Improved prediction of new COVID-19 cases using a simple vector autoregressive model: evidence from seven New York state counties.

ABSTRACT: With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and severe acute respiratory syndrome. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback - they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the numb

SUBMITTER: Kitaoka T 

PROVIDER: S-EPMC9893212 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature