Location: Arthropod-borne Animal Diseases Research2012 Annual Report
1a. Objectives (from AD-416):
Develop stochastic and predictive models for arthropod-borne diseases of medical and veterinary importance. Build a stochastic network based model of Rift Valley fever transmission that involves mosquitoes, humans and animals. The model will be used to evaluate mitigation strategies to reduce mortality and morbidity during disease outbreaks and possible Rift Valley Fever epidemics.
1b. Approach (from AD-416):
Mathematical models that incorporate disease vector transmission parameters will be used to determine the mortality and morbidity during disease epidemics. Specifically, a stochastic Rift Valley fever model will be developed that incorporates humans, livestock, and mosquito species. The model will consider mosquito and cattle population fluctuations based on movement and environmental factors. After establishing and validating the model, mitigation strategies will be evaluated for feasibility.
3. Progress Report:
Mathematical modeling is needed to study disease epidemiology in an area in the absence of the disease. Kansas State and ABADRU researchers designed a deterministic network-based model of Rift Valley fever virus propagation in Africa and the United States. The first model examines human, livestock and mosquito populations to estimate disease spread and number of infections based on mathematical models that account for the spatial distribution of all three populations throughout the landscape. This model was validated using data from a Rift Valley fever epidemic in Africa. The second model is a deterministic network-based model with stochastic features that can incorporate environmental and biological variation. The model is built upon a large directed, asymmetric network and will be used to model the epidemiological outcome of various RVFV introduction scenarios into Texas, a major cattle producing area of the United States. Humans, cattle, and Aedes and Culex mosquito populations are modeled independently and their interactions recorded to identify the effects of various spatial distributions, climate scenarios, and contact ratios on possible RVFV mitigation strategies. In the future a third model, stochastic network-based meta-population model (StochNet), will be considered which will examine the transitions to the infected compartments as driven by probability distributions.