Disentangling the web: Using models to explore vector-borne disease dynamics in ecosystems

Abstract

Vector-borne diseases are a major global health concern, causing over 700 000 deaths annually. These diseases are spread by insects like mosquitoes, ticks, and flies, which carry harmful pathogens from one host to another. Common examples include malaria, dengue fever, and West Nile virus. This research explores the intricate ways these diseases spread, with a focus on questions such as: 1. What is the impact of species not directly involved in disease transmission, but that interact with the species that do? 2. How do mosquito biting preferences affect disease spread? 3. What is the role of competition between different species in disease dynamics? We find that when mosquito species compete with each other, it tends to reduce the risk of disease spread. However, the opposite is true for host species competition, which can increase transmission risk. The research also shows that mosquito feeding preferences play a more significant role when host species are in fierce competition. Surprisingly, even species not typically associated with disease spread can sometimes increase transmission risk if they strongly compete with species that do carry the disease. For West Nile virus specifically, the study identifies mosquito feeding preferences and mosquito-to-bird transmission rates as critical factors. Natural and rural habitats are found to be more susceptible to West Nile virus, with different mosquito types taking center stage in various environments. This thesis also shows that when mosquitoes have an increased attraction to malaria-infected hosts, this can facilitate the introduction of malaria to new areas as well as influencing the spread of other diseases like Usutu virus. We also find that even when conditions aren't favorable for a major outbreak, small, temporary outbreaks of vector-borne diseases can still occur. These findings are just a small sample of the complex nature of vector-borne disease transmission. They highlight the importance of integrating real-world data with mathematical models to enhance our ability to predict disease spread and develop more effective public health strategies.