A SEIR model with time-varying coefficients for analysing the SARS-CoV-2 epidemic

TL;DR

This paper introduces a time-varying SEIR model to analyze COVID-19 outbreaks in the US, Italy, and Iceland, assessing how government interventions influence epidemic dynamics using public data.

Contribution

It develops a novel SEIR model with time-dependent parameters and employs a composite likelihood approach to estimate effects of interventions on epidemic curves.

Findings

Restrictive measures flatten epidemic curves

Model estimates show a decrease in cases over time

Interventions significantly impact epidemic progression

Abstract

In this study, we propose a time-dependent Susceptible-Exposed-Infected-Recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States of America, Italy and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.