Second quantization approach to COVID-19 epidemic

TL;DR

This paper introduces a quantum field theory-inspired method to model COVID-19 spread, incorporating social measures and matching real epidemic data.

Contribution

It applies second quantization techniques to derive a stochastic SIR model with time-dependent infection rates for COVID-19.

Findings

Model accurately fits data from three epidemic waves in South Korea.

Incorporates lockdown and social distancing effects via time-dependent infection rates.

Provides a novel theoretical framework for epidemic modeling.

Abstract

We show how the standard field theoretical language based on creation and annihilation operators may be used for a straightforward derivation of an SIR-type stochastic model for COVID-19 epidemic, from which we obtain the time evolution of the mean number of infectious (active cases) and deceased individuals. In order to capture the effects of lockdown and social distancing, we use a time-dependent infection rate. The results are in good agreement with the data for three different waves of epidemic activity in South Korea.