Malaria epidemiology is characterised by extensive heterogeneity that manifests across a range of spatial and temporal scales. This heterogeneity is driven by a diversity of factors spanning the human host, the parasite, the mosquito vector and the environment. Together, variation in these factors lead to marked differences in the epidemiology of malaria across different settings; in where malaria is concentrated, how malaria is transmitted and who is most at-risk. These differences have material consequences for the impact of control interventions aimed at combatting the disease, underscoring the crucial need to better understand and quantify the factors underlying heterogeneity in malaria epidemiology and transmission dynamics. In this thesis, I use a combination of statistical and mathematical modelling to further our understanding of how variation in the epidemiological and entomological determinants of malaria transmission drives heterogeneity in dynamics across settings and explore the implications of this variation for control efforts.
Accurate ascertainment of malaria infections represents a crucial component of malaria surveillance and control. Previous work has revealed the often-substantial prevalence of infections with parasite densities lower than the threshold of detection by microscopy (so called “submicroscopic” infections). The drivers of these infections remain uncertain, despite their established relevance to onwards transmission. In Chapter 2, I carry out a systematic literature review and meta-analysis exploring the prevalence of submicroscopic malaria infections and how this varies between settings. My results highlight extensive variation between settings, with much of this driven by a combination of both historical and current levels of transmission. Crucially, these results highlight significant variation in the prevalence of submicroscopic infections even across settings characterised by similar current levels of transmission, with implications for the utility of control efforts specifically targeting this infected sub-group depending on the context.
Within communities, the distribution of malaria infections is frequently characterised by extensive spatial heterogeneity, which can make identification and treatment of infections challenging. In Chapter 3, using a regression-based approach, I characterise the fine-scale spatial clustering of malaria infections at the household level across a diverse range of sub-Saharan African settings through systematic analysis of 57 Demographic and Health Surveys spanning 23 countries. My results highlight that malaria infections cluster within households, and that the extent of this clustering becomes significantly more pronounced as transmission declines – a factor which will affect the comparative impact of household-targeting or whole-community based control strategies and result in their appropriateness depending closely on the levels of transmission characterising a setting.
In addition to this spatial heterogeneity, malaria transmission dynamics are also frequently characterised by extensive temporal heterogeneity, a phenomenon underpinned by the (often annual) temporal fluctuations in the size of the mosquito populations responsible for transmission. Many questions remain surrounding the drivers of these dynamics however, questions that are rarely answerable from individual entomological studies (focussed on only a single location or species). In Chapter 4 I carry out a systematic literature review to collate anopheline mosquito time-series data from across India and develop a statistical framework capable of characterising the dominant temporal patterns in this dataset. The results demonstrate extensive diversity in the timing and extent of seasonality across mosquito species, but also show that this diversity can be clustered into a small number of “dynamical archetypes”, each shaped and driven by a largely unique set of environmental factors including rainfall, temperature, proximity to water bodies and patterns of land use.
In Chapter 5, I apply this framework to time-series data from across South Asia and the Middle East for the highly efficient vector Anopheles stephensi, to better understand the factors shaping its seasonal dynamics and the likely impact of its recent establishment in the Horn of Africa. My results reveal significant differences in the extent of seasonality across Anopheles stephensi populations, with dynamics frequently differing between rural and urban settings, suggesting structural differences in how these environments shape patterns of vector abundance and potentially warranting different vector control strategies depending on predominant patterns of land-use. Integrating these seasonal profiles into a mathematical model of malaria transmission highlights the crucial need for an understanding of the timing of seasonal peaks in vector density if control interventions like IRS are to be most effectively deployed.
Overall, the results presented here highlight some of the drivers influencing spatial and temporal heterogeneity in malaria epidemiology, quantifies how they contribute to the diverse malaria dynamics observed across different settings, and explores the implication of this variation for effective control of the disease.