PMC:7738161 / 7155-12869 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"57","span":{"begin":570,"end":578},"obj":"Species"},{"id":"58","span":{"begin":651,"end":657},"obj":"Species"},{"id":"59","span":{"begin":66,"end":82},"obj":"Disease"},{"id":"60","span":{"begin":703,"end":723},"obj":"Disease"},{"id":"61","span":{"begin":1002,"end":1010},"obj":"Disease"},{"id":"63","span":{"begin":1263,"end":1279},"obj":"Disease"},{"id":"67","span":{"begin":1715,"end":1717},"obj":"Chemical"},{"id":"68","span":{"begin":1719,"end":1721},"obj":"Chemical"},{"id":"69","span":{"begin":1724,"end":1726},"obj":"Chemical"},{"id":"72","span":{"begin":2319,"end":2325},"obj":"Species"},{"id":"73","span":{"begin":2381,"end":2387},"obj":"Species"},{"id":"75","span":{"begin":3692,"end":3697},"obj":"Disease"},{"id":"77","span":{"begin":5245,"end":5253},"obj":"Disease"}],"attributes":[{"id":"A57","pred":"tao:has_database_id","subj":"57","obj":"Tax:9606"},{"id":"A58","pred":"tao:has_database_id","subj":"58","obj":"Tax:9606"},{"id":"A59","pred":"tao:has_database_id","subj":"59","obj":"OMIM:176500"},{"id":"A60","pred":"tao:has_database_id","subj":"60","obj":"MESH:D012818"},{"id":"A61","pred":"tao:has_database_id","subj":"61","obj":"MESH:D007239"},{"id":"A63","pred":"tao:has_database_id","subj":"63","obj":"OMIM:176500"},{"id":"A67","pred":"tao:has_database_id","subj":"67","obj":"MESH:D014529"},{"id":"A68","pred":"tao:has_database_id","subj":"68","obj":"MESH:D014529"},{"id":"A72","pred":"tao:has_database_id","subj":"72","obj":"Tax:9606"},{"id":"A73","pred":"tao:has_database_id","subj":"73","obj":"Tax:9606"},{"id":"A75","pred":"tao:has_database_id","subj":"75","obj":"MESH:C000657245"},{"id":"A77","pred":"tao:has_database_id","subj":"77","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":"Data\nWe fit the physical distancing model to case-count data from British Columbia from March 1, 2020 (when a total of eight cases had been detected in the province) to April 11, 2020 at which time 1445 cases had been confirmed. These data are available in press releases from the BC Centre for Disease Control (BCCDC) [10], from the public data dashboard [17], and from the code repository associated with this paper. Testing procedures were adapted over the course of the outbreak. In particular, lab testing criteria were changed on March 16 to focus on hospitalized patients, healthcare workers, long-term care facility residents/staff, and those people part of an existing cluster and experiencing respiratory symptoms. This led to high variability in case counts in the surrounding days with some large jumps in the number of identified cases. We accounted for this in the model by adjusting the testing fraction ψr to accommodate widening the testing pool and thereby increasing the fraction of infected individuals being tested (Table 1). There was also variability in the daily testing rate. During March, the daily number of completed tests ranged from approximately 100 to 3500, and did not strictly increase over time.\nTable 1 Values and sources for British Columbia parameterization of the model (see Supplemental Methods and Table B in S1 Text for other jurisdictions).\nThe duration of the infectious period is shorter than the duration of severe illness, accounting for self-isolation and less severe illnesses. The quarantine parameter q reflects approximately 1/5 of severe cases either ceasing to transmit due to hospitalization or completely self-isolating. The model depends on the combination ur/(ur + ud), the fraction engaged in physical distancing, estimated from the survey data cited above. The testing patterns have changed over time, with laboratories increasing the numbers of tests on approximately March 14 (motivating our change in ψr).