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Two-step light gradient boosted model to identify human west nile virus infection risk factor in Chicago

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  • Guangya Wan
  • Joshua Allen
  • Weihao Ge
  • Shubham Rawlani
  • John Uelmen
  • Liudmila Sergeevna Mainzer
  • Rebecca Lee Smith

Abstract

West Nile virus (WNV), a flavivirus transmitted by mosquito bites, causes primarily mild symptoms but can also be fatal. Therefore, predicting and controlling the spread of West Nile virus is essential for public health in endemic areas. We hypothesized that socioeconomic factors may influence human risk from WNV. We analyzed a list of weather, land use, mosquito surveillance, and socioeconomic variables for predicting WNV cases in 1-km hexagonal grids across the Chicago metropolitan area. We used a two-stage lightGBM approach to perform the analysis and found that hexagons with incomes above and below the median are influenced by the same top characteristics. We found that weather factors and mosquito infection rates were the strongest common factors. Land use and socioeconomic variables had relatively small contributions in predicting WNV cases. The Light GBM handles unbalanced data sets well and provides meaningful predictions of the risk of epidemic disease outbreaks.

Suggested Citation

  • Guangya Wan & Joshua Allen & Weihao Ge & Shubham Rawlani & John Uelmen & Liudmila Sergeevna Mainzer & Rebecca Lee Smith, 2024. "Two-step light gradient boosted model to identify human west nile virus infection risk factor in Chicago," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0296283
    DOI: 10.1371/journal.pone.0296283
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    References listed on IDEAS

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    1. Christopher Daly & Joseph I Smith & Keith V Olson, 2015. "Mapping Atmospheric Moisture Climatologies across the Conterminous United States," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-33, October.
    2. repec:plo:pone00:0227160 is not listed on IDEAS
    3. repec:plo:pone00:0217854 is not listed on IDEAS
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