PMC:7210464 / 85620-90207
Annnotations
LitCovid-PD-FMA-UBERON
{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T26","span":{"begin":0,"end":8},"obj":"Body_part"}],"attributes":[{"id":"A26","pred":"fma_id","subj":"T26","obj":"http://purl.org/sig/ont/fma/fma14542"}],"text":"Appendix B. Exclude clinically diagnosed cases in Hubei\nCOVID-19 case definitions were changed in Hubei province on February 12 and February 20. Starting on February 12, COVID-19 cases could also be confirmed based on clinical diagnosis in Hubei province, in addition to molecular diagnostic tests. This resulted in a sharp increase in the number of daily new cases reported in Hubei, and in particular Wuhan (Fig. 2). The use of clinical diagnosis in confirming cases ended on February 20. The numbers of cases that are confirmed based on clinical diagnosis for February 12, 13, and 14 are reported by the Health Commission of Hubei Province and are displayed in Table 11. As a robustness check, we re-estimate the model after removing these cases from the daily case counts (Fig. 8). Our main findings still hold (Table 12). The transmission rates are significantly lower in February compared with January. Population flow from the epidemic source increases the infections in destinations, and this effect is slightly delayed in February.\nFig. 8 Number of daily new confirmed cases of COVID-19 in mainland China and revised case counts in Hubei Province\nTable 11 Number of cumulative clinically diagnosed cases in Hubei\nCity Feb 12 Feb 13 Feb 14\nEzhou 155 168 189\nEnshi 19 21 27\nHuanggang 221 306 306\nHuangshi 12 26 42\nJingmen 202 155‡ 150‡\nJingzhou 287 269‡ 257‡\nQianjiang 0 9 19\nShiyan 3 4 3‡\nSuizhou 0 6 4‡\nTianmen 26 67 65‡\nWuhan 12364 14031 14953\nXiantao 2 2 2\nXianning 6 189 286\nXiangyang 0 0 4\nXiaogan 35 80 148\nYichang 0 51 67\n‡The reductions in cumulative case counts are due to revised diagnosis from further tests\nTable 12 Within- and between-city transmission of COVID-19, revised case counts 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 0.747*** 0.840*** 0.939*** 2.456*** 0.790*** 1.199***\n(0.0182) (0.0431) (0.102) (0.638) (0.0211) (0.0904)\nOther cities 0.00631** 0.0124 0.0889 0.0412 − 0.00333 − 0.0328\nwt. = inv. dist. (0.00289) (0.00897) (0.0714) (0.0787) (0.00601) (0.0230)\nWuhan 0.0331*** 0.0277 − 0.879 − 0.957 0.0543* 0.0840\nwt. = inv. dist. (0.0116) (0.0284) (0.745) (0.955) (0.0271) (0.0684)\nWuhan 0.00365*** 0.00408*** 0.00462*** 0.00471*** − 0.000882 − 0.00880***\nwt. = pop. flow (0.000282) (0.000287) (0.000326) (0.000696) (0.000797) (0.00252)\nAverage # of new cases, 2-week lag\nOwn city − 0.519*** − 0.673*** 2.558 − 1.633 − 0.286*** − 0.141\n(0.0138) (0.0532) (2.350) (2.951) (0.0361) (0.0899)\nOther cities − 0.00466 − 0.0208 − 0.361 − 0.0404 − 0.00291 − 0.0235**\nwt. = inv. dist. (0.00350) (0.0143) (0.371) (0.496) (0.00566) (0.0113)\nWuhan − 0.0914* 0.0308 3.053 3.031 − 0.154 0.0110\nwt. = inv. dist. (0.0465) (0.0438) (2.834) (3.559) (0.0965) (0.0244)\nWuhan 0.00827*** 0.00807*** 0.00711*** − 0.00632 0.0119*** 0.0112***\nwt. = pop. flow (0.000264) (0.000185) (0.00213) (0.00741) (0.000523) (0.000627)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.235*** 0.983*** 1.564*** 2.992*** 0.391*** 0.725***\n(0.0355) (0.158) (0.174) (0.892) (0.0114) (0.101)\nOther cities 0.00812 − 0.0925* 0.0414 0.0704 0.0181 − 0.00494\nwt. = inv. dist. (0.00899) (0.0480) (0.0305) (0.0523) (0.0172) (0.0228)\nWuhan − 0.172* − 0.114** − 0.309 − 0.608 − 0.262 − 0.299*\nwt. = inv. dist. (0.101) (0.0472) (0.251) (0.460) (0.161) (0.169)\nWuhan 0.0133*** 0.0107*** 0.00779*** 0.00316 0.0152*** 0.0143***\nwt. = pop. flow (0.000226) (0.000509) (0.000518) (0.00276) (0.000155) (0.000447)\nObservations 12,768 12,768 4,256 4,256 8,512 8,512\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"}
