UCLA

Zoonotic diseases, which are caused by pathogens that transmit from animals into humans, are responsible for numerous ongoing public health burdens, such as leptospirosis, rabies, and West Nile virus infections, and are also considered a probable source of future epidemics in humans. Describing and quantifying the transmission dynamics of these pathogens is vital if we wish to assess which of the many known zoonotic pathogens pose a threat to humans and which management strategies would be most effective at minimizing that threat. To conduct these assessments, it is necessary to consider the ecological dynamics and interactions driving zoonotic disease transmission.

A zoonotic pathogen’s impact on humans depends not only on transmission dynamics within the human population, including heterogeneities in human contacts and interactions with endemic human pathogens, but also on disease dynamics within the reservoir and at the human-reservoir interface. Because of the complex ecological interactions driving the spread of zoonotic pathogens, qualitatively and quantitatively characterizing their spread and devising rational management strategies requires combining insights from community ecology, invasion biology, and classical single-host disease ecology with system-specific information about the pathways of transmission within the reservoir, within humans, and between the two. Bringing together these complementary perspectives can shed light on the key processes driving transmission, which is essential for predicting how changes, both purposeful interventions and natural shifts, may alter the system’s behavior. In this dissertation, I present three studies that use diverse methods to explore different aspects of zoonotic pathogens’ disease dynamics.

In chapter 1, I use a theoretical approach to explore the effects of competition between a zoonotic pathogen and a human-endemic pathogen in the context of a disease eradication program. I use a deterministic compartmental model that tracks spillover and transmission of a zoonotic disease in humans as well as transmission of a partially cross-protective endemic human pathogen to examine how the presence of the zoonotic pathogen can reduce the vaccination coverage necessary to eradicate the human pathogen and how the zoonotic pathogen’s prevalence is expected to change during and following a successful eradication campaign. I then use the smallpox-monkeypox system as a case study to assess how the theoretical findings apply in a real-world context.

In chapter 2, I move from theoretical explorations of disease dynamics to using real-world datasets to inform mechanistic models. Zoonotic disease surveillance datasets are valuable sources of information about disease dynamics, but are generally difficult and expensive to obtain and are associated with a variety of data limitations. This chapter develops methods to extract as much information as possible from these valuable information sources. I develop a model-based inference method that addresses a number of data challenges, including unobserved sources of transmission (both human and zoonotic), limited spatial information, and unknown scope of surveillance, using a spatial model with two levels of mixing. After demonstrating the robustness of the method using simulation studies, I apply the new method to a dataset of human monkeypox cases detected during an active surveillance program from 1982-1986 in the Democratic Republic of the Congo. The results provide estimates of the reproductive number and spillover rate of monkeypox during this surveillance period and suggest that most human-to-human transmission events occur over distances of 30 km or less. Taking advantage of contact-tracing data available for a subset of monkeypox cases, I find that around 80% of contact-traced links could be correctly recovered from transmission trees inferred using only date and location. The results highlight the importance of identifying the appropriate spatial scale of transmission, and show how even imperfect spatiotemporal data can be incorporated into models of zoonotic pathogens to obtain reliable estimates of transmission patterns.

Chapter 3 shifts from examining the dynamics of zoonotic pathogens after they have already spilled into humans to evaluating how interventions in the zoonotic reservoir could help reduce the risk of spillover occurring in the first place. This chapter focuses on evaluating interventions to minimize the risk of spillover of swine-origin influenza A viruses (IAV-S) into humans in the United States. In the past decade, the majority of reported human infections with IAV-S in the United States have been associated with individuals exposed to exhibition swine while attending agricultural shows. Because these exhibition swine make up a largely distinct population within the US swine herd, there is great potential to implement control practices within exhibition swine that could substantially reduce risk of spillover into humans. To understand the factors that drive influenza prevalence and persistence in US exhibition swine and to evaluate the impact of potential interventions, I develop a network model that characterizes disease spread into and among exhibition swine. The model incorporates key structural information about the system and is informed by a unique surveillance dataset collected from shows in Ohio, Michigan, and Indiana, including IAV-S genomes from more than one hundred infected swine. I use several different approaches based on both epidemiological and sequence data to estimate parameters describing transmission and to evaluate the expected impact of a set of thirty potential interventions on the risk of spillover into humans. Across all approaches, several interventions consistently are found to perform best at reducing projected spillover risk, including requiring participants to take one or two weeks off between shows and implementing strategies to reduce transmission probabilities among swine at shows.

While the studies presented in these chapters range from theoretical explorations of simplified systems to direct comparisons of intervention impacts incorporating messy real-world data and complex system structure, they all pursue the common goal of providing insights relevant for conceptualizing the prominent forces in a system and for using that understanding to inform decisions on control measures in a real-world context.