An Analytic Look at the Last Pandemic’s Spread and Its Control by Decision-Makers

Abstract

An agent-based model is a stochastic model in which the population’s characteristics, such as age, health status, sex, social features, and others, are simulated at the individual level, allowing the implementation of particular scenarios and interactions. This model is beneficial when applied to predicting disease spread and the impact of interventions on epidemics’ course. In this chapter, we intend to discuss how to use agent-based modeling, parameterized with real data, to predict the number of averted healthcare outcomes due to influenza-like diseases, such as COVID-19, and how to calculate the return on investment related to those interventions. The chapter will be structured in different sections seeking to address the following points: (i) the basic knowledge and concepts related to agent-based models; (ii) the use of real-world data in the model parameterization; (iii) an overview of COVID-19 spread inside Brazil; (iv) case studies focused on COVID-19 and control strategies applied to halt disease transmission; (v) calculation of the return on investment and social and economic benefits of intervention campaigns, such as vaccination; and (vi) how models can be used to support decision-making and the authorities.