Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities?

O. Brousse, S. Georganos, M. Demuzere, S. Dujardin, M. Lennert, C. Linard, R. W. Snow, W. Thiery, N. P.M. Van Lipzig

Research output: Contribution to journalArticlepeer-review

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 (PfPR2-10) 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 PfPR2-10. 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 PfPR2-10 (5%-30%) than rural areas (15%-40%). The PfPR2-10 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 PfPR2-10. 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.

Original languageEnglish
Article number124051
JournalEnvironmental Research Letters
Volume15
Issue number12
DOIs
Publication statusPublished - Dec 2020

Keywords

  • local climate zones
  • malaria
  • random forest modeling
  • sub-Saharan africa
  • urban health
  • urban malaria modeling
  • WUDAPT

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