Revisiting classical SIR modelling in light of the COVID-19 pandemic.

ABSTRACT:

Background

Classical infectious disease models during epidemics have widespread usage, from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses. However, it is important to correctly classify reported data and understand how this impacts estimation of model parameters. The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions, as well as how we think about classical infectious disease modelling paradigms.

Objective

We aim to assess the appropriateness of model parameter estimates and prediction results in classical infectious disease compartmental modelling frameworks given available data types (infected, active, quarantined

SUBMITTER: Kalachev L 

PROVIDER: S-EPMC9755423 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature