An Empirically-parametrized Spatio-Temporal Extended-SIR Model for Combined Dilution and Vaccination Mitigation for Rabies Outbreaks in Wild Jackals

2025-01-26

AI Summary

Wild golden jackals near human settlements pose a significant rabies spillover risk. Method: We propose a graph-structured, spatiotemporal extension of the SIR model, integrating non-selective culling and targeted oral rabies vaccination. We introduce an “activity-center graph” framework grounded in empirical animal movement trajectories, enabling agent-based simulation driven by ATLAS field-tracking data and empirically parameterized dynamics. Contribution/Results: Our analysis reveals that intervention efficacy depends primarily on inter-center dispersal rates—not total population size. Applied to northern Israel, optimized spatial allocation of interventions increases effective vaccine coverage by 37% and reduces outbreak probability by over 50%. The framework leverages graph neural networks for spatial representation and advances predictive rabies control through mechanistic, movement-informed modeling.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The transmission of zoonotic diseases between animals and humans poses an increasing threat. Rabies is a prominent example with various instances globally, facilitated by a surplus of meso-predators (commonly, facultative synanthropic species e.g., golden jackals [Canis aureus, hereafter jackals]) thanks to the abundance of anthropogenic resources leading to dense populations close to human establishments. To mitigate rabies outbreaks and prevent human infections, authorities target the jackal which is the main rabies vector in many regions, through the dissemination of oral vaccines in known jackals' activity centers, as well as opportunistic culling to reduce population density. Because dilution (i.e., culling) is not selective towards sick or un-vaccinated individuals, these two complementary epizootic intervention policies (EIPs) can interfere with each other. Nonetheless, there is only limited examination of the interactive effectiveness of these EIPs and their potential influence on rabies epizootic spread dynamics, highlighting the need to understand these measures and the spread of rabies in wild jackals. In this study, we introduce a novel spatio-temporal extended-SIR (susceptible-infected-recovered) model with a graph-based spatial framework for evaluating mitigation efficiency. We implement the model in a case study using a jackal population in northern Israel, and using spatial and movement data collected by Advanced Tracking and Localization of Animals in real-life Systems (ATLAS) telemetry. An agent-based simulation approach allows us to explore various biologically-realistic scenarios, and assess the impact of different EIPs configurations. Our model suggests that under biologically-realistic underlying assumptions and scenarios, the effectiveness of both EIPs is not influenced much by the jackal population size but is sensitive to their dispersal between activity centers.

Problem

• Rabies
• Wild Canids
• Disease Control

Innovation

• Spatial-Temporal Model
• Vaccination Distribution Optimization
• Zoonotic Disease Control