LitCovid-PD-MONDO
{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T178","span":{"begin":56,"end":64},"obj":"Disease"},{"id":"T179","span":{"begin":170,"end":178},"obj":"Disease"},{"id":"T180","span":{"begin":964,"end":977},"obj":"Disease"},{"id":"T181","span":{"begin":1087,"end":1095},"obj":"Disease"},{"id":"T182","span":{"begin":1677,"end":1685},"obj":"Disease"},{"id":"T183","span":{"begin":2120,"end":2123},"obj":"Disease"},{"id":"T184","span":{"begin":2248,"end":2251},"obj":"Disease"},{"id":"T185","span":{"begin":2693,"end":2696},"obj":"Disease"},{"id":"T186","span":{"begin":2814,"end":2817},"obj":"Disease"},{"id":"T187","span":{"begin":3273,"end":3276},"obj":"Disease"},{"id":"T188","span":{"begin":3403,"end":3406},"obj":"Disease"}],"attributes":[{"id":"A178","pred":"mondo_id","subj":"T178","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A179","pred":"mondo_id","subj":"T179","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A180","pred":"mondo_id","subj":"T180","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A181","pred":"mondo_id","subj":"T181","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A182","pred":"mondo_id","subj":"T182","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A183","pred":"mondo_id","subj":"T183","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A184","pred":"mondo_id","subj":"T184","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A185","pred":"mondo_id","subj":"T185","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A186","pred":"mondo_id","subj":"T186","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A187","pred":"mondo_id","subj":"T187","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"},{"id":"A188","pred":"mondo_id","subj":"T188","obj":"http://purl.obolibrary.org/obo/MONDO_0043678"}],"text":"Appendix B. Exclude clinically diagnosed cases in Hubei\nCOVID-19 case definitions were changed in Hubei province on February 12 and February 20. Starting on February 12, COVID-19 cases could also be confirmed based on clinical diagnosis in Hubei province, in addition to molecular diagnostic tests. This resulted in a sharp increase in the number of daily new cases reported in Hubei, and in particular Wuhan (Fig. 2). The use of clinical diagnosis in confirming cases ended on February 20. The numbers of cases that are confirmed based on clinical diagnosis for February 12, 13, and 14 are reported by the Health Commission of Hubei Province and are displayed in Table 11. As a robustness check, we re-estimate the model after removing these cases from the daily case counts (Fig. 8). Our main findings still hold (Table 12). The transmission rates are significantly lower in February compared with January. Population flow from the epidemic source increases the infections in destinations, and this effect is slightly delayed in February.