# Disease transmission along complex human-animal networks: a novel method for improving zoonotic disease modeling > **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $720,456 ## Abstract ABSTRACT Endemic and emerging zoonoses both represent profound threats to public health. While these two disease systems diverge in many ways, fundamental to both is the importance contact networks in which humans and animals mix. In STI research and veterinary epidemiology, analysis of human-only and livestock-only networks have led to significant insights on how transmission occurs, and how best to interrupt it. Yet to our knowledge, no prior research has modeled a human-animal contact network using empirical data, leaving the benefits of network epidemiology inaccessible to zoonotic disease research and control. As a result, researchers must as- sume that humans and animals mix randomly, or rely on weakly-justified assumptions about stratified risk, when building mathematical models, designing surveillance systems, or planning interventions. There is a critical need to characterize the structure and dynamics of human-animal contact across a range of settings and disease systems, in order to reduce the burden of endemic zoonoses and prevent emergence of novel zoonoses. Our long-term goal is to develop a suite of methods for conducing human-animal network analyses. Our overall objective is to demonstrate proof-of-principle: that analysis of human-animal contact networks is feasible, and results in improved inference. Because emergence of novel zoonotic pathogens is a rare event, we will instead use data from four high-burden endemic zoonoses representing a range of transmission modes: brucellosis, Q fever, leptospirosis, and anaplasmosis. This ensures we will have adequate power to achieve our objective, and contributes to the control of high-morbidity, poverty-reinforcing diseases. Across Dornod and Uvurkhangai prov- inces in Mongolia, we will use an egocentric approach to sampling whereby ego households are randomly se- lected and asked to name alter households: those whose animal herd mixes with their own. In Aim 1, following formative qualitative research we will collect empirical human-livestock contact data using surveys and livestock GPS collars. GPS collars will be placed for five months, during which period network changes will be captured using a monthly husbandry log (household) and a 24 hour contact diary (individual) completed once per month. In Aim 2 we will fit a generative network model to the network data gathered in Aim 1. We will simulate synthetic networks from this generative model, and demonstrate their validity using disease data from real-time qPCR testing and molecular strain typing. Finally, in Aim 3 we will combine these synthetic networks and disease data in an epidemic model of disease transmission, separately for each disease, broadly following an SEIR frame- work. Using these models, we will evaluate the added utility gained by incorporating network structure compared with assuming random mixing. We expect our contribution to be methods for measuring and modeling human- animal contact networks. These will provide t... ## Key facts - **NIH application ID:** 10934148 - **Project number:** 1R01AI184331-01 - **Recipient organization:** UNIVERSITY OF WASHINGTON - **Principal Investigator:** Julianne Meisner - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $720,456 - **Award type:** 1 - **Project period:** 2024-09-17 → 2029-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10934148 ## Citation > US National Institutes of Health, RePORTER application 10934148, Disease transmission along complex human-animal networks: a novel method for improving zoonotic disease modeling (1R01AI184331-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10934148. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*