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An exact method for quantifying the reliability of end-of-epidemic declarations in real time.
Parag, Kris V; Donnelly, Christl A; Jha, Rahul; Thompson, Robin N.
  • Parag KV; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • Donnelly CA; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • Jha R; Department of Statistics, University of Oxford, Oxford, UK.
  • Thompson RN; Department of Applied Math and Theoretical Physics, University of Cambridge, Cambridge, UK.
PLoS Comput Biol ; 16(11): e1008478, 2020 11.
Article in English | MEDLINE | ID: covidwho-962641
ABSTRACT
We derive and validate a novel and analytic method for estimating the probability that an epidemic has been eliminated (i.e. that no future local cases will emerge) in real time. When this probability crosses 0.95 an outbreak can be declared over with 95% confidence. Our method is easy to compute, only requires knowledge of the incidence curve and the serial interval distribution, and evaluates the statistical lifetime of the outbreak of interest. Using this approach, we show how the time-varying under-reporting of infected cases will artificially inflate the inferred probability of elimination, leading to premature (false-positive) end-of-epidemic declarations. Contrastingly, we prove that incorrectly identifying imported cases as local will deceptively decrease this probability, resulting in delayed (false-negative) declarations. Failing to sustain intensive surveillance during the later phases of an epidemic can therefore substantially mislead policymakers on when it is safe to remove travel bans or relax quarantine and social distancing advisories. World Health Organisation guidelines recommend fixed (though disease-specific) waiting times for end-of-epidemic declarations that cannot accommodate these variations. Consequently, there is an unequivocal need for more active and specialised metrics for reliably identifying the conclusion of an epidemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2020 Document Type: Article Affiliation country: Journal.pcbi.1008478

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics Type of study: Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2020 Document Type: Article Affiliation country: Journal.pcbi.1008478