PMC:7001239 / 10676-13242 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"161","span":{"begin":864,"end":872},"obj":"Disease"},{"id":"169","span":{"begin":1600,"end":1609},"obj":"Species"},{"id":"170","span":{"begin":1775,"end":1780},"obj":"Species"},{"id":"171","span":{"begin":1960,"end":1965},"obj":"Species"},{"id":"172","span":{"begin":1969,"end":1974},"obj":"Species"},{"id":"173","span":{"begin":1995,"end":2004},"obj":"Species"},{"id":"174","span":{"begin":2165,"end":2178},"obj":"Species"},{"id":"175","span":{"begin":1701,"end":1709},"obj":"Disease"}],"attributes":[{"id":"A161","pred":"tao:has_database_id","subj":"161","obj":"MESH:D007239"},{"id":"A169","pred":"tao:has_database_id","subj":"169","obj":"Tax:2697049"},{"id":"A170","pred":"tao:has_database_id","subj":"170","obj":"Tax:9606"},{"id":"A171","pred":"tao:has_database_id","subj":"171","obj":"Tax:9606"},{"id":"A172","pred":"tao:has_database_id","subj":"172","obj":"Tax:9606"},{"id":"A173","pred":"tao:has_database_id","subj":"173","obj":"Tax:2697049"},{"id":"A174","pred":"tao:has_database_id","subj":"174","obj":"Tax:11118"},{"id":"A175","pred":"tao:has_database_id","subj":"175","obj":"MESH:D015047"}],"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":"Strengths and limitations\nThe scarcity of available data, especially on case counts by date of disease onset as well as contact tracing, greatly limits the precision of our estimates and does not yet allow for reliable forecasts of epidemic spread. Case counts provided by local authorities in the early stage of an emerging epidemic are notoriously unreliable as reporting rates are unstable and vary with time. This is due to many factors such as the initial lack of proper diagnosis tools, the focus on the more severe cases or the overcrowding of hospitals. We avoided this surveillance bias by relying on an indirect estimate of epidemic size on 18 January, based on cases identified in foreign countries before quarantine measures were implemented on 23 January. This estimated range of epidemic size relies itself on several assumptions, including that all infected individuals who travelled from Wuhan to other countries have been detected [6]. This caveat may lead to an underestimation of transmissibility, especially considering the recent reports about asymptomatic cases [4]. Conversely, our results do not depend on any assumption about the existence of asymptomatic transmission, and only reflect the possible combinations of transmission events that lead to the situation on 18 January.\nOur analysis, while limited because of the scarcity of data, has two important strengths. Firstly, it is based on the simulation of a wide range of possibilities regarding epidemic parameters and allows for the full propagation on the final estimates of the many remaining uncertainties regarding 2019-nCoV and the situation in Wuhan: on the actual size of the epidemic, on the size of the initial zoonotic event at the wet market, on the date(s) of the initial animal-to-human transmission event(s) and on the generation time interval. As it accounts for all these uncertainties, our analysis provides a summary of the current state of knowledge about the human-to-human transmissibility of 2019-nCoV. Secondly, its focus on the possibility of superspreading events by using negative-binomial offspring distributions appears relevant in the context of emerging coronaviruses [7,8]. While our estimate of k remains imprecise, the simulations suggest that very low values of k \u003c 0.1 are less likely than higher values \u003c 0.1 that correspond to a more homogeneous transmission pattern. However, values of k in the range of 0.1–0.2 are still compatible with a small risk of occurrence of large superspreading events, especially impactful in hospital settings [15,16]."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T95","span":{"begin":497,"end":502},"obj":"http://purl.obolibrary.org/obo/CLO_0009985"},{"id":"T96","span":{"begin":651,"end":653},"obj":"http://purl.obolibrary.org/obo/CLO_0050510"},{"id":"T97","span":{"begin":1291,"end":1293},"obj":"http://purl.obolibrary.org/obo/CLO_0050510"},{"id":"T98","span":{"begin":1364,"end":1367},"obj":"http://purl.obolibrary.org/obo/CLO_0051582"},{"id":"T99","span":{"begin":1435,"end":1436},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T100","span":{"begin":1765,"end":1771},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_33208"},{"id":"T101","span":{"begin":1775,"end":1780},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T102","span":{"begin":1906,"end":1907},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T103","span":{"begin":1960,"end":1965},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T104","span":{"begin":1969,"end":1974},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T105","span":{"begin":2020,"end":2025},"obj":"http://purl.obolibrary.org/obo/CLO_0009985"},{"id":"T106","span":{"begin":2345,"end":2346},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T107","span":{"begin":2457,"end":2458},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Strengths and limitations\nThe scarcity of available data, especially on case counts by date of disease onset as well as contact tracing, greatly limits the precision of our estimates and does not yet allow for reliable forecasts of epidemic spread. Case counts provided by local authorities in the early stage of an emerging epidemic are notoriously unreliable as reporting rates are unstable and vary with time. This is due to many factors such as the initial lack of proper diagnosis tools, the focus on the more severe cases or the overcrowding of hospitals. We avoided this surveillance bias by relying on an indirect estimate of epidemic size on 18 January, based on cases identified in foreign countries before quarantine measures were implemented on 23 January. This estimated range of epidemic size relies itself on several assumptions, including that all infected individuals who travelled from Wuhan to other countries have been detected [6]. This caveat may lead to an underestimation of transmissibility, especially considering the recent reports about asymptomatic cases [4]. Conversely, our results do not depend on any assumption about the existence of asymptomatic transmission, and only reflect the possible combinations of transmission events that lead to the situation on 18 January.