Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities
Bibliographic Data
| ID | 15544211 |
|---|---|
| Authors | Oscar Brousse (0000-0002-7364-710X, University College London, corresponding author), Stefanos Georganos (0000-0002-0001-2058, Université Libre de Bruxelles), Matthias Demuzere (0000-0003-3237-4077, Ghent University), Sébastien Dujardin (0000-0002-8451-8258, University of Namur), Moritz Lennert (0000-0002-2870-4515, Université Libre de Bruxelles), Catherine Linard (0000-0002-0819-7755, University of Namur), Ronald W Snow (0000-0003-3725-6088, Angkor Hospital for Children), Wim Thiery (0000-0002-5183-6145, Vrije Universiteit Brussel), Nicole Van Lipzig (0000-0003-2899-4046, KU Leuven), N P M van Lipzig |
| Year | 2020 |
| Volume | 15 |
| Issue | 12 |
| Pages | 124051-124051 |
| Publication date | 2020-11-11 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/abc996 |
| PMID | 35211191 |
| OpenAlex | W3104956012 |
| Language | EN |
| Citations received | 3 |
| References cited | 67 |
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 PR 2-10 ) 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 PR 2-10 . 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 PR 2-10 (5%-30%) than rural areas (15%-40%). The Pf PR 2-10 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 PR 2-10 . 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
Biology · Environmental health · Geography · Human settlement · Land cover · Land use · Malaria · Rural area · Socioeconomics · Urbanization · Environmental Science · Malaria Research and Control · Medicine · Mosquito-borne diseases and control · Species Distribution and Climate Change · Ecology
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| Unique citing works | 3 |
|---|---|
| Citations per year | 0,75 |
| Citation span | 2022 - 2023 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |