Environmental Analysis of Hemorrhagic Fever in Iraq Using Machine Learning

Abstract

Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease with rising prevalence in Iraq. This research focused on investigating factors influencing CCHF spread in Dhi Qar Governorate, which has seen a substantial surge in cases. The problem was to uncover which environmental and climatic factors correlate with CCHF outbreaks. Meteorological data, rodent populations, vegetation indices, and epidemiological records were analyzed using data preprocessing techniques like interpolation and ARIMA modeling. Pearson correlation analysis was applied to quantify associations between CCHF cases and factors like temperature, humidity, rodent prevalence, and vegetation lushness. Results showed strong positive correlations of CCHF with rodent populations, temperature, solar radiation, and evapotranspiration. Negative correlations were found with humidity and vegetation health. The conclusions indicate environmental factors significantly influence CCHF outbreaks in Dhi Qar. This can inform prevention strategies targeting ecological and climatic drivers of the disease.