\nFig. 8 Number of daily new confirmed cases of COVID-19 in mainland China and revised case counts in Hubei Province\nTable 11 Number of cumulative clinically diagnosed cases in Hubei\nCity Feb 12 Feb 13 Feb 14\nEzhou 155 168 189\nEnshi 19 21 27\nHuanggang 221 306 306\nHuangshi 12 26 42\nJingmen 202 155‡ 150‡\nJingzhou 287 269‡ 257‡\nQianjiang 0 9 19\nShiyan 3 4 3‡\nSuizhou 0 6 4‡\nTianmen 26 67 65‡\nWuhan 12364 14031 14953\nXiantao 2 2 2\nXianning 6 189 286\nXiangyang 0 0 4\nXiaogan 35 80 148\nYichang 0 51 67\n‡The reductions in cumulative case counts are due to revised diagnosis from further tests\nTable 12 Within- and between-city transmission of COVID-19, revised case counts 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 0.747*** 0.840*** 0.939*** 2.456*** 0.790*** 1.199***\n(0.0182) (0.0431) (0.102) (0.638) (0.0211) (0.0904)\nOther cities 0.00631** 0.0124 0.0889 0.0412 − 0.00333 − 0.0328\nwt. = inv. dist. (0.00289) (0.00897) (0.0714) (0.0787) (0.00601) (0.0230)\nWuhan 0.0331*** 0.0277 − 0.879 − 0.957 0.0543* 0.0840\nwt. = inv. dist. (0.0116) (0.0284) (0.745) (0.955) (0.0271) (0.0684)\nWuhan 0.00365*** 0.00408*** 0.00462*** 0.00471*** − 0.000882 − 0.00880***\nwt. = pop. flow (0.000282) (0.000287) (0.000326) (0.000696) (0.000797) (0.00252)\nAverage # of new cases, 2-week lag\nOwn city − 0.519*** − 0.673*** 2.558 − 1.633 − 0.286*** − 0.141\n(0.0138) (0.0532) (2.350) (2.951) (0.0361) (0.0899)\nOther cities − 0.00466 − 0.0208 − 0.361 − 0.0404 − 0.00291 − 0.0235**\nwt. = inv. dist. (0.00350) (0.0143) (0.371) (0.496) (0.00566) (0.0113)\nWuhan − 0.0914* 0.0308 3.053 3.031 − 0.154 0.0110\nwt. = inv. dist. (0.0465) (0.0438) (2.834) (3.559) (0.0965) (0.0244)\nWuhan 0.00827*** 0.00807*** 0.00711*** − 0.00632 0.0119*** 0.0112***\nwt. = pop. flow (0.000264) (0.000185) (0.00213) (0.00741) (0.000523) (0.000627)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.235*** 0.983*** 1.564*** 2.992*** 0.391*** 0.725***\n(0.0355) (0.158) (0.174) (0.892) (0.0114) (0.101)\nOther cities 0.00812 − 0.0925* 0.0414 0.0704 0.0181 − 0.00494\nwt. = inv. dist. (0.00899) (0.0480) (0.0305) (0.0523) (0.0172) (0.0228)\nWuhan − 0.172* − 0.114** − 0.309 − 0.608 − 0.262 − 0.299*\nwt. = inv. dist. (0.101) (0.0472) (0.251) (0.460) (0.161) (0.169)\nWuhan 0.0133*** 0.0107*** 0.00779*** 0.00316 0.0152*** 0.0143***\nwt. = pop. flow (0.000226) (0.000509) (0.000518) (0.00276) (0.000155) (0.000447)\nObservations 12,768 12,768 4,256 4,256 8,512 8,512\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"}
LitCovid-PD-CLO
{"project":"LitCovid-PD-CLO","denotations":[{"id":"T351","span":{"begin":9,"end":10},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T352","span":{"begin":292,"end":297},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T353","span":{"begin":316,"end":317},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T354","span":{"begin":670,"end":672},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T355","span":{"begin":677,"end":678},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T356","span":{"begin":1162,"end":1164},"obj":"http://purl.obolibrary.org/obo/CLO_0053733"},{"id":"T357","span":{"begin":1278,"end":1280},"obj":"http://purl.obolibrary.org/obo/CLO_0050509"},{"id":"T358","span":{"begin":1390,"end":1393},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T359","span":{"begin":1462,"end":1465},"obj":"http://purl.obolibrary.org/obo/CLO_0050507"},{"id":"T360","span":{"begin":1511,"end":1513},"obj":"http://purl.obolibrary.org/obo/CLO_0001000"},{"id":"T361","span":{"begin":1517,"end":1520},"obj":"http://purl.obolibrary.org/obo/CLO_0001079"},{"id":"T362","span":{"begin":1621,"end":