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

Supporting materials for Natalie M. Linton, Andrei R. Akhmetzhanov, Hiroshi Nishiura. Localized end-of-outbreak determination for coronavirus disease 2019 (COVID-19): examples from clusters in Japan, International Journal of Infectious Diseases. 2021, 105: 286-292. doi:10.1016/j.ijid.2021.02.106

Abstract

Objectives End-of-outbreak declarations are an important component of outbreak response as they indicate that public health and social interventions may be relaxed or lapsed. The present study aimed to assess end-of-outbreak probabilities for clusters of coronavirus disease 2019 (COVID-19) cases during the first wave of the pandemic in Japan.

Methods We computed a statistical model for localized end-of-outbreak determination that accounted for the reporting delay for new cases. Four clusters representing different social contexts and time points during the epidemic were selected and their end-of-outbreak probabilities were evaluated.

Results The end-of-outbreak determination was most closely tied to outbreak size. Larger outbreaks (accounting for missing onsets and underascertainment of cases) tended to reach the proscribed probability thresholds for end-of-outbreak determination at later times compared to smaller outbreaks. In addition, end-of-outbreak determination was closely related to estimates of case dispersion k and the effective reproduction number R_e.

Conclusions When public health measures are effective, lower R_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. This application can help distinguish between local extinction and low levels of transmission, and communicating these end-of-outbreak probabilities helps inform public health decision-making around the appropriate use of resources

Code

A. Analysis in R and CmdStan

B. Visualization of the results

Core software and packages

• R 4.0.4
• CmdStan 2.26.0
• cmdstanr 0.3.0

Authors

Name	Twitter	Github
Natalie M. Linton	@nlinton_epi	nlinton
Andrei R. Akhmetzhanov	@aakhmetz	aakhmetz
Hiroshi Nishiura	@nishiurah