Scaling Lassa Virus Dynamics within Anthropogenic Ecosystems (SCAPES) study: a mixed-methods observational cohort study of humans, rodents, and landscapes in Nigeria.
Friant, S.; Simons, D.; Bjornstad, O. N.; Gibb, R.; Harden, C.; Imirzian, N.; Jones, K. E.; Smith, A.; Thompson, K. E.; Meremikwu, M.; Moses, L. M.; Redding, D. W.
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IntroductionZoonotic spillover driven by human activity remains a critical challenge. Research focusing on disease ecology or human-animal interactions provide detailed but often siloed local insights disconnected from global processes and policy. Risk maps are commonly used to inform decisions, yet their connection to local-scale drivers of transmission remains poorly understood. This study bridges these gaps through a cross-scale study of Lassa fever, a rodent-borne hemorrhagic fever of public health significance in West Africa and a WHO priority pathogen. We use a fine-scale quantitative and participatory modelling approach that explicitly integrates into broad-scale risk models to identify the patterns and processes that drive spillover within human-driven ecosystem at the animal-human interface. Lassa Fever epidemics are dominated by endemic and seasonal spillover of Mammarenavirus lassaense (LASV) from rodent reservoirs to humans within a rural context, positioning LASV as a uniquely tractable system in which to study spillover. Methods and AnalysisWe are currently conducting parallel observational studies of humans, rodents, and landscapes explicitly designed to feed into a cross-scale quantitative modelling framework. By integrating data on landcover, rodent dynamics, human behavior, and LASV infection, we examine how anthropogenic activities shape LASV ecology and human exposure. These data will inform spatial models of the human-rodent-LASV interface to predict key drivers of exposure risk. Finally, we integrate data from our empirical studies and emergent patterns from our interface model(s) into an existing broad-scale regression-based risk model. Our local-scale study and model predictions will clarify how zoonotic risk propagates from local to regional scales, providing evidence to inform disease management efforts for Lassa fever, and zoonotic spillover more generally. Ethics and disseminationHuman subjects and animal research approvals were obtained from multiple ethical review boards, including the National Health Research Ethics Committee and National Veterinary Research Institute in Nigeria. The research protocol adheres to principles of ethical community-based health research including fostering collaboration, enhancing transparency, supporting capacity building, and disseminating results to communities and public health authorities. STRENGTHS AND LIMITATIONS OF THIS STUDYO_ST_ABSStrengthsC_ST_ABS[bullet] Interdisciplinary approach: Integrates ecological, epidemiological, and anthropological methodologies for a comprehensive understanding of LASV dynamics. [bullet]Predictive modeling: Utilizes modeling techniques to integrate diverse data sets, offering valuable insights for mechanistic understanding, forecasting, and intervention planning. [bullet]Community engagement: Emphasizes ethical practices and active involvement of local communities, ensuring culturally sensitive research, guiding subsequent intervention design and enhancing data relevance. [bullet]Geographical focus: Conducted in Nigeria within West Africa, a region critically affected by LASV, addressing a significant gap in current research and informing public health strategies. Limitations[bullet] Complexity of integrating diverse data sets: Conceptual challenges in effectively integrating data from varied disciplines, which may require novel, integrative methodologies. [bullet]Scaling local studies to regional interpretations of hotspots: Unknown capacity of geographically restricted, fine-scale data collections to make broader scale generalizations in risk modelling. [bullet]Dependence on external factors: External factors such as political and economic stability, accessibility of remote areas, and public health policies might impact the studys execution and outcomes.
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