1626},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T363","span":{"begin":1774,"end":1779},"obj":"http://purl.obolibrary.org/obo/CLO_0001302"},{"id":"T364","span":{"begin":1816,"end":1817},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T365","span":{"begin":1932,"end":1935},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T366","span":{"begin":2497,"end":2500},"obj":"http://purl.obolibrary.org/obo/CLO_0050236"},{"id":"T367","span":{"begin":3032,"end":3033},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T368","span":{"begin":4032,"end":4033},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T369","span":{"begin":4080,"end":4081},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T370","span":{"begin":4346,"end":4358},"obj":"http://purl.obolibrary.org/obo/OBI_0000968"}],"text":"Appendix B. Exclude clinically diagnosed cases in Hubei\nCOVID-19 case definitions were changed in Hubei province on February 12 and February 20. Starting on February 12, COVID-19 cases could also be confirmed based on clinical diagnosis in Hubei province, in addition to molecular diagnostic tests. This resulted in a sharp increase in the number of daily new cases reported in Hubei, and in particular Wuhan (Fig. 2). The use of clinical diagnosis in confirming cases ended on February 20. The numbers of cases that are confirmed based on clinical diagnosis for February 12, 13, and 14 are reported by the Health Commission of Hubei Province and are displayed in Table 11. As a robustness check, we re-estimate the model after removing these cases from the daily case counts (Fig. 8). Our main findings still hold (Table 12). The transmission rates are significantly lower in February compared with January. Population flow from the epidemic source increases the infections in destinations, and this effect is slightly delayed in February.\nFig. 8 Number of daily new confirmed cases of COVID-19 in mainland China and revised case counts in Hubei Province\nTable 11 Number of cumulative clinically diagnosed cases in Hubei\nCity Feb 12 Feb 13 Feb 14\nEzhou 155 168 189\nEnshi 19 21 27\nHuanggang 221 306 306\nHuangshi 12 26 42\nJingmen 202 155‡ 150‡\nJingzhou 287 269‡ 257‡\nQianjiang 0 9 19\nShiyan 3 4 3‡\nSuizhou 0 6 4‡\nTianmen 26 67 65‡\nWuhan 12364 14031 14953\nXiantao 2 2 2\nXianning 6 189 286\nXiangyang 0 0 4\nXiaogan 35 80 148\nYichang 0 51 67\n‡The reductions in cumulative case counts are due to revised diagnosis from further tests\nTable 12 Within- and between-city transmission of COVID-19, revised case counts 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 0.747*** 0.840*** 0.939*** 2.456*** 0.790*** 1.199***\n(0.0182) (0.0431) (0.102) (0.638) (0.0211) (0.0904)\nOther cities 0.00631** 0.0124 0.0889 0.0412 − 0.00333 − 0.0328\nwt. = inv. dist. (0.00289) (0.00897) (0.0714) (0.0787) (0.00601) (0.0230)\nWuhan 0.0331*** 0.0277 − 0.879 − 0.957 0.0543* 0.0840\nwt. = inv. dist. (0.0116) (0.0284) (0.745) (0.955) (0.0271) (0.0684)\nWuhan 0.00365*** 0.00408*** 0.00462*** 0.00471*** − 0.000882 − 0.00880***\nwt. = pop. flow (0.000282) (0.000287) (0.000326) (0.000696) (0.000797) (0.00252)\nAverage # of new cases, 2-week lag\nOwn city − 0.519*** − 0.673*** 2.558 − 1.633 − 0.286*** − 0.141\n(0.0138) (0.0532) (2.350) (2.951) (0.0361) (0.0899)\nOther cities − 0.00466 − 0.0208 − 0.361 − 0.0404 − 0.00291 − 0.0235**\nwt. = inv. dist. (0.00350) (0.0143) (0.371) (0.496) (0.00566) (0.0113)\nWuhan − 0.0914* 