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The ongoing Yemen cholera outbreak has been deemed one of the worst choleraoutbreaks in history, with over a million people impacted and thousands dead.Triggered by a civil war, the outbreak has been shaped by various political,environmental, and epidemiological factors and continues to worsen. Whilecholera has several effective treatments, the untimely and inefficientdistribution of existing medicines has been the primary cause of choleramortality. With the hope of facilitating resource allocation, variousmathematical models have been created to track the Yemeni outbreak and identifyat-risk administrative divisions, called governorates. Existing models are notpowerful enough to accurately and consistently forecast cholera cases pergovernorate over multiple timeframes. To address the need for a complex,reliable model, we offer the Cholera Artificial Learning Model (CALM); a systemof 4 extreme-gradient-boosting (XGBoost) machine learning models that forecastthe number of new cholera cases a Yemeni governorate will experience from atime range of 2 weeks to 2 months. CALM provides a novel machine learningapproach that makes use of rainfall data, past cholera cases and deaths data,civil war fatalities, and inter-governorate interactions represented acrossmultiple time frames. Additionally, the use of machine learning, along withextensive feature engineering, allows CALM to easily learn complex non-linearrelations apparent in an epidemiological phenomenon. CALM is able to forecastcholera incidence 2 weeks to 2 months in advance within a margin of just 5cholera cases per 10,000 people in real-world simulation.

Forecasting the 2017-2018 Yemen Cholera Outbreak with Machine Learning