Advisor(s)
Ian Knowles
Committee Member(s)
Chengcui Zhang
Marius Nkashama
Roger Sidje
Satyaki Roy
Document Type
Dissertation
Date of Award
6-1-2026
Degree Name
Doctor of Philosophy (PhD)
School
College of Arts and Sciences
Department
Applied Mathematics
Abstract
In 2021 Fitzgibbon, Morgan, Webb, and Wu used a modified SEIR (susceptible, exposed, infected, and recovered) model to predict how COVID-19 spread through Brazil [12]. For their model, six constant coefficients were used that were fitted, referenced, or assumed. In this thesis, we geo-spatially modify their SEIR model and formulate an inverse problem to recover the now spatial coefficients of the model. We first show there exists a unique solution to the modified model. To solve the inverse problem, we modify an inverse method [17] that focused on minimizing convex functionals. These recovered spatial coefficients can be used with Matlab’s PDE solver to give a better, and longer, prediction of COVID-19 cases in Brazil.
ProQuest ID
Recommended Citation
Mills, Cameron Keith, "Inverse Modeling The Geo-Spread Of Covid-19 In Brazil" (2026). ETDs from 2020-2029. 146.
https://digitalcommons.library.uab.edu/etd-2020s/146