0.0308 3.053 3.031 − 0.154 0.0110\nwt. = inv. dist. (0.0465) (0.0438) (2.834) (3.559) (0.0965) (0.0244)\nWuhan 0.00827*** 0.00807*** 0.00711*** − 0.00632 0.0119*** 0.0112***\nwt. = pop. flow (0.000264) (0.000185) (0.00213) (0.00741) (0.000523) (0.000627)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.235*** 0.983*** 1.564*** 2.992*** 0.391*** 0.725***\n(0.0355) (0.158) (0.174) (0.892) (0.0114) (0.101)\nOther cities 0.00812 − 0.0925* 0.0414 0.0704 0.0181 − 0.00494\nwt. = inv. dist. (0.00899) (0.0480) (0.0305) (0.0523) (0.0172) (0.0228)\nWuhan − 0.172* − 0.114** − 0.309 − 0.608 − 0.262 − 0.299*\nwt. = inv. dist. (0.101) (0.0472) (0.251) (0.460) (0.161) (0.169)\nWuhan 0.0133*** 0.0107*** 0.00779*** 0.00316 0.0152*** 0.0143***\nwt. = pop. flow (0.000226) (0.000509) (0.000518) (0.00276) (0.000155) (0.000447)\nObservations 12,768 12,768 4,256 4,256 8,512 8,512\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"}
LitCovid-sentences
{"project":"LitCovid-sentences","denotations":[{"id":"T795","span":{"begin":0,"end":11},"obj":"Sentence"},{"id":"T796","span":{"begin":12,"end":55},"obj":"Sentence"},{"id":"T797","span":{"begin":56,"end":144},"obj":"Sentence"},{"id":"T798","span":{"begin":145,"end":298},"obj":"Sentence"},{"id":"T799","span":{"begin":299,"end":418},"obj":"Sentence"},{"id":"T800","span":{"begin":419,"end":490},"obj":"Sentence"},{"id":"T801","span":{"begin":491,"end":673},"obj":"Sentence"},{"id":"T802","span":{"begin":674,"end":785},"obj":"Sentence"},{"id":"T803","span":{"begin":786,"end":826},"obj":"Sentence"},{"id":"T804","span":{"begin":827,"end":908},"obj":"Sentence"},{"id":"T805","span":{"begin":909,"end":1040},"obj":"Sentence"},{"id":"T806","span":{"begin":1041,"end":1155},"obj":"Sentence"},{"id":"T807","span":{"begin":1156,"end":1221},"obj":"Sentence"},{"id":"T808","span":{"begin":1222,"end":1247},"obj":"Sentence"},{"id":"T809","span":{"begin":1248,"end":1265},"obj":"Sentence"},{"id":"T810","span":{"begin":1266,"end":1280},"obj":"Sentence"},{"id":"T811","span":{"begin":1281,"end":1302},"obj":"Sentence"},{"id":"T812","span":{"begin":1303,"end":1320},"obj":"Sentence"},{"id":"T813","span":{"begin":1321,"end":1342},"obj":"Sentence"},{"id":"T814","span":{"begin":1343,"end":1365},"obj":"Sentence"},{"id":"T815","span":{"begin":1366,"end":1382},"obj":"Sentence"},{"id":"T816","span":{"begin":1383,"end":1396},"obj":"Sentence"},{"id":"T817","span":{"begin":1397,"end":1411},"obj":"Sentence"},{"id":"T818","span":{"begin":1412,"end":1429},"obj":"Sentence"},{"id":"T819","span":{"begin":1430,"end":1453},"obj":"Sentence"},{"id":"T820","span":{"begin":1454,"end":1467},"obj":"Sentence"},{"id":"T821","span":{"begin":1468,"end":1486},"obj":"Sentence"},{"id":"T822","span":{"begin":1487,"end":1502},"obj":"Sentence"},{"id":"T823","span":{"begin":1503,"end":1520},"obj":"Sentence"},{"id":"T824","span":{"begin":1521,"end":1536},"obj":"Sentence"},{"id":"T825","span":{"begin":1537,"end":1626},"obj":"Sentence"},{"id":"T826","span":{"begin":1627,"end":1724},"obj":"Sentence"},{"id":"T827","span":{"begin":1725,"end":1764},"obj":"Sentence"},{"id":"T828","span":{"begin":1765,"end":1788},"obj":"Sentence"},{"id":"T829","span":{"begin":1789,"end":1809},"obj":"Sentence"},{"id":"T830","span":{"begin":1810,"end":1900},"obj":"Sentence"},{"id":"T831","span":{"begin":1901,"end":1935},"obj":"Sentence"},{"id":"T832","span":{"begin":1936,"end":1998},"obj":"Sentence"},{"id":"T833","span":{"begin":1999,"end