Urban malaria in Africa: an environmental and socio-economic modelling approach for Yaoundé, Cameroon
Most of the existing predictive malaria risks spatial models use very broad spatial scales, and they are usually built-up for continental or national outlines. These models usually do not account for the complexity of socio-economic variables intervening into the malaria transmission process as well...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2010
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| In: |
Natural hazards
Year: 2010, Jahrgang: 55, Heft: 3, Pages: 599-619 |
| ISSN: | 1573-0840 |
| DOI: | 10.1007/s11069-009-9485-x |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s11069-009-9485-x
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| Verfasserangaben: | Roland Ngom, Alexander Siegmund |
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| 520 | |a Most of the existing predictive malaria risks spatial models use very broad spatial scales, and they are usually built-up for continental or national outlines. These models usually do not account for the complexity of socio-economic variables intervening into the malaria transmission process as well malaria prevention strategies. These spatial and thematic shortcomings are particularly interesting when looking at urban environments. This paper explains why a focus on urban malaria overcomes these shortcomings. A set of environmental variables derived from remotely sensed and ground climate station sources was used to build-up an ecological model. An original data collection process based on GPS measurements and retrospective interviews was introduced for the creation of various geoepidemiological and geosocial variables. Multinomial logistic models were used to predict and evaluate the contribution of various thematically separated groups of variables to malaria risk. Finally, it was possible to statistically and spatially evaluate the contribution of ecologic, socio-ecologic, socio-economic and behavioural characteristics of these groups of variables to malaria risks. | ||
| 650 | 4 | |a Ecological models | |
| 650 | 4 | |a Prevention | |
| 650 | 4 | |a Social models | |
| 650 | 4 | |a Spatial prediction | |
| 650 | 4 | |a Statistical prediction | |
| 650 | 4 | |a Urban malaria | |
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