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July 31, 2025PNAS NexusOpen Access

Using mosquito and arbovirus data to computationally predict West Nile virus in unsampled areas of the Northeast United States

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Authors

JMJoseph R. McMillanJSJames D. SunLCLuis Fernando Chaves

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Overview

Analysis predicts West Nile virus detection in mosquitoes, indicating potential human risk across multiple states.

Key Points

  • Predicted odds of human cases were significantly linked to WNV detection in mosquitoes, enhancing risk assessment.
  • Using machine learning, the study analyzed 20 years of data to optimize predictive models for WNV activity.
  • The methodology employs a mapping framework to estimate risk in unsampled areas at a 4 × 4 km resolution across states.
  • Improving mosquito surveillance data interpretation may lead to better public health responses to WNV threats.

Cite This Study

McMillan et al. (2025) studied this question.

synapsesocial.com/papers/68af4754ad7bf08b1ead3f45https://doi.org/10.1093/pnasnexus/pgaf227
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