Data-driven mathematical models for assessing the COVID-19: SIRD-type equations

Detalhes bibliográficos
Ano de defesa: 2021
Autor(a) principal: Amaral, Fábio Vinícius Goes
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Estadual Paulista (Unesp)
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://hdl.handle.net/11449/214330
Resumo: São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of COVID-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s deceases. Join- ing the Brazilian academia efforts in the fight against COVID-19, in this work we describe a unified framework for monitoring and forecasting the COVID-19 progress. A novel fore- casting data-driven method has been proposed, by combining the so-called Susceptible- Infectious-Recovered-Deceased model with machine learning strategies to properly fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the resulting predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our in- tegrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of COVID-19 curves for different regions of the state and country. Finally, we extend our methodology to in- vestigate the effects of the vaccination process with a more complex model. In particular, our studies are able to predict different scenarios varying the rate of vaccination and the effectiveness of the vaccines.