A Modeling Study on the Effect of Interstate Mobility Restrictions on the SARS-CoV-2 Pandemic

• Published in: Bulletin of mathematical biology 2024-09, Vol.86 (9), p.118, Article 118
• Main Authors: Libotte, Gustavo B., dos Anjos, Lucas, de Almeida, Regina C. C., Malta, Sandra M. C.
• Format: Article
• Language: English
• Subjects: Basic Reproduction Number - statistics & numerical data; Brazil - epidemiology; Cell Biology; Computer Simulation; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 - transmission; Epidemiological Models; Epidemiology; Graph theory; Humans; Life Sciences; Mathematical and Computational Biology; Mathematical Concepts; Mathematics; Mathematics and Statistics; Mobility; Models, Biological; Movement; Original Article; Pandemics; Pandemics - prevention & control; Pandemics - statistics & numerical data; Parameter estimation; Quarantine - statistics & numerical data; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Viral diseases
• ISSN: 0092-8240; 1522-9602; 1522-9602
• DOI: 10.1007/s11538-024-01347-4

Mobility is a crucial element in comprehending the possible expansion of the transmission chain in an epidemic. In the initial phases, strategies for containing cases can be directly linked to population mobility restrictions, especially when only non-pharmaceutical measures are available. During the pandemic of COVID-19 in Brazil, mobility limitation measures were strongly opposed by a large portion of the population. Hypothetically, if the population had supported such measures, the sharp rise in the number of cases could have been suppressed. In this context, computational modeling offers systematic methods for analyzing scenarios about the development of the epidemiological situation taking into account specific conditions. In this study, we examine the impacts of interstate mobility in Brazil. To do so, we develop a metapopulational model that considers both intra and intercompartmental dynamics, utilizing graph theory. We use a parameter estimation technique that allows us to infer the effective reproduction number in each state and estimate the time-varying transmission rate. This makes it possible to investigate scenarios related to mobility and quantify the effect of people moving between states and how certain measures to limit movement might reduce the impact of the pandemic. Our results demonstrate a clear association between the number of cases and mobility, which is heightened when states are closer to each other. This serves as a proof of concept and shows how reducing mobility in more heavily trafficked areas can be more effective.