Spatial panorama of malaria prevalence in Africa under climate change and interventions scenarios

Autor: Sansoa A. Pedro, Francois M. M. Kakmeni, Ritter A. Guimapi, Frank T. Ndjomatchoua, James M. Mutunga, Henri E. Z. Tonnang
Rok vydání: 2018
Předmět:
General Computer Science
Climate Change
Health geography
Plasmodium falciparum
030231 tropical medicine
Population
Geographic Mapping
Climate change
Context (language use)
Mosquito Vectors
lcsh:Computer applications to medicine. Medical informatics
law.invention
03 medical and health sciences
0302 clinical medicine
law
Vector-borne disease
parasitic diseases
Prevalence
medicine
Animals
Humans
Transmission
030212 general & internal medicine
education
education.field_of_study
Research
Geographical information system (GIS)
Public Health
Environmental and Occupational Health
Models
Theoretical
medicine.disease
General Business
Management and Accounting
Basic reproduction number
Malaria
Network model
Transmission (mechanics)
Geography
Africa
Geographic Information Systems
lcsh:R858-859.7
Scale (map)
Cartography
Zdroj: International Journal of Health Geographics
International Journal of Health Geographics, Vol 17, Iss 1, Pp 1-13 (2018)
ISSN: 1476-072X
Popis: Background Malaria is highly sensitive to climatic variables and is strongly influenced by the presence of vectors in a region that further contribute to parasite development and sustained disease transmission. Mathematical analysis of malaria transmission through the use and application of the value of the basic reproduction number (R0) threshold is an important and useful tool for the understanding of disease patterns. Methods Temperature dependence aspect of R0 obtained from dynamical mathematical network model was used to derive the spatial distribution maps for malaria transmission under different climatic and intervention scenarios. Model validation was conducted using MARA map and the Annual Plasmodium falciparum Entomological Inoculation Rates for Africa. Results The inclusion of the coupling between patches in dynamical model seems to have no effects on the estimate of the optimal temperature (about 25 °C) for malaria transmission. In patches environment, we were able to establish a threshold value (about α = 5) representing the ratio between the migration rates from one patch to another that has no effect on the magnitude of R0. Such findings allow us to limit the production of the spatial distribution map of R0 to a single patch model. Future projections using temperature changes indicated a shift in malaria transmission areas towards the southern and northern areas of Africa and the application of the interventions scenario yielded a considerable reduction in transmission within malaria endemic areas of the continent. Conclusions The approach employed here is a sole study that defined the limits of contemporary malaria transmission, using R0 derived from a dynamical mathematical model. It has offered a unique prospect for measuring the impacts of interventions through simple manipulation of model parameters. Projections at scale provide options to visualize and query the results, when linked to the human population could potentially deliver adequate highlight on the number of individuals at risk of malaria infection across Africa. The findings provide a reasonable basis for understanding the fundamental effects of malaria control and could contribute towards disease elimination, which is considered as a challenge especially in the context of climate change.
Databáze: OpenAIRE
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