-week lag 2-week lag\n(1) (2) (3) (4)\nOwn City\nMaximum temperature, 3-week lag 0.200*** − 0.0431 0.564 − 2.022***\n(0.0579) (0.0503) (0.424) (0.417)\nPrecipitation, 3-week lag − 0.685 − 0.865* 4.516 − 1.998\n(0.552) (0.480) (4.045) (3.982)\nWind speed, 3-week lag 0.508** 0.299 − 0.827 3.247*\n(0.256) (0.223) (1.878) (1.849)\nPrecipitation × wind speed, 3-week lag − 0.412** 0.122 − 1.129 − 2.091\n(0.199) (0.173) (1.460) (1.437)\nMaximum temperature, 4-week lag 0.162*** 0.125** 1.379*** 1.181***\n(0.0560) (0.0487) (0.410) (0.404)\nPrecipitation, 4-week lag 0.0250 − 0.503 2.667 8.952***\n(0.440) (0.383) (3.224) (3.174)\nWind speed, 4-week lag 0.179 0.214 − 1.839 1.658\n(0.199) (0.173) (1.458) (1.435)\nPrecipitation × wind speed, 4-week lag − 0.354** − 0.0270 1.107 − 2.159**\n(0.145) (0.126) (1.059) (1.043)\nOther cities, weight = inverse distance\nMaximum temperature, 3-week lag − 0.0809*** − 0.00633 0.0520 1.152***\n(0.0203) (0.0176) (0.149) (0.146)\nPrecipitation, 3-week lag 4.366*** − 2.370*** 17.99*** − 72.68***\n(0.639) (0.556) (4.684) (4.611)\nWind speed, 3-week lag 0.326*** − 0.222** − 1.456 − 11.02***\n(0.126) (0.110) (0.926) (0.912)\nPrecipitation × wind speed, 3-week lag − 1.780*** 0.724*** − 6.750*** 27.73***\n(0.227) (0.197) (1.663) (1.637)\nMaximum temperature, 4-week lag − 0.0929*** − 0.0346* − 0.518*** 0.0407\n(0.0220) (0.0191) (0.161) (0.159)\nPrecipitation, 4-week lag 3.357*** − 0.578 46.57*** − 25.31***\n(0.504) (0.438) (3.691) (3.633)\nWind speed, 4-week lag 0.499*** 0.214** 4.660*** − 4.639***\n(0.107) (0.0934) (0.787) (0.774)\nPrecipitation × wind speed, 4-week lag − 1.358*** − 0.0416 − 17.26*** 8.967***\n(0.178) (0.155) (1.303) (1.282)\nF statistic 11.41 8.46 19.10 36.32\np value 0.0000 0.0000 0.0000 0.0000\nObservations 12,768 12,768 12,768 12,768\nNumber of cities 304 304 304 304\n# cases in Wuhan Yes Yes Yes Yes\nContemporaneous weather controls Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes\nCity by week FE Yes Yes Yes Yes\nThis table shows the results of the first stage IV regressions. The weather variables are weekly averages of daily weather readings. Coefficients of the weather variables which are used as excluded instrumental variables are reported. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1"}

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Table 9 reports results of the first stage of the IV regressions (Table 4) using the full sample. In columns (1) and (2), the dependent variables are the numbers of newly confirmed COVID-19 cases in the own city in the preceding first and second weeks, respectively. In columns (3) and (4), the dependent variables are the sum of inverse log distance weighted numbers of newly confirmed COVID-19 cases in other cities in the preceding first and second weeks, respectively. These are the endogenous variables in the IV regressions. The weather variables in the preceding first and second weeks are included in the control variables. The weather variables in the preceding third and fourth weeks are the excluded instruments, and their coefficients are reported in the table. Because the variables are averages in 7-day moving windows, the error term may be serially correlated, and we include city by week fixed effects. Also included in the control variables are the average numbers of new cases in Wuhan in the preceding first and second weeks, interacted with the inverse log distance or the population flow.