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LitCovid-PD-MONDO

LitCovid-PD-CLO

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T71","span":{"begin":124,"end":125},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T72","span":{"begin":427,"end":428},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T73","span":{"begin":885,"end":886},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T74","span":{"begin":925,"end":926},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T75","span":{"begin":1121,"end":1122},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T76","span":{"begin":1230,"end":1231},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T77","span":{"begin":1331,"end":1332},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T78","span":{"begin":1390,"end":1391},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T79","span":{"begin":1544,"end":1551},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"},{"id":"T80","span":{"begin":1607,"end":1608},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T81","span":{"begin":2094,"end":2101},"obj":"http://purl.obolibrary.org/obo/CLO_0009985"},{"id":"T82","span":{"begin":2392,"end":2397},"obj":"http://purl.obolibrary.org/obo/UBERON_0000473"}],"text":"Discussion and conclusions\nWe used some parameter estimates from (He et al., 2013). The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic. Recent studies showed that COVID-19 transmitted rapidly. In this regard, it resembles influenza rather than SARS. In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units. Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.\nThe merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period. Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P. Wu et al., 2020). Thus, our model should be considered as a baseline model for further improvement.\nWe avoid to fit model to data in conventional way. Instead, we use a simple model framework to discuss what elements might be needed. For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies. Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated. We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation. Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.\nIn this work, we focused on the transmission of COVID-19 in Wuhan, China. Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context. Our model can be fitted to daily data when more information (e.g., daily number of tests) is available."}

LitCovid-PD-HP

{"project":"LitCovid-PD-HP","denotations":[{"id":"T7","span":{"begin":181,"end":190},"obj":"Phenotype"},{"id":"T8","span":{"begin":515,"end":524},"obj":"Phenotype"}],"attributes":[{"id":"A7","pred":"hp_id","subj":"T7","obj":"http://purl.obolibrary.org/obo/HP_0002090"},{"id":"A8","pred":"hp_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/HP_0002090"}],"text":"Discussion and conclusions\nWe used some parameter estimates from (He et al., 2013). The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic. Recent studies showed that COVID-19 transmitted rapidly. In this regard, it resembles influenza rather than SARS. In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units. Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.\nThe merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period. Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P. Wu et al., 2020). Thus, our model should be considered as a baseline model for further improvement.\nWe avoid to fit model to data in conventional way. Instead, we use a simple model framework to discuss what elements might be needed. For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies. Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated. We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation. Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.\nIn this work, we focused on the transmission of COVID-19 in Wuhan, China. Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context. Our model can be fitted to daily data when more information (e.g., daily number of tests) is available."}

LitCovid-PD-GO-BP

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T4","span":{"begin":801,"end":812},"obj":"http://purl.obolibrary.org/obo/GO_0007610"}],"text":"Discussion and conclusions\nWe used some parameter estimates from (He et al., 2013). The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic. Recent studies showed that COVID-19 transmitted rapidly. In this regard, it resembles influenza rather than SARS. In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units. Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.\nThe merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period. Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P. Wu et al., 2020). Thus, our model should be considered as a baseline model for further improvement.\nWe avoid to fit model to data in conventional way. Instead, we use a simple model framework to discuss what elements might be needed. For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies. Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated. We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation. Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.\nIn this work, we focused on the transmission of COVID-19 in Wuhan, China. Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context. Our model can be fitted to daily data when more information (e.g., daily number of tests) is available."}

LitCovid-sentences

{"project":"LitCovid-sentences","denotations":[{"id":"T125","span":{"begin":0,"end":26},"obj":"Sentence"},{"id":"T126","span":{"begin":27,"end":83},"obj":"Sentence"},{"id":"T127","span":{"begin":84,"end":257},"obj":"Sentence"},{"id":"T128","span":{"begin":258,"end":314},"obj":"Sentence"},{"id":"T129","span":{"begin":315,"end":371},"obj":"Sentence"},{"id":"T130","span":{"begin":372,"end":563},"obj":"Sentence"},{"id":"T131","span":{"begin":564,"end":709},"obj":"Sentence"},{"id":"T132","span":{"begin":710,"end":945},"obj":"Sentence"},{"id":"T133","span":{"begin":946,"end":1062},"obj":"Sentence"},{"id":"T134","span":{"begin":1063,"end":1080},"obj":"Sentence"},{"id":"T135","span":{"begin":1081,"end":1162},"obj":"Sentence"},{"id":"T136","span":{"begin":1163,"end":1213},"obj":"Sentence"},{"id":"T137","span":{"begin":1214,"end":1296},"obj":"Sentence"},{"id":"T138","span":{"begin":1297,"end":1571},"obj":"Sentence"},{"id":"T139","span":{"begin":1572,"end":1692},"obj":"Sentence"},{"id":"T140","span":{"begin":1693,"end":1880},"obj":"Sentence"},{"id":"T141","span":{"begin":1881,"end":2076},"obj":"Sentence"},{"id":"T142","span":{"begin":2077,"end":2150},"obj":"Sentence"},{"id":"T143","span":{"begin":2151,"end":2308},"obj":"Sentence"},{"id":"T144","span":{"begin":2309,"end":2412},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Discussion and conclusions\nWe used some parameter estimates from (He et al., 2013). The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic. Recent studies showed that COVID-19 transmitted rapidly. In this regard, it resembles influenza rather than SARS. In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units. Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.\nThe merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period. Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P. Wu et al., 2020). Thus, our model should be considered as a baseline model for further improvement.\nWe avoid to fit model to data in conventional way. Instead, we use a simple model framework to discuss what elements might be needed. For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies. Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated. We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation. Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.\nIn this work, we focused on the transmission of COVID-19 in Wuhan, China. Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context. Our model can be fitted to daily data when more information (e.g., daily number of tests) is available."}

