Brousse, O;
Georganos, S;
Demuzere, M;
Dujardin, S;
Lennert, M;
Linard, C;
Snow, RW;
... van Lipzig, NPM; + view all
(2020)
Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities?
Environmental Research Letters
, 15
(12)
, Article 124051. 10.1088/1748-9326/abc996.
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Abstract
Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate (Pf PR2−10) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling Pf PR2−10. Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower Pf PR2−10 (5%–30%) than rural areas (15%–40%). The Pf PR2−10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher Pf PR2−10. Informal settlements—represented by the LCZ 7 (lightweight lowrise)—have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.
| Type: | Article |
|---|---|
| Title: | Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1748-9326/abc996 |
| Publisher version: | https://doi.org/10.1088/1748-9326/abc996 |
| Language: | English |
| Additional information: | Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | malaria, sub-Saharan africa, local climate zones, urban malaria modeling, random forest modeling, urban health, WUDAPT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10118695 |
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