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

Abstract

Objectives: 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.

Methods: 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.

Results: 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 R_e. Increasing local transmission (R_e>1) leads to greater uncertainty in the probability estimates.

Conclusions: When public health measures are effective, lowerR_e (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.

Keywords: elimination; epidemic; epidemiology; extinction; mathematical model; transmission dynamics.

Figure 1

End-of-outbreak probabilities for four coronavirus disease 2019 (COVID-19) case clusters in Japan. Each subfigure begins on the last date of onset within the cluster. All plots assume $R_{\text{e}}=0.5$ and $k$ = 0.25. Lines are median values and shaded areas are 95% credible intervals (CrI) for the datasets. Purple represents the datasets including only reported dates of onset; indigo represents the datasets including imputed dates of onset; yellow represents the datasets accounting for 20% underascertainment of cases; green represents the datasets accounting for 50% underascertainment of cases. The horizontal line represents the threshold for 5% probability of failure of the model. Cumulative case counts over time for each cluster are shown in the inset figures.