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

Clicks: 67
ID: 283586
2019
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #438 of 803 articles by views in arXiv

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
fernandez-reyes2019datadriven Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Biobele J. Brown; Alexander A. Przybylski; Petru Manescu; Fabio Caccioli; Gbeminiyi Oyinloye; Muna Elmi; Michael J. Shaw; Vijay Pawar; Remy Claveau; John Shawe-Taylor; Mandayam A. Srinivasan; Nathaniel K. Afolabi; Adebola E. Orimadegun; Wasiu A. Ajetunmobi; Francis Akinkunmi; Olayinka Kowobari; Kikelomo Osinusi; Felix O. Akinbami; Samuel Omokhodion; Wuraola A. Shokunbi; Ikeoluwa Lagunju; Olugbemiro Sodeinde; Delmiro Fernandez-Reyes
Journal arXiv
Year 2019
DOI
DOI not found
URL
http://arxiv.org/abs/1906.07502v1
Keywords
stat.ap cs.lg stat.ml j.3; i.5.4

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.