ZV-Sim: Probabilistic Simulation Framework for Pre-emergent Novel Zoonose Tracking

TL;DR

The paper tackles the challenge of early identification of emergent zoonotic spillover by integrating diverse sensing data into a probabilistic, agent-based simulation framework. ZV-Sim provides modular components for Human and Animal Presence agents, motion models, hazard-based spread, and Bayesian zoonotic-origin inference, all designed for extensibility and Monte Carlo analysis. Through illustrative datasets and simple models, the authors demonstrate how model choices influence inferred zoonotic origin and support multi-trial analysis. The framework aims to enable flexible, data-fused tracking of novel zoonoses and to serve as a platform for future data-rich extensions and real-world evaluations.

Abstract

ZV-Sim is an open-source, modular Python framework for probabilistic simulation and analysis of pre-emergent novel zoonotic diseases using pervasive sensing data. It incorporates customizable Human and Animal Presence agents that leverage known and simulated location data, contact networks, and illness reports to assess and predict disease origins and spread. The framework supports Monte Carlo experiments to analyze outcomes with various user-defined movement and probability models. Although initial models are basic and illustrative, ZV-Sim's extensible design facilitates the integration of more sophisticated models as richer data become available, enhancing future capabilities in zoonotic disease tracking. The source code is publicly available \href{https://github.com/jmaff/zv-sim}{\underline{\textit{here}}}.

Figures (3)

• Figure 1: ZV-Sim Architecture
• Figure 2: Included PyGame display module implementation. Human agents are colored red if sick.
• Figure 3: Probability of novel zoonotic origin for the illnesses experienced by each human agent, compared across motion models None (left), Random Walk (center), and Noisy Linear Interpolation (right)