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Plasmodium falciparum malaria still poses one of the greatest threats tohuman life with over 200 million cases globally leading to half-million deathsannually. Of these, 90% of cases and of the mortality occurs in sub-SaharanAfrica, mostly among children. Although malaria prediction systems are centralto the 2016-2030 malaria Global Technical Strategy, currently these areinadequate at capturing and estimating the burden of disease in highly endemiccountries. We developed and validated a computational system that exploits thepredictive power of current Machine Learning approaches on 22-years ofprospective data from the high-transmission holoendemic malariaurban-densely-populated sub-Saharan West-Africa metropolis of Ibadan. Ourdataset of >9x104 screened study participants attending our clinical andcommunity services from 1996 to 2017 contains monthly prevalence, temporal,environmental and host features. Our Locality-specific Elastic-Net basedMalaria Prediction System (LEMPS) achieves good generalization performance,both in magnitude and direction of the prediction, when tasked to predictmonthly prevalence on previously unseen validation data (MAE<=6x10-2,MSE<=7x10-3) within a range of (+0.1 to -0.05) error-tolerance which isrelevant and usable for aiding decision-support in a holoendemic setting. LEMPSis well-suited for malaria prediction, where there are multiple features whichare correlated with one another, and trading-off betweenregularization-strength L1-norm and L2-norm allows the system to retainstability. Data-driven systems are critical for regionally-adaptablesurveillance, management of control strategies and resource allocation acrossstretched healthcare systems.

Data-Driven Malaria Prevalence Prediction in Large Densely-Populated  Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning  Approaches and 22-years of Prospectively Collected Data