":2050},"obj":"Sentence"},{"id":"T834","span":{"begin":2051,"end":2113},"obj":"Sentence"},{"id":"T835","span":{"begin":2114,"end":2187},"obj":"Sentence"},{"id":"T836","span":{"begin":2188,"end":2241},"obj":"Sentence"},{"id":"T837","span":{"begin":2242,"end":2310},"obj":"Sentence"},{"id":"T838","span":{"begin":2311,"end":2384},"obj":"Sentence"},{"id":"T839","span":{"begin":2385,"end":2465},"obj":"Sentence"},{"id":"T840","span":{"begin":2466,"end":2500},"obj":"Sentence"},{"id":"T841","span":{"begin":2501,"end":2564},"obj":"Sentence"},{"id":"T842","span":{"begin":2565,"end":2616},"obj":"Sentence"},{"id":"T843","span":{"begin":2617,"end":2686},"obj":"Sentence"},{"id":"T844","span":{"begin":2687,"end":2757},"obj":"Sentence"},{"id":"T845","span":{"begin":2758,"end":2807},"obj":"Sentence"},{"id":"T846","span":{"begin":2808,"end":2876},"obj":"Sentence"},{"id":"T847","span":{"begin":2877,"end":2945},"obj":"Sentence"},{"id":"T848","span":{"begin":2946,"end":3025},"obj":"Sentence"},{"id":"T849","span":{"begin":3026,"end":3091},"obj":"Sentence"},{"id":"T850","span":{"begin":3092,"end":3154},"obj":"Sentence"},{"id":"T851","span":{"begin":3155,"end":3204},"obj":"Sentence"},{"id":"T852","span":{"begin":3205,"end":3266},"obj":"Sentence"},{"id":"T853","span":{"begin":3267,"end":3338},"obj":"Sentence"},{"id":"T854","span":{"begin":3339,"end":3396},"obj":"Sentence"},{"id":"T855","span":{"begin":3397,"end":3462},"obj":"Sentence"},{"id":"T856","span":{"begin":3463,"end":3527},"obj":"Sentence"},{"id":"T857","span":{"begin":3528,"end":3608},"obj":"Sentence"},{"id":"T858","span":{"begin":3609,"end":3659},"obj":"Sentence"},{"id":"T859","span":{"begin":3660,"end":3700},"obj":"Sentence"},{"id":"T860","span":{"begin":3701,"end":3741},"obj":"Sentence"},{"id":"T861","span":{"begin":3742,"end":3773},"obj":"Sentence"},{"id":"T862","span":{"begin":3774,"end":3805},"obj":"Sentence"},{"id":"T863","span":{"begin":3806,"end":3862},"obj":"Sentence"},{"id":"T864","span":{"begin":3863,"end":4083},"obj":"Sentence"},{"id":"T865","span":{"begin":4084,"end":4391},"obj":"Sentence"},{"id":"T866","span":{"begin":4392,"end":4491},"obj":"Sentence"},{"id":"T867","span":{"begin":4492,"end":4587},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Appendix B. Exclude clinically diagnosed cases in Hubei\nCOVID-19 case definitions were changed in Hubei province on February 12 and February 20. Starting on February 12, COVID-19 cases could also be confirmed based on clinical diagnosis in Hubei province, in addition to molecular diagnostic tests. This resulted in a sharp increase in the number of daily new cases reported in Hubei, and in particular Wuhan (Fig. 2). The use of clinical diagnosis in confirming cases ended on February 20. The numbers of cases that are confirmed based on clinical diagnosis for February 12, 13, and 14 are reported by the Health Commission of Hubei Province and are displayed in Table 11. As a robustness check, we re-estimate the model after removing these cases from the daily case counts (Fig. 8). Our main findings still hold (Table 12). The transmission rates are significantly lower in February compared with January. Population flow from the epidemic source increases the infections in destinations, and this effect is slightly delayed in February.