Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities?
Malaria burden is increasing in sub-Saharan cities because of their rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between the urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies....
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
IOP Publishing
2020
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| _version_ | 1826277470808047616 |
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| author | Brousse, O Georganos, S Demuzere, M Dujardin, S Lennert, M Linard, C Snow, RW Thiery, W van Lipzig, NPM |
| author_facet | Brousse, O Georganos, S Demuzere, M Dujardin, S Lennert, M Linard, C Snow, RW Thiery, W van Lipzig, NPM |
| author_sort | Brousse, O |
| collection | OXFORD |
| description | Malaria burden is increasing in sub-Saharan cities because of their rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between the 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 modelling malaria (PfPR 2-10) prevalence 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 modelling PfPR 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 PfPR 2-10 (5 % to 30 %) than rural areas (15 % to 40 %). The PfPR 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 PfPR 2-10. We also find that 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 modelling in urban malaria studies. |
| first_indexed | 2024-03-06T23:29:23Z |
| format | Journal article |
| id | oxford-uuid:6b7cb4f7-80f2-4a69-bc44-851b152eb6c6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:29:23Z |
| publishDate | 2020 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | oxford-uuid:6b7cb4f7-80f2-4a69-bc44-851b152eb6c62022-03-26T19:04:28ZCan we use local climate zones for predicting malaria prevalence across sub-Saharan African cities?Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6b7cb4f7-80f2-4a69-bc44-851b152eb6c6EnglishSymplectic ElementsIOP Publishing2020Brousse, OGeorganos, SDemuzere, MDujardin, SLennert, MLinard, CSnow, RWThiery, Wvan Lipzig, NPMMalaria burden is increasing in sub-Saharan cities because of their rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between the 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 modelling malaria (PfPR 2-10) prevalence 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 modelling PfPR 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 PfPR 2-10 (5 % to 30 %) than rural areas (15 % to 40 %). The PfPR 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 PfPR 2-10. We also find that 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 modelling in urban malaria studies. |
| spellingShingle | Brousse, O Georganos, S Demuzere, M Dujardin, S Lennert, M Linard, C Snow, RW Thiery, W van Lipzig, NPM Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| title | Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| title_full | Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| title_fullStr | Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| title_full_unstemmed | Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| title_short | Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities? |
| title_sort | can we use local climate zones for predicting malaria prevalence across sub saharan african cities |
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