A Data-Driven Study of Mosquito Patterns in Chicago (2007-2024) with Machine Learning Techniques

We apply data analytics to the publicly available and recently updated Chicago 2007-2024 Mosquito Database. In this database, 195 traps have been deployed in Chicago, Illinois, USA, from 2007 to 2024. Every year, from late May to early October, public health workers in Chicago set up mosquito traps scattered across the city. These traps collect mosquitoes, which are then partitioned into batches of fifty specimens. Each batch has been assessed using Polymerase Chain Reaction (PCR) for the presence of West Nile virus before the end of each week. The database records include the number of mosquitoes, the mosquito species, geographical information, and whether West Nile virus is present in each cohort. In its first part, this work explores the application of mosquito data analytics to the manually collected data, focusing on the potential to identify trends, find the outbreaks, and localize hotspots to support vector control strategies. In its second part, we investigate at what extent a virus-positive batch can be predicted using the rest of the variables recorded in the database, showing that an AUC score of approximately 81% can be achieved on a 2-year held out subset without including weather data.