\nTable 9 First stage regressions\nDependent variable Average # of new cases\nOwn city Other cities\n1-week lag 2-week lag 1-week lag 2-week lag\n(1) (2) (3) (4)\nOwn City\nMaximum temperature, 3-week lag 0.200*** − 0.0431 0.564 − 2.022***\n(0.0579) (0.0503) (0.424) (0.417)\nPrecipitation, 3-week lag − 0.685 − 0.865* 4.516 − 1.998\n(0.552) (0.480) (4.045) (3.982)\nWind speed, 3-week lag 0.508** 0.299 − 0.827 3.247*\n(0.256) (0.223) (1.878) (1.849)\nPrecipitation × wind speed, 3-week lag − 0.412** 0.122 − 1.129 − 2.091\n(0.199) (0.173) (1.460) (1.437)\nMaximum temperature, 4-week lag 0.162*** 0.125** 1.379*** 1.181***\n(0.0560) (0.0487) (0.410) (0.404)\nPrecipitation, 4-week lag 0.0250 − 0.503 2.667 8.952***\n(0.440) (0.383) (3.224) (3.174)\nWind speed, 4-week lag 0.179 0.214 − 1.839 1.658\n(0.199) (0.173) (1.458) (1.435)\nPrecipitation × wind speed, 4-week lag − 0.354** − 0.0270 1.107 − 2.159**\n(0.145) (0.126) (1.059) (1.043)\nOther cities, weight = inverse distance\nMaximum temperature, 3-week lag − 0.0809*** − 0.00633 0.0520 1.152***\n(0.0203) (0.0176) (0.149) (0.146)\nPrecipitation, 3-week lag 4.366*** − 2.370*** 17.99*** − 72.68***\n(0.639) (0.556) (4.684) (4.611)\nWind speed, 3-week lag 0.326*** − 0.222** − 1.456 − 11.02***\n(0.126) (0.110) (0.926) (0.912)\nPrecipitation × wind speed, 3-week lag − 1.780*** 0.724*** − 6.750*** 27.73***\n(0.227) (0.197) (1.663) (1.637)\nMaximum temperature, 4-week lag − 0.0929*** − 0.0346* − 0.518*** 0.0407\n(0.0220) (0.0191) (0.161) (0.159)\nPrecipitation, 4-week lag 3.357*** − 0.578 46.57*** − 25.31***\n(0.504) (0.438) (3.691) (3.633)\nWind speed, 4-week lag 0.499*** 0.214** 4.660*** − 4.639***\n(0.107) (0.0934) (0.787) (0.774)\nPrecipitation × wind speed, 4-week lag − 1.358*** − 0.0416 − 17.26*** 8.967***\n(0.178) (0.155) (1.303) (1.282)\nF statistic 11.41 8.46 19.10 36.32\np value 0.0000 0.0000 0.0000 0.0000\nObservations 12,768 12,768 12,768 12,768\nNumber of cities 304 304 304 304\n# cases in Wuhan Yes Yes Yes Yes\nContemporaneous weather controls Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes\nCity by week FE Yes Yes Yes Yes\nThis table shows the results of the first stage IV regressions. The weather variables are weekly averages of daily weather readings. Coefficients of the weather variables which are used as excluded instrumental variables are reported. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1"}

{"project":"LitCovid-PubTator","denotations":[{"id":"436","span":{"begin":1403,"end":1408},"obj":"Gene"},{"id":"437","span":{"begin":1414,"end":1419},"obj":"Gene"},{"id":"438","span":{"begin":2781,"end":2786},"obj":"Gene"},{"id":"439","span":{"begin":1392,"end":1397},"obj":"Gene"},{"id":"443","span":{"begin":162,"end":167},"obj":"Species"},{"id":"444","span":{"begin":359,"end":367},"obj":"Disease"},{"id":"445","span":{"begin":565,"end":573},"obj":"Disease"}],"attributes":[{"id":"A436","pred":"tao:has_database_id","subj":"436","obj":"Gene:388372"},{"id":"A437","pred":"tao:has_database_id","subj":"437","obj":"Gene:10578"},{"id":"A438","pred":"tao:has_database_id","subj":"438","obj":"Gene:3902"},{"id":"A439","pred":"tao:has_database_id","subj":"439","obj":"Gene:10578"},{"id":"A443","pred":"tao:has_database_id","subj":"443","obj":"Tax:9606"},{"id":"A444","pred":"tao:has_database_id","subj":"444","obj":"MESH:C000657245"},{"id":"A445","pred":"tao:has_database_id","subj":"445","obj":"MESH:C000657245"}],"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":"Weather conditions affect disease transmissions either directly if the virus can more easily survive and spread in certain environment, or indirectly by changing