What? While central to public health, the evolution of disease-causing organisms — pathogens — remains highly unpredictable. This project aims to increase our understanding of the driving forces behind pathogen evolution. Parsimonious, data-driven mathematical models of evolution can allow us to probe counter-factual scenarios and assess the impact of individual contributing factors, such as population structure, complex immunity landscapes and differences in how the pathogen evolves within each host. A strong, computational understanding of these processes will allow us to address fundamental questions for disease control as well as vaccine design and distribution. The focus will initially be on SARS-CoV-2 (the virus behind COVID-19), and to a lesser extent influenza, due to the enormity of available data. Longer term, this work may lay the groundwork for cross-pathogen comparisons of evolutionary patterns – i.e., comparative phylodynamics. Why? The death toll due to infectious diseases is almost unimaginably high. In a typical year, it is on the order of 10 million deaths and during pandemics it can be substantially higher. The loss of quality of life is likewise enormous. Much of the difficulty of controlling infectious diseases stems from the ability of pathogens to evolve and evade our defenses, whether they are due to acquired immunity, vaccination, medication or even non-pharmaceutical interventions. The good news is that the possibilities for deciphering the evolutionary patterns of pathogens have never been better. The number of available viral genomes has increased radically in recent years, and especially SARS-CoV-2 can serve as a paradigm within which to test evolutionary hypotheses. Doing so will require the formulation of novel data analysis methods as well as model algorithms with which to test the influence of different factors on the evolutionary course of a pathogen. How? The methods of statistical physics were developed to describe systems with many – often interacting – constituents. As such they are naturally adaptable to studying the spread of pathogens among a large number of individual hosts. Combining these methods with principles of mathematical epidemiology and large-scale genomic sequence analysis opens up rich possibilities for uncovering the evolutionary patterns of pathogens, as they interact with population immunity. Together with collaborators at Princeton University – the birthplace of the field of phylodynamics – I have already applied these principles to obtain fundamental insights into the evolution of SARS-CoV-2. With this fellowship, I will develop a framework which unifies disease transmission and sequence-level computational modeling to better predict and characterize possible evolutionary shifts for some of the most burdensome pathogens.

What? The occurrence of human-wildlife interfaces has increased during the Anthropocene, yet we have very little knowledge of the patterns and potentials of cross-species co-construction of culture in these interfaces, which can have significant implications for coexistence. Using a model species for interface research, the long-tailed macaque (Macaca fascicularis), I will investigate the existence, diversity and nature of co-constructed cultures between humans and macaques in urban, rural, historical and novel interfaces Why? When humans and wildlife coexist it can lead to the co-creation of ecological niches and the emergence of cultural traits specific to their shared ecosystems. However, only a few studies have investigated such shared cultures, primarily because much human-wildlife interface research has focused on negative interactions and lacks an interdisciplinary approach. Additionally, the often invoked 'disturbance hypothesis' predicts that human disturbance will decrease wildlife population sizes and thereby reduce the foundations for new cultural features. However, for synanthropic wildlife species the exact opposite may occur. I propose a substantive shift in animal cultural research, to where we perceive and portray humans and wildlife as a part of one another's lives in shared interface ecologies How? I will compare urban and rural interfaces in Malaysia by observing the interspecific interactions between humans and long-tailed macaques and cultural behaviours/patterns. I will also collect data on demography, and environmental and social factors. Furthermore, through our organisation, The Long-Tailed Macaque Project (www.theltmproject.org), I will compare novel and historical interfaces by contacting all project researchers. Finally, I will compare my results with related data from human-macaque interfaces in India, including two other macaque species: the rhesus (Macaca mulatta) and bonnet macaques (Macaca radiata) to further investigate geographical origin effects and macaque-specific cultural patterns, similar or different from those of the long-tailed macaque