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


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          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.

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            Google Earth Engine: Planetary-scale geospatial analysis for everyone

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              The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015

              Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015 and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.

                Author and article information

                Environ Res Lett
                Environ Res Lett
                Environmental research letters : ERL [Web site]
                16 February 2022
                15 December 2020
                23 February 2022
                : 15
                : 12
                : 124051
                [1 ]Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
                [2 ]UCL Institute for Environmental Design and Engineering, University College London, London, United Kingdom
                [3 ]Department of Geosciences, Environment and Society, Université Libre de Bruxelles, Brussels, Belgium
                [4 ]Department of Geography, Ruhr-University Bochum, Bochum, Germany
                [5 ]Department of Environment, Ghent University, Ghent, Belgium
                [6 ]Department of Geography, Université de Namur, Namur, Belgium
                [7 ]Population and Health Unit, Kenya Medical Research Institute Wellcome Trust, Nairobi, Kenya
                [8 ]Department of Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
                [9 ]Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium
                Author notes
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                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.


                malaria,sub-saharan africa,local climate zones,urban malaria modeling,random forest modeling,urban health,wudapt


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