human behavior. Table 9 reports results of the first stage of the IV regressions (Table 4) using the full sample. In columns (1) and (2), the dependent variables are the numbers of newly confirmed COVID-19 cases in the own city in the preceding first and second weeks, respectively. In columns (3) and (4), the dependent variables are the sum of inverse log distance weighted numbers of newly confirmed COVID-19 cases in other cities in the preceding first and second weeks, respectively. These are the endogenous variables in the IV regressions. The weather variables in the preceding first and second weeks are included in the control variables. The weather variables in the preceding third and fourth weeks are the excluded instruments, and their coefficients are reported in the table. Because the variables are averages in 7-day moving windows, the error term may be serially correlated, and we include city by week fixed effects. Also included in the control variables are the average numbers of new cases in Wuhan in the preceding first and second weeks, interacted with the inverse log distance or the population flow.\nTable 9 First stage regressions\nDependent variable Average # of new cases\nOwn city Other cities\n1-week lag 2-week lag 1-week lag 2-week lag\n(1) (2) (3) (4)\nOwn City\nMaximum temperature, 3-week lag 0.200*** − 0.0431 0.564 − 2.022***\n(0.0579) (0.0503) (0.424) (0.417)\nPrecipitation, 3-week lag − 0.685 − 0.865* 4.516 − 1.998\n(0.552) (0.480) (4.045) (3.982)\nWind speed, 3-week lag 0.508** 0.299 − 0.827 3.247*\n(0.256) (0.223) (1.878) (1.849)\nPrecipitation × wind speed, 3-week lag − 0.412** 0.122 − 1.129 − 2.091\n(0.199) (0.173) (1.460) (1.437)\nMaximum temperature, 4-week lag 0.162*** 0.125** 1.379*** 1.181***\n(0.0560) (0.0487) (0.410) (0.404)\nPrecipitation, 4-week lag 0.0250 − 0.503 2.667 8.952***\n(0.440) (0.383) (3.224) (3.174)\nWind speed, 4-week lag 0.179 0.214 − 1.839 1.658\n(0.199) (0.173) (1.458) (1.435)\nPrecipitation × wind speed, 4-week lag − 0.354** − 0.0270 1.107 − 2.159**\n(0.145) (0.126) (1.059) (1.043)\nOther cities, weight = inverse distance\nMaximum temperature, 3-week lag − 0.0809*** − 0.00633 0.0520 1.152***\n(0.0203) (0.0176) (0.149) (0.146)\nPrecipitation, 3-week lag 4.366*** − 2.370*** 17.99*** − 72.68***\n(0.639) (0.556) (4.684) (4.611)\nWind speed, 3-week lag 0.326*** − 0.222** − 1.456 − 11.02***\n(0.126) (0.110) (0.926) (0.912)\nPrecipitation × wind speed, 3-week lag − 1.780*** 0.724*** − 6.750*** 27.73***\n(0.227) (0.197) (1.663) (1.637)\nMaximum temperature, 4-week lag − 0.0929*** − 0.0346* − 0.518*** 0.0407\n(0.0220) (0.0191) (0.161) (0.159)\nPrecipitation, 4-week lag 3.357*** − 0.578 46.57*** − 25.31***\n(0.504) (0.438) (3.691) (3.633)\nWind speed, 4-week lag 0.499*** 0.214** 4.660*** − 4.639***\n(0.107) (0.0934) (0.787) (0.774)\nPrecipitation × wind speed, 4-week lag − 1.358*** − 0.0416 − 17.26*** 8.967***\n(0.178) (0.155) (1.303) (1.282)\nF statistic 11.41 8.46 19.10 36.32\np value 0.0000 0.0000 0.0000 0.0000\nObservations 12,768 12,768 12,768 12,768\nNumber of cities 304 304 304 304\n# cases in Wuhan Yes Yes Yes Yes\nContemporaneous weather controls Yes Yes Yes Yes\nCity FE Yes Yes Yes Yes\nDate FE Yes Yes Yes Yes\nCity by week FE Yes Yes Yes Yes\nThis table shows the results of the first stage IV regressions. The weather variables are weekly averages of daily weather readings. Coefficients of the weather variables which are used as excluded instrumental variables are reported. *** p \u003c 0.01, ** p \u003c 0.05, * p \u003c 0.1"}