Modeling Dengue Transmission and Vaccination

Dengue is one of the most rapidly spreading mosquito-borne viral diseases in the world and inflicts significant health, economic and social burdens on populations. In this dissertation, I studied different aspects of modeling of dengue and vector-borne diseases in general. Among various dengue models that have appeared in literature, some explicitly model the mosquito population, while others model them implicitly. In spite of extensive use of both modeling approaches, little guidance exists for which type of model should be preferred. I developed a Bayesian approach that uses a Markov chain Monte Carlo (MCMC) method to fit disease models to epidemiological data and used it to explore how well these models explain observed incidence and to find good estimates for the epidemiological parameters for dengue. I fitted dengue hemorrhagic fever data from Thailand to both type of models and found using Akaike Information Criterion that explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission. On comparing my estimates of the basic reproduction number, R0, with other estimates in literature, I found a wid