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Int J Health Geogr. 2016 Jul 30;15(1):26. doi: 10.1186/s12942-016-0051-y.

Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam.

Author information

1
Spatial Health Metrics Group, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya. ckabaria@kemri-wellcome.org.
2
National Malaria Control Programme, Ministry of Health and Social Welfare, Dar es Salaam, Tanzania.
3
Swiss Tropical and Public Health Institute, Dar es Salaam, Tanzania.
4
Spatial Health Metrics Group, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
5
Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
6
Department of Geography, Université de Namur, Rue de Bruxelles 61, 5000, Namur, Belgium.
7
Biological Control and Spatial Ecology, Université Libre de Bruxelles CP160/12, Av. F.D. Roosevelt 50, 1050, Brussels, Belgium.

Abstract

BACKGROUND:

With more than half of Africa's population expected to live in urban settlements by 2030, the burden of malaria among urban populations in Africa continues to rise with an increasing number of people at risk of infection. However, malaria intervention across Africa remains focused on rural, highly endemic communities with far fewer strategic policy directions for the control of malaria in rapidly growing African urban settlements. The complex and heterogeneous nature of urban malaria requires a better understanding of the spatial and temporal patterns of urban malaria risk in order to design effective urban malaria control programs. In this study, we use remotely sensed variables and other environmental covariates to examine the predictability of intra-urban variations of malaria infection risk across the rapidly growing city of Dar es Salaam, Tanzania between 2006 and 2014.

METHODS:

High resolution SPOT satellite imagery was used to identify urban environmental factors associated malaria prevalence in Dar es Salaam. Supervised classification with a random forest classifier was used to develop high resolution land cover classes that were combined with malaria parasite prevalence data to identify environmental factors that influence localized heterogeneity of malaria transmission and develop a high resolution predictive malaria risk map of Dar es Salaam.

RESULTS:

Results indicate that the risk of malaria infection varied across the city. The risk of infection increased away from the city centre with lower parasite prevalence predicted in administrative units in the city centre compared to administrative units in the peri-urban suburbs. The variation in malaria risk within Dar es Salaam was shown to be influenced by varying environmental factors. Higher malaria risks were associated with proximity to dense vegetation, inland water and wet/swampy areas while lower risk of infection was predicted in densely built-up areas.

CONCLUSIONS:

The predictive maps produced can serve as valuable resources for municipal councils aiming to shrink the extents of malaria across cities, target resources for vector control or intensify mosquito and disease surveillance. The semi-automated modelling process developed can be replicated in other urban areas to identify factors that influence heterogeneity in malaria risk patterns and detect vulnerable zones. There is a definite need to expand research into the unique epidemiology of malaria transmission in urban areas for focal elimination and sustained control agendas.

KEYWORDS:

Boosted Regression Trees; Dar es Salaam; Remote sensing; Urban malaria

PMID:
27473186
PMCID:
PMC4967308
DOI:
10.1186/s12942-016-0051-y
[Indexed for MEDLINE]
Free PMC Article

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