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Quantifying transmissibility of SARS-CoV-2 and impact of intervention within long-term healthcare facilities.
Stockdale, Jessica E; Anderson, Sean C; Edwards, Andrew M; Iyaniwura, Sarafa A; Mulberry, Nicola; Otterstatter, Michael C; Janjua, Naveed Z; Coombs, Daniel; Colijn, Caroline; Irvine, Michael A.
  • Stockdale JE; Department of Mathematics, Simon Fraser University, Burnaby, Canada.
  • Anderson SC; Department of Mathematics, Simon Fraser University, Burnaby, Canada.
  • Edwards AM; Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada.
  • Iyaniwura SA; Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada.
  • Mulberry N; Department of Biology, University of Victoria, Victoria, Canada.
  • Otterstatter MC; Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, Vancouver, Canada.
  • Janjua NZ; British Columbia Centre for Disease Control, Vancouver, Canada.
  • Coombs D; Department of Mathematics, Simon Fraser University, Burnaby, Canada.
  • Colijn C; School of Population and Public Health, University of British Columbia, Vancouver, Canada.
  • Irvine MA; British Columbia Centre for Disease Control, Vancouver, Canada.
R Soc Open Sci ; 9(1): 211710, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1626952
ABSTRACT
Estimates of the basic reproduction number (R 0) for COVID-19 are particularly variable in the context of transmission within locations such as long-term healthcare (LTHC) facilities. We sought to characterize the heterogeneity of R 0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that occurred within LTHC facilities in British Columbia, Canada as of 21 September 2020. We estimated R 0 in 18 LTHC outbreaks with a novel Bayesian hierarchical dynamic model of susceptible, exposed, infected and recovered individuals, incorporating heterogeneity of R 0 between facilities. We further compared these estimates to those obtained with standard methods that use the exponential growth rate and maximum likelihood. The total size of outbreaks varied dramatically, with range of attack rates 2%-86%. The Bayesian analysis provided an overall estimate of R 0 = 2.51 (90% credible interval 0.47-9.0), with individual facility estimates ranging between 0.56 and 9.17. Uncertainty in these estimates was more constrained than standard methods, particularly for smaller outbreaks informed by the population-level model. We further estimated that intervention led to 61% (52%-69%) of all potential cases being averted within the LTHC facilities, or 75% (68%-79%) when using a model with multi-level intervention effect. Understanding of transmission risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: R Soc Open Sci Year: 2022 Document Type: Article Affiliation country: Rsos.211710

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Language: English Journal: R Soc Open Sci Year: 2022 Document Type: Article Affiliation country: Rsos.211710