Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 16223
- Type
- D - Journal article
- DOI
-
10.1038/s41598-020-72575-6
- Title of journal
- Scientific Reports
- Article number
- ARTN 15918
- First page
- -
- Volume
- 10
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
22
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Malaria hinders sub-Saharan development where it is critical to adapt clinical pathways provision to prevalence changes to reduce childhood mortality. Contrary to explicit models, our novel d short-lead REMP system is accurate within an all-year-around malaria 3-million inhabitants urban setting and is currently used in our clinics in Nigeria. Its impact resides on its simplicity as it must be deployed in resource poor settings; informs ahead of time resource allocation of malaria diagnosis and treatment services; exploits our novel and unique 22-years longitudinal quality-controlled open-source data. First time a simple yet-effective data-driven EN based approach is prospectively validated at scale.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -