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

TL;DR

ZV-Sim is an open-source Python framework that uses probabilistic simulation and pervasive sensing data to track and predict the emergence and spread of novel zoonotic diseases.

Contribution

It introduces a modular, extensible simulation framework for pre-emergent zoonoses that integrates diverse data sources and supports customizable models for disease tracking.

Findings

Framework supports Monte Carlo experiments for outcome analysis.

Initial models are basic but demonstrate potential for future enhancements.

Open-source code available for community use and development.

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}}}.