Abstract
This thesis develops deterministic and stochastic models to assess the risk of Chikungunya virus (CHIKV) establishment and spread in Florida, a subtropical region vulnerable to arbovirus transmission. Chapter 2 introduces a multi‑patch model that incorporates human movement from endemic areas, temperature‑dependent mosquito dynamics, vertical transmission, and incubation periods. It establishes the theoretical relationship between the mosquito reproduction number and the disease reproduction number, showing how these metrics shape transmission across connected regions.
Chapter 3 presents a simplified version of the first model, enhanced with external introductions of infected individuals. Using a Continuous‑Time Markov Chain framework and numerical simulations, it evaluates the probability of long‑term endemicity following single or repeated introductions. The model identifies seasonal high‑risk periods and assesses vector‑control strategies to prevent sporadic outbreaks from becoming sustained transmission.
Chapter 4 shifts to a mechanistic Bayesian approach to study CHIKV transmission in Brazil’s three most endemic regions. A flexible compartmental model incorporates temperature‑driven transmission and potential strain co‑circulation. Using 2020–2024 incidence and climate data, the model estimates posterior distributions for key parameters, including transmission rates, reporting probabilities, and climate modifiers. Results show that modest adjustments to a base SEIR structure can capture diverse regional transmission patterns.
Chapter 5 synthesizes findings and outlines future research directions.