tetano
Editor, Senior Moderator
Int J Infect Dis
. 2021 Mar 1;S1201-9712(21)00192-2.
doi: 10.1016/j.ijid.2021.02.106. Online ahead of print.
Localized end-of-outbreak determination for coronavirus disease 2019 (COVID-19): examples from clusters in Japan
Natalie M Linton[SUP] 1 [/SUP], Andrei R Akhmetzhanov[SUP] 2 [/SUP], Hiroshi Nishiura[SUP] 3 [/SUP]
Affiliations
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 detected during the first wave of the COVID-19 pandemic in Japan.
Methods: We computed a statistical model for end-of-outbreak determination that accounted for the reporting delay for new cases. 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[SUB]e[/SUB]. Increasing local transmission (R[SUB]e[/SUB]>1) leads to greater uncertainty in the probability estimates.
Conclusions: When public health measures are effective, lowerR[SUB]e[/SUB] (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 relation to the appropriate use of resources.
Keywords: Elimination; Epidemic; Epidemiology; Extinction; Mathematical model; Transmission dynamics.
. 2021 Mar 1;S1201-9712(21)00192-2.
doi: 10.1016/j.ijid.2021.02.106. Online ahead of print.
Localized end-of-outbreak determination for coronavirus disease 2019 (COVID-19): examples from clusters in Japan
Natalie M Linton[SUP] 1 [/SUP], Andrei R Akhmetzhanov[SUP] 2 [/SUP], Hiroshi Nishiura[SUP] 3 [/SUP]
Affiliations
- PMID: 33662600
- 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 detected during the first wave of the COVID-19 pandemic in Japan.
Methods: We computed a statistical model for end-of-outbreak determination that accounted for the reporting delay for new cases. 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[SUB]e[/SUB]. Increasing local transmission (R[SUB]e[/SUB]>1) leads to greater uncertainty in the probability estimates.
Conclusions: When public health measures are effective, lowerR[SUB]e[/SUB] (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 relation to the appropriate use of resources.
Keywords: Elimination; Epidemic; Epidemiology; Extinction; Mathematical model; Transmission dynamics.