Analysis of Covid-19 Dynamics in Brazil by Recursive State and Parameter Estimations

Daniel Martins Silva () and Argimiro Resende Secchi ()
Additional contact information
Daniel Martins Silva: Center of Technology, COPPE, Federal University of Rio de Janeiro
Argimiro Resende Secchi: Center of Technology, COPPE, Federal University of Rio de Janeiro

A chapter in Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics, 2023, pp 335-374 from Springer

Abstract: Abstract In this article, the epidemiological dynamics of COVID-19 in Brazil were studied for each federative unit using state and parameter estimations based on public data. Transmissibility, the severity of the disease, and mortality dynamics were estimated over time for each case study through augmented states using a constrained extended Kalman filter (CEKF) and a Rauch-Tung-Striebel smoother. The estimated parameters were defined in a proposed SEIR-based model containing mobility data and heuristic assumptions. In addition, a heuristic data preprocessing was performed for each case study to remove data outliers from underreporting and overreporting. Simulations evaluated the COVID-19 epidemiological dynamics for each case study from 1 October 2020 to 1 October 2022. The dynamic feedback approach was efficient in detecting emerging predominant variants in most federative units. In addition, it satisfactorily characterized the predominant variants circulating dynamics under time-varying model uncertainty. Nonetheless, the technique was insufficient to describe the case studies with meaningful underreporting changes or poor data quality over time.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-33050-6_20

Ordering information: This item can be ordered from
http://www.springer.com/9783031330506

DOI: 10.1007/978-3-031-33050-6_20

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().