Yellow fever (YF) is an arbovirus that affects both humans and non-human primates (NHPs). Despite a longstanding recognition of YF as a significant public health problem, many aspects of its underlying transmission and maintenance remain unknown. These knowledge gaps continue to exist even with the increasing availability of data, techniques and recent large-scale outbreaks in South America and Africa.Using several statistical and machine learning methods, I investigate the role of climate, environment and host in predicting the suitability of YF across South America, as an average, seasonally and inter-annually. Following Iexamine the role of seasonality of agriculture, as a proxy for exposure, on human and NHP YF reports in Brazil. To contextualise these predictions and recommendations, I calculate and describe population-level YF vaccination coverage estimates (1940-2050) across Africa and South America, as well as the interactive web-based platform these are published on. Finally, I predict the distribution and density of NHP genera across the South-East of Brazil, and use this ina stochastic multi-species, age structured, meta-population model to explore the role of NHP genera on viral maintenance, and the potential for the establishment of endemicity in the state of Rio de Janeiro.Overall this thesis describes several key aspects necessary to understand the enigma of YF transmission in South America. A greater understanding of climate and environment allows for the possibility of forecasting periods of heightened transmission which could inform pro-active surveillance and vaccination, supported through our vaccination coverage estimates. Finally, by providing insights into the role of NHP genera on maintenance and critical community sizes for YFV transmission, I can highlight the potential for endemicity to be established.Open Acces
