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A data-parsimonious model for long-term risk assessments of West Nile virus spillover

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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.

Data availability

The datasets used in this study, including weather covariates and time series of incident West Nile virus cases, as well as the code used to perform the analyses and generate the results, are available in the KSUNetSE GitHub repository: https://github.com/KSUNetSE/Eco-Epi-Model. All generated results, including model predictions and estimated distributions, are provided in this published article and its Appendix and Supplementary Information files.

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Funding

This work was funded by the USDA National Institute of Food and Agriculture award number 2022-67015-38059 via the NSF/NIH/USDA/BBSRC/BSF/NSFC Ecology and Evolution of Infectious Diseases program, and the United States Department of Agriculture ARS under agreement number 58-3022-1-010.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA

    Saman Hosseini, Matin Marjani & Caterina Scoglio

  2. Foreign Arthropod-Borne Animal Diseases Research Unit, USDA-ARS, Manhattan, KS, USA

    Lee W. Cohnstaedt

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  1. Saman Hosseini
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  2. Lee W. Cohnstaedt
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  3. Matin Marjani
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  4. Caterina Scoglio
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Contributions

Hosseini, Saman: Conceptualization, data curation, formal analysis, investigation, methodology, validation, visualization, writing – original draft, writing – review & editing. Cohnstaedt, Lee: Conceptualization, methodology, resources, supervision, validation, writing – original draft, writing, review & editing. Marjani, Matin: Data curation, validation, visualization, writing – review & editing. Scoglio, Caterina: Conceptualization, formal analysis, funding acquisition, methodology, project administration, supervision, validation, writing original draft, writing, review & editing.

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Saman Hosseini.

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Hosseini, S., Cohnstaedt, L.W., Marjani, M. et al. A data-parsimonious model for long-term risk assessments of West Nile virus spillover.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-47413-w

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