Localized end-of-outbreak determination for coronavirus disease 2019 (COVID-19): examples from clusters in Japan

• Published in: International journal of infectious diseases Vol. 105; pp. 286 - 292
• Main Authors: Linton, Natalie M., Akhmetzhanov, Andrei R., Nishiura, Hiroshi
• Format: Journal Article
• Language: English
• Published: Canada Elsevier Ltd 01-04-2021; The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases; Elsevier
• Subjects: Basic Reproduction Number; COVID-19 - epidemiology; Disease Hotspot; elimination; epidemic; epidemiology; extinction; Humans; Japan - epidemiology; mathematical model; Models, Statistical; Probability; Public Health; SARS-CoV-2; transmission dynamics; Japan; mathematical model; epidemiology; extinction; elimination; epidemic; transmission dynamics
• ISSN: 1201-9712; 1878-3511
• DOI: 10.1016/j.ijid.2021.02.106

•Adjusting for reporting delays, end-of-outbreak probabilities were computed for four clusters of COVID-19 in Japan.•End-of-outbreak can be declared with greater confidence when public health measures are effective, with lower Re and larger dispersion.•Communicating end-of-outbreak probabilities helps inform public health decision making regarding the appropriate use of resources.
End-of-outbreak declarations are an important component of outbreak response because they indicate that public health and social interventions may be relaxed or lapsed. Our study aimed to assess end-of-outbreak probabilities for clusters of coronavirus disease 2019 (COVID-19) cases detected during the first wave of the COVID-19 pandemic in Japan.
A statistical model for end-of-outbreak determination, which accounted for reporting delays for new cases, was computed. Four clusters, representing different social contexts and time points during the first wave of the epidemic, were selected and their end-of-outbreak probabilities were evaluated.
The speed of end-of-outbreak determination was most closely tied to outbreak size. Notably, accounting underascertainment of cases led to later end-of-outbreak determinations. In addition, end-of-outbreak determination was closely related to estimates of case dispersionk and the effective reproduction number Re. Increasing local transmission (Re>1) leads to greater uncertainty in the probability estimates.
When public health measures are effective, lowerRe (less transmission on average) and larger k (lower risk of superspreading) will be in effect, and end-of-outbreak determinations can be declared with greater confidence. The application of end-of-outbreak probabilities can help distinguish between local extinction and low levels of transmission, and communicating these end-of-outbreak probabilities can help inform public health decision making with regard to the appropriate use of resources.