| Full text | |
| Author(s): |
Valeriano, Joao Pedro
;
Cintra, Pedro Henrique
;
Libotte, Gustavo
;
Reis, Igor
;
Fontinele, Felipe
;
Silva, Renato
;
Malta, Sandra
Total Authors: 7
|
| Document type: | Journal article |
| Source: | NONLINEAR DYNAMICS; v. 111, n. 1, p. 10-pg., 2022-09-25. |
| Abstract | |
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) | |
| FAPESP's process: | 21/02027-0 - Study of the Galactic Center region and searches for dark matter signals with gamma-rays |
| Grantee: | Igor Reis |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| FAPESP's process: | 20/14169-0 - The effect of a chaotic environmental flow on the evolution of microbial social behaviors |
| Grantee: | João Pedro Valeriano Miranda |
| Support Opportunities: | Scholarships in Brazil - Master |