PMC:7797241 / 26958-29406 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"242","span":{"begin":26,"end":36},"obj":"Disease"},{"id":"243","span":{"begin":191,"end":199},"obj":"Disease"},{"id":"244","span":{"begin":785,"end":795},"obj":"Disease"},{"id":"245","span":{"begin":1531,"end":1539},"obj":"Disease"}],"attributes":[{"id":"A242","pred":"tao:has_database_id","subj":"242","obj":"MESH:D007239"},{"id":"A243","pred":"tao:has_database_id","subj":"243","obj":"MESH:C000657245"},{"id":"A244","pred":"tao:has_database_id","subj":"244","obj":"MESH:D007239"},{"id":"A245","pred":"tao:has_database_id","subj":"245","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":"As with all models of new infections there are significant parameter uncertainties. Rapidly emerging literature is exploring a wide range of biological and epidemiological factors concerning COVID-19, but due to the worldwide nature of these studies, often parameter bands are wide and may be context specific. For example, early estimates of the basic reproduction number ranged from 1.6 to 3.8 in different locations,28 29 with an early estimate of 2.4 used in UK model projections.8 In addition, the information which informs our parameter selection is rapidly evolving as new data are made available, sometimes on a daily basis. From our initial analysis, we identified the following parameters as critical in determining the epidemic trajectory within our model—the percentage of infections which become symptomatic, the recovery time for cases which do not require hospital, the period between acute and IC occupancy, the length of stay in IC, the probability of transmission per contact and the gradual implementation of lockdown rather than immediate effect. Other parameters (such as the percentage reduction in school-age contacts from school closures) did not seem to influence the dynamic trajectory as strongly—and thus we assume point estimates for these parameters. However, for example, assuming that 95% of school-age contacts are reduced as a direct result of school closures is perhaps an overestimate, and future modelling work should address these uncertainties and their impacts on the epidemic trajectory of COVID-19 (but in this case, this value was somewhat arbitrary, and the assumption was used in the absence of school-age contact survey data). In addition, we did not explicitly model the societal effect prior to governmental advice (social distancing, school closures, lockdown), instead assuming a fixed date, before which we assume there were no interventions. This assumption may not be realistic and could have influenced the model output, but it is difficult to quantify the percentage compliance with interventions prior to the official release of governmental advice. More research is urgently needed to refine these parameter ranges and to validate these biological parameters experimentally. These estimates will improve the model as more empirical data become available. We look forward to reducing the uncertainty in these parameters so that we can make better predictions and fit the data more accurately."}

{"project":"LitCovid-sentences","denotations":[{"id":"T172","span":{"begin":0,"end":83},"obj":"Sentence"},{"id":"T173","span":{"begin":84,"end":310},"obj":"Sentence"},{"id":"T174","span":{"begin":311,"end":632},"obj":"Sentence"},{"id":"T175","span":{"begin":633,"end":1066},"obj":"Sentence"},{"id":"T176","span":{"begin":1067,"end":1280},"obj":"Sentence"},{"id":"T177","span":{"begin":1281,"end":1672},"obj":"Sentence"},{"id":"T178","span":{"begin":1673,"end":1893},"obj":"Sentence"},{"id":"T179","span":{"begin":1894,"end":2105},"obj":"Sentence"},{"id":"T180","span":{"begin":2106,"end":2231},"obj":"Sentence"},{"id":"T181","span":{"begin":2232,"end":2311},"obj":"Sentence"},{"id":"T182","span":{"begin":2312,"end":2448},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"As with all models of new infections there are significant parameter uncertainties. Rapidly emerging literature is exploring a wide range of biological and epidemiological factors concerning COVID-19, but due to the worldwide nature of these studies, often parameter bands are wide and may be context specific. For example, early estimates of the basic reproduction number ranged from 1.6 to 3.8 in different locations,28 29 with an early estimate of 2.4 used in UK model projections.8 In addition, the information which informs our parameter selection is rapidly evolving as new data are made available, sometimes on a daily basis. From our initial analysis, we identified the following parameters as critical in determining the epidemic trajectory within our model—the percentage of infections which become symptomatic, the recovery time for cases which do not require hospital, the period between acute and IC occupancy, the length of stay in IC, the probability of transmission per contact and the gradual implementation of lockdown rather than immediate effect. Other parameters (such as the percentage reduction in school-age contacts from school closures) did not seem to influence the dynamic trajectory as strongly—and thus we assume point estimates for these parameters. However, for example, assuming that 95% of school-age contacts are reduced as a direct result of school closures is perhaps an overestimate, and future modelling work should address these uncertainties and their impacts on the epidemic trajectory of COVID-19 (but in this case, this value was somewhat arbitrary, and the assumption was used in the absence of school-age contact survey data). In addition, we did not explicitly model the societal effect prior to governmental advice (social distancing, school closures, lockdown), instead assuming a fixed date, before which we assume there were no interventions. This assumption may not be realistic and could have influenced the model output, but it is difficult to quantify the percentage compliance with interventions prior to the official release of governmental advice. More research is urgently needed to refine these parameter ranges and to validate these biological parameters experimentally. These estimates will improve the model as more empirical data become available. We look forward to reducing the uncertainty in these parameters so that we can make better predictions and fit the data more accurately."}