Modeling and Simulation of the spread of coronavirus disease (COVID-19) in Lebanon

TL;DR

This paper presents a probabilistic model for COVID-19 spread in Lebanon, accounting for undetected cases, social factors, and border influx, aiding policy decisions and understanding of transmission dynamics.

Contribution

It introduces a new probabilistic model that incorporates undetected cases, social structure, and border effects, tailored for Lebanon's COVID-19 data.

Findings

Model accurately fits Lebanon's COVID-19 data

Simulations inform policy on easing restrictions

Identifies key factors influencing virus spread

Abstract

In this paper, we develop a probabilistic mathematical model for the spread of coronavirus disease (COVID-19). It takes into account the known special characteristics of this disease such as the existence of infectious undetected cases and the different social and infectiousness conditions of infected people. In particular, it considers the social structure and governmental measures in a country, the fraction of detected cases over the real total infected cases, and the influx of undetected infected people from outside the borders. Although the model is simple and allows a reasonable identification of its parameters, using the data provided by local authorities on this pandemic, it is also complex enough to capture the most important effects. We study the particular case of Lebanon and use its reported data to estimate the model parameters, which can be of interest for estimating the spread of COVID-19 in other countries. We show a good agreement between the reported data and the estimations given by our model. We also simulate several scenarios that help policy makers in deciding how to loosen different measures without risking a severe wave of COVID-19. We are also able to identify the main factors that lead to specific scenarios which helps in a better understanding of the spread of the virus.