Abstract
A central feature of an emerging infectious disease in a pandemic scenario is the spread through geographical scales and the impacts on different locations according to the adopted mitigation protocols. We investigated a stochastic epidemic model with the metapopulation approach in which patches represent municipalities. Contagion follows a stochastic compartmental model for municipalities; the latter, in turn, interact with each other through recurrent mobility. As a case of study, we consider the epidemic of COVID-19 in Brazil performing data-driven simulations. Properties of the simulated epidemic curves have very broad distributions across different geographical locations and scales, from states, passing through intermediate and immediate regions down to municipality levels. Correlations between delay of the epidemic outbreak and distance from the respective capital cities were predicted to be strong in several states and weak in others, signaling influences of multiple epidemic foci propagating towards the inland cities. Responses of different regions to a same mitigation protocol can vary enormously implying that the policies of combating the epidemics must be engineered according to the region' specificity but integrated with the overall situation. Real series of reported cases confirm the qualitative scenarios predicted in simulations. Even though we restricted our study to Brazil, the prospects and model can be extended to other geographical organizations with heterogeneous demographic distributions.
Highlights
Theoretical and computational toolboxes used and developed by physicists over the past decades, those of equilibrium and nonequilibrium statistical physics, have found an inexhaustible source of applications in complexity [1,2,3,4], attaining an apex with the rise of network science in the late 1990s [5]
Epidemic models, which are intimately related with nonequilibrium phase transitions [6], running on the top of networks soon became one of the most imminent fields of interdisciplinary physics [7,8,9], with many applications in real epidemic scenarios led by physicists [10,11,12]
We developed a stochastic metapopulation model and applied it to the spread of COVID-19 in Brazilian municipalities
Summary
Theoretical and computational toolboxes used and developed by physicists over the past decades, those of equilibrium and nonequilibrium statistical physics, have found an inexhaustible source of applications in complexity [1,2,3,4], attaining an apex with the rise of network science in the late 1990s [5]. Efforts to track the transmission from Wuhan to the rest of mainland China and the world relied on mathematical modeling [13,14,18,21,22], which was soon extended to other countries [23,24,25] using the metapopulation approach [26,27,28] In this modeling, the population is grouped in patches representing geographic regions, and the epidemic contagion obeys standard compartmental models [29] within the patches, while mobility among them promotes the spread of the epidemic throughout the whole population. VI, we present a brief afterword commenting on the achievements of the model in forecasting the qualitative countrywide behavior of the pandemics in Brazil five months after the predictions were made [39]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
