# Graph-based epidemic modeling of West Nile Virus: Forecasting and containment

## TL;DR

This paper introduces a new computational framework to model and predict West Nile virus spread, helping guide effective containment strategies.

## Contribution

A modular, dynamic modeling framework for simulating WNV transmission and evaluating time-sensitive interventions.

## Key findings

- Early, dual-target strategies reduce transmission and human exposure risk.
- The framework supports scenario testing for proactive outbreak preparedness.
- Intervention timing and intensity significantly affect epidemic progression.

## Abstract

The increasing prevalence of vector-borne diseases like West Nile virus (WNV) highlights the critical need for predictive modeling tools that can guide public health decision-making, particularly given the absence of effective vaccines. We developed a modular computational framework that simulates and analyzes WNV transmission dynamics through compartmental models capturing the intricate ecological interactions among avian hosts, mosquito vectors, and human populations. Our system integrates epidemiological parameters with customizable intervention mechanisms, facilitating the assessment of scenario-specific mitigation approaches. Distinguishing itself from conventional static models, this framework enables users to model dynamic, time-sensitive interventions including targeted mosquito control and strategic bird population management—the two principal containment strategies currently employed against WNV. Using simulations that reflect realistic outbreak scenarios, we evaluated how varying intervention intensities and implementation timings affect epi- demic progression. Our findings reveal that early implemented, dual-target strategies addressing both vector populations and avian reservoirs can substantially reduce transmission dynamics and minimize human exposure risk. This framework serves as a comprehensive decision-support platform for policymakers and vector control agencies, delivering mechanistic insights into the effectiveness of non-pharmaceutical interventions against zoonotic pathogens within complex ecological systems. The tool’s modular design and scenario-testing capabilities make it particularly valuable for proactive outbreak preparedness and evidence-based intervention planning.