Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies.

ABSTRACT: Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We propose a robust and parsimonious survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (Rt ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate and quantify uncertainty by permutation. In China and South Korea, we predicted the entire dise

SUBMITTER: Wang Q 

PROVIDER: S-EPMC7273280 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature