Continuous-time stochastic processes for the spread of COVID-19 disease simulated via a Monte Carlo approach and comparison with deterministic models

TL;DR

This paper introduces stochastic models for COVID-19 spread, compares them with deterministic models, and uses Monte Carlo simulations to analyze effects of interventions like social distancing, providing insights for policy decisions.

Contribution

It develops novel stochastic models with compartments including asymptomatic and isolated individuals, and compares their predictions with deterministic models using Monte Carlo simulations.

Findings

Stochastic models capture variability in disease spread.

Monte Carlo simulations provide detailed scenario analysis.

Results suggest herd immunity strategies are risky.

Abstract

Two stochastic models are proposed to describe the evolution of the COVID-19 pandemic. In the first model the population is partitioned into four compartments: susceptible $S$, infected $I$, removed $R$ and dead people $D$. In order to have a cross validation, a deterministic version of such a model is also devised which is represented by a system of ordinary differential equations with delays. In the second stochastic model two further compartments are added: the class $A$ of asymptomatic individuals and the class $L$ of isolated infected people. Effects such as social distancing measures are easily included and the consequences are analyzed. Numerical solutions are obtained with Monte Carlo simulations. Quantitative predictions are provided which can be useful for the evaluation of political measures, e.g. the obtained results suggest that strategies based on herd immunity are too risky.