Mosquito biting behavior and malaria transmission: interactions between host preferences and local environmental conditions

Vector-borne diseases particularly malaria cause a significant burden on human populations in the tropics. Distribution of these diseases is governed by a mix of both environmental and social factors which in turn effect vector and host interactions. A key factor behind the transmission of vector borne diseases is the behavior of the vector its self within its environment.

Mathematical modelling plays a crucial role in aiding in predicting disease transmission dynamics and allow the potential impact of control measures to be assessed across various scenarios. Despite its clear importance, it is currently recognised as a neglected area of research. All mathematical models have factors within the model to reflect various scenarios which relate to the field situation. Usually these can be based on assumptions or on experiment evidence collected from the field. Recently mathematical models of arthropod borne disease have developed and increased in complexity resulting in an increased number of parameters. Despite this surge in complex models, at the core of many of these models there are still some assumptions. One of these assumptions is how vector host preference is affected by host availability.
Different mosquito species show a variety of host biting behaviours. For example, Anopheles gambiae (a primary malaria vector) has shown an extreme preference for human blood over other blood meal sources. As a result of this, their influence on disease transmission in human populations is highly significant. Currently many disease models assume the proportion of bites humans receive from a mosquito population is directly proportional to the human host availability. However, research into insect ecology shows us that this is rarely the case. Despite this, this assumption is central to many vector-borne disease models and at this time is solely based on theoretical data.
The role of host species availability, particularly availability of humans in a population is crucial to determine if disease transmission and control programs predictions are to be accurately modelled. This is particularly important for diseases such as malaria where control and reduction of disease burden operates by reducing the number of individuals available to bite, primarily through use of bed nets, repellents and other control measures. Importantly, this can have a profound effect on how effective a disease control intervention is predicted to be and therefore it is vital to determine this response in a field setting.
This PhD has provided the opportunity to collect novel, empirical field data and is allowing me to develop skills in molecular biology as well as learn how to generate mathematical models to predict disease transmission and better assess disease control strategies. This work could not only have a profound effect on malaria control strategies but also on vector control efforts as a whole. In addition, the lab based work will also allow data to be collected of species composition, insecticide resistance status, vector species overlap and prevalence of arthropod borne disease in the local mosquito populations allowing control efforts to be better targeted.