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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere0296283
JournalPloS one
Volume19
Issue number1 January
DOIs
StatePublished - Jan 5 2024

ASJC Scopus subject areas

  • General

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