Abstract
The pandemic caused by the novel coronavirus, although more than a year has passed since the first case, still plagues almost the whole world. Several policies have been adopted, especially related to social distancing measures, aiming to mitigate the spread of the disease. Such decisions, in general, take into account simulations capable of providing an overview of the spread of the virus in a given location. Based on the guidelines of the World Health Organization, countries have defined their own policies to fight against the disease, considering economic and social interests. Determining strategies that are increasingly efficient in modeling and simulating such phenomena is essential to support decision making in adverse circumstances. Our objective is to provide a more comprehensive view of strategies for predicting the spread of COVID-19 in the scope of computational modeling and to analyze scenarios capable of describing the impact of social distancing measures. Two different strategies are compared to characterize the virus incubation period, using particular models. Since Italy was one of the countries most affected by the pandemic, despite taking drastic measures to reduce mobility and contact between citizens, we adopt the situation of the early stages of the disease outbreak in this country to demonstrate the numerical results.
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Acknowledgements
The authors would like to thank the Ministry of Science, Technology, Innovation and Communication (MCTIC) of Brazil. Gustavo Libotte is supported by a postdoctoral fellowship from the Institutional Training Program (PCI) of the Brazilian National Council for Scientific and Technological Development (CNPq), grant number 303185/2020-1.
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Lobato, F.S., Libotte, G.B., Platt, G.M., Almeida, R.C., Silva, R.S., Malta, S.M.C. (2021). Simulations of Social Distancing Scenarios and Analysis of Strategies to Predict the Spread of COVID-19. In: Toni, B. (eds) The Mathematics of Patterns, Symmetries, and Beauties in Nature. STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health. Springer, Cham. https://doi.org/10.1007/978-3-030-84596-4_5
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