2208034

US-UK Collab: Cross-Scale Dynamics of LASV Spillover within Human-Driven Ecosystems

The transmission of animal diseases to humans has significant consequences for public health and economies globally, as evidenced by the COVID-19 pandemic. When evaluating disease risks, policy decisions often rely on data visualization techniques, such as risk maps. However, there is a limited understanding of how broad-scale risk maps relate to local-scale processes that affect disease transmission from animals to humans.

For example, risk maps are often missing vital information on how broad associations between disease risk and factors such as climate, land-use, and poverty are driven by interactions between humans, animals, and the environment that affect the probability of human infection. On the other hand, localized studies revealing fine-scale disease processes are often poorly situated to inform projections of risk at broader spatial scales.

This project bridges this gap through a study of Lassa fever, a rodent-borne hemorrhagic fever of public health significance in West Africa and a global health priority. Field studies are conducted to examine human-environment interactions that support reservoir populations and behaviors that result in Lassa virus exposures. These data can improve our understanding of risk factors and inform public health policies.

This study uses participatory methods that engage local communities at the forefront of global health challenges in the construction of knowledge and management strategies. It also contributes to public health via the production of robust data products for scientists and decision-makers and improved epidemic preparedness.

This project uses a fine-scale quantitative and participatory modeling approach that explicitly integrates results from local-scale field studies into broad-scale risk models to identify the patterns and processes that drive spillover of Lassa virus within human-driven ecosystems. Field studies sample across known broad-scale drivers of Lassa fever risk to understand how local-scale processes vary across scale (e.g., how reservoir population dynamics are impacted by human land-use or how poverty translates to high-risk human behavior, etc.).

Data on rodent population dynamics, movement, and infection are combined with data from participatory activity mapping and ethno-epidemiological research to capture the anthropogenic factors that construct pathways for zoonotic spillover. For example, through practices that modify environments, pathogen dynamics within reservoir hosts, and the human-reservoir interface.

These local-scale analyses inform a set of interface models that can help to unpack the processes that determine the spatial and temporal distribution of Lassa virus and risky human exposures in relation to landscape dynamics. Model outcomes provide scenarios for participatory examination of disease control interventions in the context of competing risks, e.g., poverty and food insecurity.

Finally, data from empirical studies and emergent patterns from the interface models are integrated back into existing broad-scale regression-based risk models. Local-scale studies and model predictions can therefore fill key gaps in our understanding of how risk is propagated across scales and be used to inform ongoing disease management efforts for Lassa fever and zoonotic spillover more generally.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The Pennsylvania State University

THE GOAL OF THIS FUNDING OPPORTUNITY, "ECOLOGY AND EVOLUTION OF INFECTIOUS DISEASES", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF21609

47.074 - Biological Sciences

Division of Environmental Biology (DEB) [NSF]

University Park, Pennsylvania 16802-1503 United States

Single Zip Code

Ecology and Evolution of Infectious Diseases (21-609)

Amendment Since initial award the total obligations have increased 16% from $2,627,212 to $3,046,832.