DMS/NIGMS 2: Spatial, Multi-Host Petri Net Models for Zoonotic Disease Forecasting

Diseases that spillover from wild animals pose an increasing threat to human health worldwide, but forecasting how zoonotic pathogens spread remains a major challenge. Zoonotic diseases are complex, spatially and temporally evolving systems whose behaviors are influenced by social, ecological, genetic, and evolutionary factors. Understanding the contributions of biotic and abiotic factors to accurate disease forecasting is an urgent priority for managing the emerging risks of rapid environmental change and for improving mechanistic models of complex ecological systems. Field studies monitoring zoonotic pathogens and their host species have typically assumed that observing high host prevalence is strong evidence that (i) the host is a 'reservoir' of the pathogen, maintaining it at a stable population level; and (ii) the host is a persistent source of spillover into other species. However, ecological models have shown that reservoir status can be strongly context dependent, mediated by extrinsic factors including interactions with other species and habitat fragmentation. An interdisciplinary approach combining complex systems modeling, data science, and risk analysis is therefore needed to model zoonotic spillover dynamics. This project will construct novel largescale, multi-host mechanistic models of the Hantavirus Pulmonary Syndrome (HPS) and Valley Fever (coccidioidomycosis) diseases in the Phoenix, Arizona metro area to investigate the stability versus context-sensitivity of hosts as disease reservoirs and identify key future data sources required to improve forecasting and identify effective interventions aimed at reducing disease burden. Both diseases are endemic to the southwestern United States and are believed to be spread by rodent hosts, but they are caused by different types of pathogens (viruses and fungi, respectively) and show divergent case trends. The project will use Petri Net models, which are modular, scalable, and readily visualized as mechanistic network diagrams, which makes them a valuable tool for exploring how adding or removing hosts and changing land use are expected to change disease dynamics. For training and outreach, the project will implement and evaluate initiatives to (i) communicate results in public outreach events; (ii) construct a course-based undergraduate research experience (CURE) focused on disease modeling; and (iii) build capacity for researching Valley Fever and mitigating outbreaks. For outreach, the project will create a free web app for public interaction with modular disease models and their visual outputs. In addition to presenting project results for HPS and Valley Fever, the web app will be linked to an interactive textbook introducing Petri Nets to be developed by the project. For the CURE class, students will focus on synthesizing zoonotic disease data to improve risk modeling, providing urgently needed research opportunities for ASU's approximately 7,000 in-person and online biology majors. Lastly, the project will organize a capacity-building workshop of academic and public health researchers to introduce them to Petri Net resources and to identify data needs for improved forecasting.