\nSymbol Definition Specified/fitted value Justification\nN Population size 5,100,000 [23]\nD Mean duration of the infectious period 5 days [24, 25]\nk 1 (time to infectiousness)−1 (E1 to E2) 0.2 days−1 [26–28]\nk 2 (time period of pre-symptomatic transmissibility)−1 (E2 to I) 1 days−1 [27, 28]\nq Quarantine rate 0.05 [29]\nu d Rate of people moving to physical distancing 0.1 [20]\nu r Rate of people returning from physical distancing 0.02 [20]\nψ r Proportion of anticipated cases on day r that are tested and reported 0.1 Before March 14\n0.3 On and after March 14\nShape Weibull parameter in delay-to-reporting distribution 1.73 (1.60–1.86 95% CI) Fit to data from Fig 1B\nScale Weibull parameter in delay-to-reporting distribution 9.85 (9.30–10.46 95% CI) Fit to data from Fig 1B\nR 0b Basic reproductive number 2.95 (2.88–3.02 95% CI) Fit to data from Fig 1C\nf 2 Fraction of normal contacts during physical distancing 0.22 (0.08– 0.36 95% CI) Fit to data from Fig 1C\nϕ Inverse dispersion from negative binomial (NB2) observation model 6.86 (3.39–12.37 95% CI) Fit to data from Fig 1C For some confirmed cases in BC, estimates of the date of symptom onset are available (Fig 1). We used the delays between symptom onset and cases being reported to parameterize the physical distancing model. In this case-specific data set there were only seven cases reported before February 29, and a decline in reported cases after April 2 (Fig 1). Therefore we used only the 535 cases in the case-specific data that were reported between these dates to parameterize the delay part of the model. For California (CA), New York (NY), and Florida (FL) we used reported case and testing data from The COVID Tracking Project, and assumed a BC-like delay between symptom onset and case reporting. For New Zealand (NZ) we used reported cases from the NZ Ministry of Health [18], and A. Lustig and M. Plank (pers. comm.) fit the delay distribution to NZ case-reporting data using our package [19], since these data are not publicly available (see Supplemental Methods in S1 Text).\nFor our main analysis with BC, we fit our model to data until April 11, 2020. When demonstrating the application of our model to other jurisdictions, we included data until May 6 or 7, 2020, the date we completed this portion of the analysis and before these jurisdictions had begun relaxing physical distancing measures. An outbreak at a poultry plant in BC, combined with an expansion of testing in mid April, meant that in order to extend the BC model to May 6 or 7, we would have had to introduce changes to the methodology that would not be straightforward and would not be possible in other jurisdictions. These include, for example, modelling the poultry workforce’s interventions and contacts and the links between the outbreak and general community transmission, in concert with differential testing. We therefore limited the data used for the BC model to April 11.\nWe motivated the structure of our model based on a survey conducted by the Angus Reid Institute to examine how physical distancing measures changed behaviour in Canada (March 20–23, 2020; n = 1664; [20]). Responses indicated that there was a subset of the population believing that the response to the COVID-19 epidemic was “overblown”, who were less willing than others to engage in distancing behaviours. This motivated treating the distancing and non-distancing compartments of our model separately and assuming that ∼80% of individuals were able and willing to physically distance. We used the timing of known government interventions to inform the timing of behavioural changes, and verified these dates against publicly available mobility data for each region [21]."}

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These data are available in press releases from the BC Centre for Disease Control (BCCDC) [10], from the public data dashboard [17], and from the code repository associated with this paper. Testing procedures were adapted over the course of the outbreak. In particular, lab testing criteria were changed on March 16 to focus on hospitalized patients, healthcare workers, long-term care facility residents/staff, and those people part of an existing cluster and experiencing respiratory symptoms. This led to high variability in case counts in the surrounding days with some large jumps in the number of identified cases. We accounted for this in the model by adjusting the testing fraction ψr to accommodate widening the testing pool and thereby increasing the fraction of infected individuals being tested (Table 1). There was also variability in the daily testing rate. During March, the daily number of completed tests ranged from approximately 100 to 3500, and did not strictly increase over time.\nTable 1 Values and sources for British Columbia parameterization of the model (see Supplemental Methods and Table B in S1 Text for other jurisdictions).