\nFig. 8 Number of daily new confirmed cases of COVID-19 in mainland China and revised case counts in Hubei Province\nTable 11 Number of cumulative clinically diagnosed cases in Hubei\nCity Feb 12 Feb 13 Feb 14\nEzhou 155 168 189\nEnshi 19 21 27\nHuanggang 221 306 306\nHuangshi 12 26 42\nJingmen 202 155‡ 150‡\nJingzhou 287 269‡ 257‡\nQianjiang 0 9 19\nShiyan 3 4 3‡\nSuizhou 0 6 4‡\nTianmen 26 67 65‡\nWuhan 12364 14031 14953\nXiantao 2 2 2\nXianning 6 189 286\nXiangyang 0 0 4\nXiaogan 35 80 148\nYichang 0 51 67\n‡The reductions in cumulative case counts are due to revised diagnosis from further tests\nTable 12 Within- and between-city transmission of COVID-19, revised case counts 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 0.747*** 0.840*** 0.939*** 2.456*** 0.790*** 1.199***\n(0.0182) (0.0431) (0.102) (0.638) (0.0211) (0.0904)\nOther cities 0.00631** 0.0124 0.0889 0.0412 − 0.00333 − 0.0328\nwt. = inv. dist. (0.00289) (0.00897) (0.0714) (0.0787) (0.00601) (0.0230)\nWuhan 0.0331*** 0.0277 − 0.879 − 0.957 0.0543* 0.0840\nwt. = inv. dist. (0.0116) (0.0284) (0.745) (0.955) (0.0271) (0.0684)\nWuhan 0.00365*** 0.00408*** 0.00462*** 0.00471*** − 0.000882 − 0.00880***\nwt. = pop. flow (0.000282) (0.000287) (0.000326) (0.000696) (0.000797) (0.00252)\nAverage # of new cases, 2-week lag\nOwn city − 0.519*** − 0.673*** 2.558 − 1.633 − 0.286*** − 0.141\n(0.0138) (0.0532) (2.350) (2.951) (0.0361) (0.0899)\nOther cities − 0.00466 − 0.0208 − 0.361 − 0.0404 − 0.00291 − 0.0235**\nwt. = inv. dist. (0.00350) (0.0143) (0.371) (0.496) (0.00566) (0.0113)\nWuhan − 0.0914* 0.0308 3.053 3.031 − 0.154 0.0110\nwt. = inv. dist. (0.0465) (0.0438) (2.834) (3.559) (0.0965) (0.0244)\nWuhan 0.00827*** 0.00807*** 0.00711*** − 0.00632 0.0119*** 0.0112***\nwt. = pop. flow (0.000264) (0.000185) (0.00213) (0.00741) (0.000523) (0.000627)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.235*** 0.983*** 1.564*** 2.992*** 0.391*** 0.725***\n(0.0355) (0.158) (0.174) (0.892) (0.0114) (0.101)\nOther cities 0.00812 − 0.0925* 0.0414 0.0704 0.0181 − 0.00494\nwt. = inv. dist. (0.00899) (0.0480) (0.0305) (0.0523) (0.0172) (0.0228)\nWuhan − 0.172* − 0.114** − 0.309 − 0.608 − 0.262 − 0.299*\nwt. = inv. dist. (0.101) (0.0472) (0.251) (0.460) (0.161) (0.169)\nWuhan 0.0133*** 0.0107*** 0.00779*** 0.00316 0.0152*** 0.0143***\nwt. = pop. flow (0.000226) (0.000509) (0.000518) (0.00276) (0.000155) (0.000447)\nObservations 12,768 12,768 4,256 4,256 8,512 8,512\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"}
LitCovid-PubTator
{"project":"LitCovid-PubTator","denotations":[{"id":"453","span":{"begin":1087,"end":1095},"obj":"Disease"},{"id":"455","span":{"begin":1746,"end":1757},"obj":"Gene"},{"id":"457","span":{"begin":1677,"end":1685},"obj":"Disease"},{"id":"461","span":{"begin":56,"end":64},"obj":"Disease"},{"id":"462","span":{"begin":170,"end":178},"obj":"Disease"},{"id":"463","span":{"begin":950,"end":974},"obj":"Disease"}],"attributes":[{"id":"A453","pred":"tao:has_database_id","subj":"453","obj":"MESH:C000657245"},{"id":"A455","pred":"tao:has_database_id","subj":"455","obj":"Gene:2233"},{"id":"A457","pred":"tao:has_database_id","subj":"457","obj":"MESH:C000657245"},{"id":"A461","pred":"tao:has_database_id","subj":"461","obj":"MESH:C000657245"},{"id":"A462","pred":"tao:has_database_id","subj":"462","obj":"MESH:C000657245"},{"id":"A463","pred":"tao:has_database_id","subj":"463","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":"Appendix B. Exclude clinically diagnosed cases in Hubei\nCOVID-19 case definitions were changed in Hubei province on February 12 and February 