# Modeling and Simulation of the Role of Mass Testing in Controlling COVID-19

## TL;DR

This study uses a model to show how mass testing, combined with isolation strategies, can help control the spread of COVID-19, with different approaches needed based on population structure.

## Contribution

The study introduces an age-stratified model to evaluate how mass testing and isolation strategies affect the pandemic's reproduction number and control.

## Key findings

- Mass testing combined with isolation is crucial for reducing virus spread in countries with high elderly-young cohabitation.
- Aged and least developed countries can control the pandemic with fewer tests due to different demographic structures.
- Identifying asymptomatic cases is critical for optimal epidemic control.

## Abstract

This study explores the role of mass testing in controlling the COVID-19 pandemic using an age-stratified compartmental model. The model evaluates the impact of different testing strategies on the pandemic’s reproduction number, \documentclass[12pt]{minimal}
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\begin{document}$$R_0$$\end{document}R0, while also considering social distancing measures and demographic characteristics. The analysis highlights the importance of combining mass testing with isolation strategies to reduce the spread of the virus. The simulations demonstrate that in countries characterized by high levels of elderly cohabitation with younger individuals, vertical isolation is insufficient; horizontal isolation with work restrictions, alongside testing and susceptibility reduction measures, is crucial. For aged developed countries, where cohabitation of the elderly with younger individuals is less prevalent, and for least developed countries, where the population has a predominantly youthful age structure, pandemic control is more feasible with fewer tests. The study also emphasizes the critical role of identifying asymptomatic cases to achieve optimal epidemic control. Lastly, the cost-effectiveness of various testing strategies is examined, providing insights for public health policy decision-making.