PMC:7449695 / 24459-25681 JSONTXT

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T46","span":{"begin":228,"end":238},"obj":"Disease"},{"id":"T47","span":{"begin":374,"end":384},"obj":"Disease"}],"attributes":[{"id":"A46","pred":"mondo_id","subj":"T46","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A47","pred":"mondo_id","subj":"T47","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"}],"text":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T76","span":{"begin":667,"end":668},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T77","span":{"begin":697,"end":698},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T78","span":{"begin":908,"end":909},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T79","span":{"begin":978,"end":979},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T80","span":{"begin":1008,"end":1009},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T81","span":{"begin":1087,"end":1088},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T82","span":{"begin":1155,"end":1163},"obj":"http://purl.obolibrary.org/obo/CLO_0009985"}],"text":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T14","span":{"begin":407,"end":412},"obj":"Chemical"},{"id":"T15","span":{"begin":473,"end":477},"obj":"Chemical"}],"attributes":[{"id":"A14","pred":"chebi_id","subj":"T14","obj":"http://purl.obolibrary.org/obo/CHEBI_24433"},{"id":"A15","pred":"chebi_id","subj":"T15","obj":"http://purl.obolibrary.org/obo/CHEBI_22695"}],"text":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}

{"project":"LitCovid-PubTator","denotations":[{"id":"94","span":{"begin":228,"end":247},"obj":"Disease"},{"id":"95","span":{"begin":534,"end":539},"obj":"Disease"}],"attributes":[{"id":"A94","pred":"tao:has_database_id","subj":"94","obj":"MESH:D003141"},{"id":"A95","pred":"tao:has_database_id","subj":"95","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":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}

{"project":"LitCovid-sentences","denotations":[{"id":"T170","span":{"begin":0,"end":8},"obj":"Sentence"},{"id":"T171","span":{"begin":9,"end":139},"obj":"Sentence"},{"id":"T172","span":{"begin":140,"end":420},"obj":"Sentence"},{"id":"T173","span":{"begin":421,"end":542},"obj":"Sentence"},{"id":"T174","span":{"begin":543,"end":862},"obj":"Sentence"},{"id":"T175","span":{"begin":863,"end":1075},"obj":"Sentence"},{"id":"T176","span":{"begin":1076,"end":1222},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}

{"project":"2_test","denotations":[{"id":"32788039-24043437-27152626","span":{"begin":71,"end":75},"obj":"24043437"}],"text":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}

{"project":"MyTest","denotations":[{"id":"32788039-24043437-27152626","span":{"begin":71,"end":75},"obj":"24043437"}],"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":"Overview\nThe method used to estimate R𝑒𝑓𝑓 is described in Cori et al., 2013, as implemented in the R package, EpiNow (Abbott et al., 2020). This method is currently in development by the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene and Tropical Medicine (London School of Hygiene \u0026 Tropical Medicine Mathematical Modelling of Infectious Diseases nCoV working group, 2020). Full details of their statistical analysis and code base is available via their website (https://epiforecasts.io/covid/).\nThe uncertainty in the R𝑒𝑓𝑓 estimates (shown in Figure 2; Figure 2—figure supplements 1, 2 and 3) represents variability in a population-level average as a result of imperfect data, rather than individual-level heterogeneity in transmission (i.e., the variation in the number of secondary cases generated by each case). This is akin to the variation represented by a confidence interval (i.e., variation in the estimate resulting from a finite sample), rather than a prediction interval (i.e., variation in individual observations).\nWe provide a brief overview of the method and sources of imperfect data below, focusing on how the analysis was adapted to the Australian context."}