Using a physical model and aggregate data to estimate the spreading of Covid-19 in Israel in the presence of waning immunity and competing variants

TL;DR

This paper introduces a physics-based particle model to predict Covid-19 spread in Israel, accounting for waning immunity and multiple variants, providing insights into vaccine effects and variant dynamics.

Contribution

The study presents a novel dynamic particle model rooted in statistical physics that captures the effects of vaccines, waning immunity, and competing variants on Covid-19 spread.

Findings

Model accurately predicts Covid-19 spread and variant prevalence.

Identifies the impact of vaccine waning on infection dynamics.

Analyzes the effects of multiple variants on morbidity.

Abstract

In more than two years since the COVID-19 virus was first detected in China, hundreds of millions of individuals have been infected, and millions have died. Aside from the immediate need for medical solutions (such as vaccines and medications) to treat the epidemic, the Corona pandemic has strengthened the demand for mathematical models that can predict the spread of the pandemic in an ever-changing reality. Here, we present a novel, dynamic particle model based on the basic principles of statistical physics that enables the prediction of the spreading of Covid-19 in the presence of effective vaccines. This particle model enables us to accurately examine the effects of the vaccine on different subgroups of the vaccinated population and the entire population and to identify the vaccine waning. Furthermore, a particle model can predict the prevalence of two competing variants over time and their associated morbidity.