\nOur analysis, while limited because of the scarcity of data, has two important strengths. Firstly, it is based on the simulation of a wide range of possibilities regarding epidemic parameters and allows for the full propagation on the final estimates of the many remaining uncertainties regarding 2019-nCoV and the situation in Wuhan: on the actual size of the epidemic, on the size of the initial zoonotic event at the wet market, on the date(s) of the initial animal-to-human transmission event(s) and on the generation time interval. As it accounts for all these uncertainties, our analysis provides a summary of the current state of knowledge about the human-to-human transmissibility of 2019-nCoV. Secondly, its focus on the possibility of superspreading events by using negative-binomial offspring distributions appears relevant in the context of emerging coronaviruses [7,8]. While our estimate of k remains imprecise, the simulations suggest that very low values of k \u003c 0.1 are less likely than higher values \u003c 0.1 that correspond to a more homogeneous transmission pattern. However, values of k in the range of 0.1–0.2 are still compatible with a small risk of occurrence of large superspreading events, especially impactful in hospital settings [15,16]."}

{"project":"LitCovid-sentences","denotations":[{"id":"T80","span":{"begin":0,"end":25},"obj":"Sentence"},{"id":"T81","span":{"begin":26,"end":248},"obj":"Sentence"},{"id":"T82","span":{"begin":249,"end":412},"obj":"Sentence"},{"id":"T83","span":{"begin":413,"end":561},"obj":"Sentence"},{"id":"T84","span":{"begin":562,"end":768},"obj":"Sentence"},{"id":"T85","span":{"begin":769,"end":952},"obj":"Sentence"},{"id":"T86","span":{"begin":953,"end":1088},"obj":"Sentence"},{"id":"T87","span":{"begin":1089,"end":1302},"obj":"Sentence"},{"id":"T88","span":{"begin":1303,"end":1392},"obj":"Sentence"},{"id":"T89","span":{"begin":1393,"end":1839},"obj":"Sentence"},{"id":"T90","span":{"begin":1840,"end":2005},"obj":"Sentence"},{"id":"T91","span":{"begin":2006,"end":2185},"obj":"Sentence"},{"id":"T92","span":{"begin":2186,"end":2385},"obj":"Sentence"},{"id":"T93","span":{"begin":2386,"end":2566},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Strengths and limitations\nThe scarcity of available data, especially on case counts by date of disease onset as well as contact tracing, greatly limits the precision of our estimates and does not yet allow for reliable forecasts of epidemic spread. Case counts provided by local authorities in the early stage of an emerging epidemic are notoriously unreliable as reporting rates are unstable and vary with time. This is due to many factors such as the initial lack of proper diagnosis tools, the focus on the more severe cases or the overcrowding of hospitals. We avoided this surveillance bias by relying on an indirect estimate of epidemic size on 18 January, based on cases identified in foreign countries before quarantine measures were implemented on 23 January. This estimated range of epidemic size relies itself on several assumptions, including that all infected individuals who travelled from Wuhan to other countries have been detected [6]. This caveat may lead to an underestimation of transmissibility, especially considering the recent reports about asymptomatic cases [4]. Conversely, our results do not depend on any assumption about the existence of asymptomatic transmission, and only reflect the possible combinations of transmission events that lead to the situation on 18 January.\nOur analysis, while limited because of the scarcity of data, has two important strengths. Firstly, it is based on the simulation of a wide range of possibilities regarding epidemic parameters and allows for the full propagation on the final estimates of the many remaining uncertainties regarding 2019-nCoV and the situation in Wuhan: on the actual size of the epidemic, on the size of the initial zoonotic event at the wet market, on the date(s) of the initial animal-to-human transmission event(s) and on the generation time interval. As it accounts for all these uncertainties, our analysis provides a summary of the current state of knowledge about the human-to-human transmissibility of 2019-nCoV. Secondly, its focus on the possibility of superspreading events by using negative-binomial offspring distributions appears relevant in the context of emerging coronaviruses [7,8]. While our estimate of k remains imprecise, the simulations suggest that very low values of k \u003c 0.1 are less likely than higher values \u003c 0.1 that correspond to a more homogeneous transmission pattern. However, values of k in the range of 0.1–0.2 are still compatible with a small risk of occurrence of large superspreading events, especially impactful in hospital settings [15,16]."}

