  Plasmodium falciparum malaria still poses one of the greatest threats to
human life with over 200 million cases globally leading to half-million deaths
annually. Of these, 90% of cases and of the mortality occurs in sub-Saharan
Africa, mostly among children. Although malaria prediction systems are central
to the 2016-2030 malaria Global Technical Strategy, currently these are
inadequate at capturing and estimating the burden of disease in highly endemic
countries. We developed and validated a computational system that exploits the
predictive power of current Machine Learning approaches on 22-years of
prospective data from the high-transmission holoendemic malaria
urban-densely-populated sub-Saharan West-Africa metropolis of Ibadan. Our
dataset of >9x104 screened study participants attending our clinical and
community services from 1996 to 2017 contains monthly prevalence, temporal,
environmental and host features. Our Locality-specific Elastic-Net based
Malaria Prediction System (LEMPS) achieves good generalization performance,
both in magnitude and direction of the prediction, when tasked to predict
monthly prevalence on previously unseen validation data (MAE<=6x10-2,
MSE<=7x10-3) within a range of (+0.1 to -0.05) error-tolerance which is
relevant and usable for aiding decision-support in a holoendemic setting. LEMPS
is well-suited for malaria prediction, where there are multiple features which
are correlated with one another, and trading-off between
regularization-strength L1-norm and L2-norm allows the system to retain
stability. Data-driven systems are critical for regionally-adaptable
surveillance, management of control strategies and resource allocation across
stretched healthcare systems.