\nThe duration of the infectious period is shorter than the duration of severe illness, accounting for self-isolation and less severe illnesses. The quarantine parameter q reflects approximately 1/5 of severe cases either ceasing to transmit due to hospitalization or completely self-isolating. The model depends on the combination ur/(ur + ud), the fraction engaged in physical distancing, estimated from the survey data cited above. The testing patterns have changed over time, with laboratories increasing the numbers of tests on approximately March 14 (motivating our change in ψr).\nSymbol Definition Specified/fitted value Justification\nN Population size 5,100,000 [23]\nD Mean duration of the infectious period 5 days [24, 25]\nk 1 (time to infectiousness)−1 (E1 to E2) 0.2 days−1 [26–28]\nk 2 (time period of pre-symptomatic transmissibility)−1 (E2 to I) 1 days−1 [27, 28]\nq Quarantine rate 0.05 [29]\nu d Rate of people moving to physical distancing 0.1 [20]\nu r Rate of people returning from physical distancing 0.02 [20]\nψ r Proportion of anticipated cases on day r that are tested and reported 0.1 Before March 14\n0.3 On and after March 14\nShape Weibull parameter in delay-to-reporting distribution 1.73 (1.60–1.86 95% CI) Fit to data from Fig 1B\nScale Weibull parameter in delay-to-reporting distribution 9.85 (9.30–10.46 95% CI) Fit to data from Fig 1B\nR 0b Basic reproductive number 2.95 (2.88–3.02 95% CI) Fit to data from Fig 1C\nf 2 Fraction of normal contacts during physical distancing 0.22 (0.08– 0.36 95% CI) Fit to data from Fig 1C\nϕ Inverse dispersion from negative binomial (NB2) observation model 6.86 (3.39–12.37 95% CI) Fit to data from Fig 1C For some confirmed cases in BC, estimates of the date of symptom onset are available (Fig 1). We used the delays between symptom onset and cases being reported to parameterize the physical distancing model. In this case-specific data set there were only seven cases reported before February 29, and a decline in reported cases after April 2 (Fig 1). Therefore we used only the 535 cases in the case-specific data that were reported between these dates to parameterize the delay part of the model. For California (CA), New York (NY), and Florida (FL) we used reported case and testing data from The COVID Tracking Project, and assumed a BC-like delay between symptom onset and case reporting. For New Zealand (NZ) we used reported cases from the NZ Ministry of Health [18], and A. Lustig and M. Plank (pers. comm.) fit the delay distribution to NZ case-reporting data using our package [19], since these data are not publicly available (see Supplemental Methods in S1 Text).\nFor our main analysis with BC, we fit our model to data until April 11, 2020. When demonstrating the application of our model to other jurisdictions, we included data until May 6 or 7, 2020, the date we completed this portion of the analysis and before these jurisdictions had begun relaxing physical distancing measures. An outbreak at a poultry plant in BC, combined with an expansion of testing in mid April, meant that in order to extend the BC model to May 6 or 7, we would have had to introduce changes to the methodology that would not be straightforward and would not be possible in other jurisdictions. These include, for example, modelling the poultry workforce’s interventions and contacts and the links between the outbreak and general community transmission, in concert with differential testing. We therefore limited the data used for the BC model to April 11.\nWe motivated the structure of our model based on a survey conducted by the Angus Reid Institute to examine how physical distancing measures changed behaviour in Canada (March 20–23, 2020; n = 1664; [20]). Responses indicated that there was a subset of the population believing that the response to the COVID-19 epidemic was “overblown”, who were less willing than others to engage in distancing behaviours. This motivated treating the distancing and non-distancing compartments of our model separately and assuming that ∼80% of individuals were able and willing to physically distance. We used the timing of known government interventions to inform the timing of behavioural changes, and verified these dates against publicly available mobility data for each region [21]."}