Modeling Behavioral and Biological Complexities of Malaria Control

Abstract

Malaria, caused by a single-cell parasite and transmitted by blood-feeding mosquitoes, is one of the world's leading infectious diseases in terms of both morbidity and mortality. While the first two decades of the twenty-first century saw rapid advances in the control and elimination of malaria worldwide, progress has stalled in recent years due to a combination of biological and behavioral factors. A better understanding of these barriers is essential to the future of malaria eradication. Mathematical modeling is an important tool for understanding the dynamics of infectious diseases. Models can lend important insight into challenges for control and elimination, as well as forecast the future of disease transmission. In this dissertation, I apply three mechanistic modeling approaches to understand complexities of malaria transmission, with a focus on incorporating important questions about human behavior and mosquito behavior and biology as they relate to malaria interventions. In Chapter 1, I use a temperature-based model of the basic reproduction number (R0) for malaria to understand the epidemiological impact of a new experimental finding: that the latent period of the malaria parasite in the mosquito is shortened by additional blood meals. I find that the latent period reduction due to multiple blood-feeding leads to an increase in estimated R0, which has important implications for the continued persistence of malaria in low-transmission settings. In Chapter 2, I construct a mosquito population model to simulate insecticide resistance assays from adult-captured mosquito collections, in order to quantify possible biases that may arise in resistance assays. I find that adult-capture assays can be improved using a simple mathematical correction, and are thus a viable alternative to larval assays for resistance monitoring programs. Finally, in Chapter 3, I develop an agent-based model of a small human population with endemic malaria to explore the impact of risk-based decision making on the effectiveness of bed nets against malaria transmission. I consider three scenarios, including one in which people respond to a misperception of disease risk, to demonstrate the importance of considering subjective risk perception in intervention planning models.