AUTHOR=Liu Caifen , Xu Lingfeng , Bai Yuan , Xu Xiaoke , Lau Eric H. Y. , Cowling Benjamin J. , Du Zhanwei 

TITLE=Local Surveillance of the COVID-19 Outbreak

JOURNAL=Frontiers in Physics

VOLUME=10

YEAR=2022

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.824369

DOI=10.3389/fphy.2022.824369

ISSN=2296-424X

ABSTRACT=<p>Given the worldwide pandemic of the novel coronavirus disease 2019 (COVID-19) and its continuing threat brought by the emergence of virus variants, there are great demands for accurate surveillance and monitoring of outbreaks. A valuable metric for assessing the current risk posed by an outbreak is the time-varying reproduction number (<inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>). Several methods have been proposed to estimate <inline-formula id="inf2"><mml:math id="m2" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi>t</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> using different types of data. We developed a new tool that integrated two commonly used approaches into a unified and user-friendly platform for the estimation of time-varying reproduction numbers. This tool allows users to perform simulations and yield real-time tracking of local epidemic of COVID-19 with an R package.</p>