20. Starting on February 12, COVID-19 cases could also be confirmed based on clinical diagnosis in Hubei province, in addition to molecular diagnostic tests. This resulted in a sharp increase in the number of daily new cases reported in Hubei, and in particular Wuhan (Fig. 2). The use of clinical diagnosis in confirming cases ended on February 20. The numbers of cases that are confirmed based on clinical diagnosis for February 12, 13, and 14 are reported by the Health Commission of Hubei Province and are displayed in Table 11. As a robustness check, we re-estimate the model after removing these cases from the daily case counts (Fig. 8). Our main findings still hold (Table 12). The transmission rates are significantly lower in February compared with January. Population flow from the epidemic source increases the infections in destinations, and this effect is slightly delayed in February.\nFig. 8 Number of daily new confirmed cases of COVID-19 in mainland China and revised case counts in Hubei Province\nTable 11 Number of cumulative clinically diagnosed cases in Hubei\nCity Feb 12 Feb 13 Feb 14\nEzhou 155 168 189\nEnshi 19 21 27\nHuanggang 221 306 306\nHuangshi 12 26 42\nJingmen 202 155‡ 150‡\nJingzhou 287 269‡ 257‡\nQianjiang 0 9 19\nShiyan 3 4 3‡\nSuizhou 0 6 4‡\nTianmen 26 67 65‡\nWuhan 12364 14031 14953\nXiantao 2 2 2\nXianning 6 189 286\nXiangyang 0 0 4\nXiaogan 35 80 148\nYichang 0 51 67\n‡The reductions in cumulative case counts are due to revised diagnosis from further tests\nTable 12 Within- and between-city transmission of COVID-19, revised case counts 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 0.747*** 0.840*** 0.939*** 2.456*** 0.790*** 1.199***\n(0.0182) (0.0431) (0.102) (0.638) (0.0211) (0.0904)\nOther cities 0.00631** 0.0124 0.0889 0.0412 − 0.00333 − 0.0328\nwt. = inv. dist. (0.00289) (0.00897) (0.0714) (0.0787) (0.00601) (0.0230)\nWuhan 0.0331*** 0.0277 − 0.879 − 0.957 0.0543* 0.0840\nwt. = inv. dist. (0.0116) (0.0284) (0.745) (0.955) (0.0271) (0.0684)\nWuhan 0.00365*** 0.00408*** 0.00462*** 0.00471*** − 0.000882 − 0.00880***\nwt. = pop. flow (0.000282) (0.000287) (0.000326) (0.000696) (0.000797) (0.00252)\nAverage # of new cases, 2-week lag\nOwn city − 0.519*** − 0.673*** 2.558 − 1.633 − 0.286*** − 0.141\n(0.0138) (0.0532) (2.350) (2.951) (0.0361) (0.0899)\nOther cities − 0.00466 − 0.0208 − 0.361 − 0.0404 − 0.00291 − 0.0235**\nwt. = inv. dist. (0.00350) (0.0143) (0.371) (0.496) (0.00566) (0.0113)\nWuhan − 0.0914* 0.0308 3.053 3.031 − 0.154 0.0110\nwt. = inv. dist. (0.0465) (0.0438) (2.834) (3.559) (0.0965) (0.0244)\nWuhan 0.00827*** 0.00807*** 0.00711*** − 0.00632 0.0119*** 0.0112***\nwt. = pop. flow (0.000264) (0.000185) (0.00213) (0.00741) (0.000523) (0.000627)\nModel B: lagged variables are averages over the preceding 2 weeks\nOwn city 0.235*** 0.983*** 1.564*** 2.992*** 0.391*** 0.725***\n(0.0355) (0.158) (0.174) (0.892) (0.0114) (0.101)\nOther cities 0.00812 − 0.0925* 0.0414 0.0704 0.0181 − 0.00494\nwt. = inv. dist. (0.00899) (0.0480) (0.0305) (0.0523) (0.0172) (0.0228)\nWuhan − 0.172* − 0.114** − 0.309 − 0.608 − 0.262 − 0.299*\nwt. = inv. dist. (0.101) (0.0472) (0.251) (0.460) (0.161) (0.169)\nWuhan 0.0133*** 0.0107*** 0.00779*** 0.00316 0.0152*** 0.0143***\nwt. = pop. flow (0.000226) (0.000509) (0.000518) (0.00276) (0.000155) (0.000447)\nObservations 12,768 12,768 4,256 4,256 8,512 8,512\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"}