Coupling Up: A Systems Approach to Aedes-Borne Disease Dynamics Across Scales

Dengue virus is a vector-borne disease transmitted by Aedes mosquitoes that circulates in tropical and sub-tropical climates around the world. Dengue has a high global burden, and about half of the world's population is at risk. In the absence of a specific treatment for dengue, its dynamics and control are largely centered around vector populations and how humans interact with them. In this Dissertation, I present three analyses that demonstrate how mathematical models can be used to explore how dengue transmission interfaces with other dynamic processes at different scales. First, to understand the environmental and biological conditions that make co-infections with multiple arboviruses, including dengue, Zika, and chikungunya viruses, possible, I developed a mathematical model of co-circulation of two arboviruses, with co-infection possible in both mosquitoes and humans. I examined the effect that seasonal timing, environmental, and biological conditions had on the extent of co-infection and found that temporal synchrony of co-infecting viruses and average temperature were the most influential drivers of co-infection incidence. Using global sensitivity analyses, I concluded that appreciable numbers of co-infections are unlikely outside of tropical settings when the viruses co-occur in time and space. Second, to demonstrate how different behavioral stimuli and forms of mosquito prevention shape the equilibrium prevalence of dengue, I constructed a theoretical model in which both dengue and control behavior were modeled as contagious processes. I performed robust analytical and numerical analyses of the model that identified mathematical relationships between system equilibria and assumptions about behavior, determined that mosquito biting was a primary driver of behavior uptake, and confirmed that different forms of mosquito prevention mediated distinct influences of disease. My results highlighted how a coupled contagion framework can provide insight into unique features of multiple-contagion systems with distinct biological features. Third, to show how assumptions about human mobility can shape population-level network structures and epidemiological outcomes, I used an individual-based model to systematically vary human movement patterns at the household level. Through extensive simulations, I revealed that as conformity in fine-scale movement increased, there was a moderate reduction in dengue transmission, as more densely connected global networks were less favorable for dengue transmission. Using counterfactual scenarios, I demonstrated the importance of modeling human movement with sufficient granularity to capture the factors that shape disease risk dynamics and determine optimal control measures. In conclusion, my research shows how mathematical models can be used to capture and describe complexities in the transmission of dengue and other Aedes-borne diseases that can subtly shape the dynamics of these diseases and their control.<p></p>