{"project":"2_test","denotations":[{"id":"32019669-31986261-29338358","span":{"begin":1085,"end":1086},"obj":"31986261"},{"id":"32019669-16292310-29338359","span":{"begin":2180,"end":2181},"obj":"16292310"},{"id":"32019669-25932579-29338360","span":{"begin":2182,"end":2183},"obj":"25932579"},{"id":"32019669-26539018-29338361","span":{"begin":2559,"end":2561},"obj":"26539018"},{"id":"32019669-23782161-29338362","span":{"begin":2562,"end":2564},"obj":"23782161"}],"text":"Strengths and limitations\nThe scarcity of available data, especially on case counts by date of disease onset as well as contact tracing, greatly limits the precision of our estimates and does not yet allow for reliable forecasts of epidemic spread. Case counts provided by local authorities in the early stage of an emerging epidemic are notoriously unreliable as reporting rates are unstable and vary with time. This is due to many factors such as the initial lack of proper diagnosis tools, the focus on the more severe cases or the overcrowding of hospitals. We avoided this surveillance bias by relying on an indirect estimate of epidemic size on 18 January, based on cases identified in foreign countries before quarantine measures were implemented on 23 January. This estimated range of epidemic size relies itself on several assumptions, including that all infected individuals who travelled from Wuhan to other countries have been detected [6]. This caveat may lead to an underestimation of transmissibility, especially considering the recent reports about asymptomatic cases [4]. Conversely, our results do not depend on any assumption about the existence of asymptomatic transmission, and only reflect the possible combinations of transmission events that lead to the situation on 18 January.\nOur analysis, while limited because of the scarcity of data, has two important strengths. Firstly, it is based on the simulation of a wide range of possibilities regarding epidemic parameters and allows for the full propagation on the final estimates of the many remaining uncertainties regarding 2019-nCoV and the situation in Wuhan: on the actual size of the epidemic, on the size of the initial zoonotic event at the wet market, on the date(s) of the initial animal-to-human transmission event(s) and on the generation time interval. As it accounts for all these uncertainties, our analysis provides a summary of the current state of knowledge about the human-to-human transmissibility of 2019-nCoV. Secondly, its focus on the possibility of superspreading events by using negative-binomial offspring distributions appears relevant in the context of emerging coronaviruses [7,8]. While our estimate of k remains imprecise, the simulations suggest that very low values of k \u003c 0.1 are less likely than higher values \u003c 0.1 that correspond to a more homogeneous transmission pattern. However, values of k in the range of 0.1–0.2 are still compatible with a small risk of occurrence of large superspreading events, especially impactful in hospital settings [15,16]."}

{"project":"MyTest","denotations":[{"id":"32019669-31986261-29338358","span":{"begin":1085,"end":1086},"obj":"31986261"},{"id":"32019669-16292310-29338359","span":{"begin":2180,"end":2181},"obj":"16292310"},{"id":"32019669-25932579-29338360","span":{"begin":2182,"end":2183},"obj":"25932579"},{"id":"32019669-26539018-29338361","span":{"begin":2559,"end":2561},"obj":"26539018"},{"id":"32019669-23782161-29338362","span":{"begin":2562,"end":2564},"obj":"23782161"}],"namespaces":[{"prefix":"_base","uri":"https://www.uniprot.org/uniprot/testbase"},{"prefix":"UniProtKB","uri":"https://www.uniprot.org/uniprot/"},{"prefix":"uniprot","uri":"https://www.uniprot.org/uniprotkb/"}],"text":"Strengths and limitations\nThe scarcity of available data, especially on case counts by date of disease onset as well as contact tracing, greatly limits the precision of our estimates and does not yet allow for reliable forecasts of epidemic spread. Case counts provided by local authorities in the early stage of an emerging epidemic are notoriously unreliable as reporting rates are unstable and vary with time. This is due to many factors such as the initial lack of proper diagnosis tools, the focus on the more severe cases or the overcrowding of hospitals. We avoided this surveillance bias by relying on an indirect estimate of epidemic size on 18 January, based on cases identified in foreign countries before quarantine measures were implemented on 23 January. This estimated range of epidemic size relies itself on several assumptions, including that all infected individuals who travelled from Wuhan to other countries have been detected [6]. This caveat may lead to an underestimation of transmissibility, especially considering the recent reports about asymptomatic cases [4]. Conversely, our results do not depend on any assumption about the existence of asymptomatic transmission, and only reflect the possible combinations of transmission events that lead to the situation on 18 January.\nOur analysis, while limited because of the scarcity of data, has two important strengths. Firstly, it is based on the simulation of a wide range of possibilities regarding epidemic parameters and allows for the full propagation on the final estimates of the many remaining uncertainties regarding 2019-nCoV and the situation in Wuhan: on the actual size of the epidemic, on the size of the initial zoonotic event at the wet market, on the date(s) of the initial animal-to-human transmission event(s) and on the generation time interval. As it accounts for all these uncertainties, our analysis provides a summary of the current state of knowledge about the human-to-human transmissibility of 2019-nCoV. Secondly, its focus on the possibility of superspreading events by using negative-binomial offspring distributions appears relevant in the context of emerging coronaviruses [7,8]. While our estimate of k remains imprecise, the simulations suggest that very low values of k \u003c 0.1 are less likely than higher values \u003c 0.1 that correspond to a more homogeneous transmission pattern. However, values of k in the range of 0.1–0.2 are still compatible with a small risk of occurrence of large superspreading events, especially impactful in hospital settings [15,16]."}