| Texto completo | |
| Autor(es): |
Valeriano, Joao Pedro
;
Cintra, Pedro Henrique
;
Libotte, Gustavo
;
Reis, Igor
;
Fontinele, Felipe
;
Silva, Renato
;
Malta, Sandra
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | NONLINEAR DYNAMICS; v. 111, n. 1, p. 10-pg., 2022-09-25. |
| Resumo | |
The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an approximate Bayesian computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting. (AU) | |
| Processo FAPESP: | 21/02027-0 - Estudo do Centro Galáctico e busca de sinais de matéria escura com raios gama |
| Beneficiário: | Igor Reis |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |
| Processo FAPESP: | 20/14169-0 - O efeito de um fluxo ambiental caótico na evolução dos comportamentos sociais microbianos |
| Beneficiário: | João Pedro Valeriano Miranda |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |