PMC:7210464 / 71931-73606 JSONTXT

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T20","span":{"begin":1652,"end":1660},"obj":"Body_part"}],"attributes":[{"id":"A20","pred":"fma_id","subj":"T20","obj":"http://purl.org/sig/ont/fma/fma14542"}],"text":"In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province. We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7. Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D). Assuming a fatality rate of 4%, there would be 56,339 more deaths. The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller. As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C). Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively. These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.\nFig. 7 Counterfactual policy simulations. This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China. The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)"}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T153","span":{"begin":1376,"end":1384},"obj":"Disease"}],"attributes":[{"id":"A153","pred":"mondo_id","subj":"T153","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province. We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7. Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D). Assuming a fatality rate of 4%, there would be 56,339 more deaths. The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller. As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C). Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively. These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.\nFig. 7 Counterfactual policy simulations. This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China. The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)"}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T275","span":{"begin":501,"end":502},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T276","span":{"begin":650,"end":651},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T277","span":{"begin":1539,"end":1540},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T278","span":{"begin":1661,"end":1662},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"}],"text":"In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province. We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7. Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D). Assuming a fatality rate of 4%, there would be 56,339 more deaths. The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller. As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C). Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively. These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.\nFig. 7 Counterfactual policy simulations. This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China. The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)"}

{"project":"LitCovid-sentences","denotations":[{"id":"T653","span":{"begin":0,"end":172},"obj":"Sentence"},{"id":"T654","span":{"begin":173,"end":288},"obj":"Sentence"},{"id":"T655","span":{"begin":289,"end":491},"obj":"Sentence"},{"id":"T656","span":{"begin":492,"end":558},"obj":"Sentence"},{"id":"T657","span":{"begin":559,"end":646},"obj":"Sentence"},{"id":"T658","span":{"begin":647,"end":790},"obj":"Sentence"},{"id":"T659","span":{"begin":791,"end":1029},"obj":"Sentence"},{"id":"T660","span":{"begin":1030,"end":1220},"obj":"Sentence"},{"id":"T661","span":{"begin":1221,"end":1262},"obj":"Sentence"},{"id":"T662","span":{"begin":1263,"end":1489},"obj":"Sentence"},{"id":"T663","span":{"begin":1490,"end":1675},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province. We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7. Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D). Assuming a fatality rate of 4%, there would be 56,339 more deaths. The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller. As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C). Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively. These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.\nFig. 7 Counterfactual policy simulations. This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China. The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)"}

{"project":"LitCovid-PubTator","denotations":[{"id":"381","span":{"begin":1376,"end":1384},"obj":"Disease"},{"id":"383","span":{"begin":551,"end":557},"obj":"Disease"}],"attributes":[{"id":"A381","pred":"tao:has_database_id","subj":"381","obj":"MESH:C000657245"},{"id":"A383","pred":"tao:has_database_id","subj":"383","obj":"MESH:D003643"}],"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":"In Fig. 7, we report the differences between the predicted number of daily new cases in the counterfactual scenarios and the actual data, for cities outside Hubei province. We also report the predicted cumulative effect in each scenario at the bottom of the corresponding panel in Fig. 7. Had the transmission rates in cities outside Hubei province increased to the level observed in late January, by February 29, there would be 1,408,479 (95% CI, 815,585∼2,001,373) more cases (scenario D). Assuming a fatality rate of 4%, there would be 56,339 more deaths. The magnitude of the effect from Wuhan lockdown and local NPIs is considerably smaller. As a result of Wuhan lockdown, 31,071 (95% CI, 8296∼53,845) fewer cases would be reported for cities outside Hubei by February 29 (scenario C). Closed management of communities and family outdoor restrictions would reduce the number of cases by 3803 (95% CI, 1142∼6465; or 15.78 per city with the policy) and 2703 (95% CI, 654∼4751; or 21.98 per city with the policy), respectively. These estimates, combined with additional assumptions on the value of statistical life, lost time from work, etc., may contribute to cost-benefit analyses of relevant public health measures.\nFig. 7 Counterfactual policy simulations. This figure displays the daily differences between the total predicted number and the actual number of daily new COVID-19 cases for each of the four counterfactual scenarios for cities outside Hubei province in mainland China. The spike on February 12 in scenario C is due to a sharp increase in daily case counts in Wuhan resulting from changes in case definitions in Hubei province (see Appendix B for details)"}