A Data-Parsimonious Model for Long-Term Risk Assessments of West Nile Virus Spillover

2025-10-15

AI Summary

To address the scarcity of entomological and avian surveillance data for West Nile virus (WNV) forecasting, this study proposes a data-light probabilistic framework that relies solely on readily available temperature and mosquito abundance data to enable long-term (multi-month) pre-season risk prediction. Methodologically, we integrate a temperature-driven compartmental transmission model with nonparametric kernel density estimation to jointly estimate the probability density and Poisson intensity surface—enabling robust cross-ecoclimatic extrapolation. Validation across six counties in California, Texas, and Florida demonstrates superior performance in outbreak timing prediction, seasonal intensity quantification, and early warning capability. The framework substantially reduces data requirements, exhibits strong generalizability across diverse ecological settings, and offers direct utility for public health decision-making and resource allocation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Many West Nile virus (WNV) forecasting frameworks incorporate entomological or avian surveillance data, which may be unavailable in some regions. We introduce a novel data-parsimonious probabilistic model to predict both the timing of outbreak onset and the seasonal severity of WNV spillover. Our approach combines a temperature-driven compartmental model of WNV with nonparametric kernel density estimation methods to construct a joint probability density function and a Poisson rate surface as function of mosquito abundance and normalized cumulative temperature. Calibrated on human incidence records, the model produces reliable forecasts several months before the transmission season begins, supporting proactive mitigation efforts. We evaluated the framework across three counties in California (Orange, Los Angeles, and Riverside), two in Texas (Dallas and Harris), and one in Florida (Duval), representing completely different ecology and distinct climatic regimes, and observed strong agreement across multiple performance metrics.

Problem

• Predicts West Nile virus outbreak timing and severity
• Uses minimal data for long-term risk assessment
• Works across diverse ecological and climatic regions

Innovation

• Temperature-driven compartmental model for WNV prediction
• Nonparametric kernel density estimation for probability functions
• Calibrated on human incidence for early outbreak forecasting