LitCovid-PubTator

{"project":"LitCovid-PubTator","denotations":[{"id":"126","span":{"begin":181,"end":190},"obj":"Disease"},{"id":"127","span":{"begin":285,"end":293},"obj":"Disease"},{"id":"128","span":{"begin":366,"end":370},"obj":"Disease"},{"id":"129","span":{"begin":515,"end":524},"obj":"Disease"},{"id":"130","span":{"begin":596,"end":605},"obj":"Disease"},{"id":"131","span":{"begin":684,"end":692},"obj":"Disease"},{"id":"133","span":{"begin":845,"end":853},"obj":"Disease"},{"id":"136","span":{"begin":1799,"end":1807},"obj":"Disease"},{"id":"137","span":{"begin":1914,"end":1922},"obj":"Disease"},{"id":"139","span":{"begin":2125,"end":2133},"obj":"Disease"}],"attributes":[{"id":"A126","pred":"tao:has_database_id","subj":"126","obj":"MESH:D011014"},{"id":"A127","pred":"tao:has_database_id","subj":"127","obj":"MESH:C000657245"},{"id":"A128","pred":"tao:has_database_id","subj":"128","obj":"MESH:D045169"},{"id":"A129","pred":"tao:has_database_id","subj":"129","obj":"MESH:D011014"},{"id":"A130","pred":"tao:has_database_id","subj":"130","obj":"MESH:D007239"},{"id":"A131","pred":"tao:has_database_id","subj":"131","obj":"MESH:C000657245"},{"id":"A133","pred":"tao:has_database_id","subj":"133","obj":"MESH:D015047"},{"id":"A136","pred":"tao:has_database_id","subj":"136","obj":"MESH:C000657245"},{"id":"A137","pred":"tao:has_database_id","subj":"137","obj":"MESH:D007239"},{"id":"A139","pred":"tao:has_database_id","subj":"139","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":"Discussion and conclusions\nWe used some parameter estimates from (He et al., 2013). The estimates were obtained via fitting a mechanistic model to the observed weekly influenza and pneumonia mortality in England and Wales during the 1918 influenza pandemic. Recent studies showed that COVID-19 transmitted rapidly. In this regard, it resembles influenza rather than SARS. In our 1918 influenza work (He et al., 2013), we built a similar model as we introduced here, and we fitted that model to weekly influenza and pneumonia mortality in 334 administrative units. Note that 1918 influenza had an infection-fatality-rate of 2%, which was at the same level of the case-fatality-rate of COVID-19 in Wuhan, China.\nThe merit of our model is that we considered some essential elements, including individual behavioural response, governmental actions, zoonotic transmission and emigration of a large proportion of the population in a short time period. Meanwhile, our model is relatively simple and our estimates are in line with previous studies (Imai et al., 2020, P. Wu et al., 2020). Thus, our model should be considered as a baseline model for further improvement.\nWe avoid to fit model to data in conventional way. Instead, we use a simple model framework to discuss what elements might be needed. For instance, in order to achieve a good fitting performance, one obviously needs to include a time-varying report rate (as we reconstructed in Figure 4b), which was caused by the availability of medical supplies, hospital capacities and changing testing/reporting policies. Thus it would be challenging given a relatively short time series, and several other unknown parameters to be estimated. We employ some parameter estimates from the 1918 influenza pandemic, given the similar characteristics of COVID-19 and influenza (most cases are mild) and the similar level of mitigation. Transmission from asymptotically infected cases is reported but the contribution of asymptomatic transmission is unclear (presumably small), which shall be further investigated in future studies.\nIn this work, we focused on the transmission of COVID-19 in Wuhan, China. Our conceptual framework can be applied to other cities/countries, or be built into one multiple-patch model for modelling multiple cities/countries context. Our model can be fitted to daily data when more information (e.g., daily number of tests) is available."}