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A Multi-detection Assay for Malaria Transmitting Mosquitoes
Published on: February 28, 2015
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Malaria in Senegal: Recent and Future Changes Based on Bias-Corrected CMIP6 Simulations
Ibrahima Diouf1, Jacques-André Ndione2, Amadou Thierno Gaye1
1Laboratoire de Physique de l'Atmosphère et de l'Océan-Siméon Fongang, Ecole Supérieure Polytechnique de l'Université Cheikh Anta Diop (UCAD), BP 5085, Dakar-Fann, Dakar 10700, Senegal.
Tropical Medicine and Infectious Disease
|November 10, 2022
Summary
Climate change significantly impacts malaria transmission. This study used the Liverpool Malaria Model to project future malaria incidence in Senegal, revealing regional decreases under extreme scenarios, aiding control efforts.
Area of Science:
- Environmental science
- Climate change impacts on health
- Epidemiology
Background:
- Climate conditions critically influence malaria transmission dynamics.
- Temperature affects the parasite's life cycle within mosquitoes.
- Rainfall patterns impact mosquito population development.
Purpose of the Study:
- To simulate the spatiotemporal variation of malaria incidence in Senegal using the Liverpool Malaria Model (LMM).
- To project future malaria transmission trends under different climate change scenarios (SSP126, SSP245, SSP585).
- To inform malaria control strategies in Senegal.
Main Methods:
- Utilized the Liverpool Malaria Model (LMM) for simulations.
- Employed WATCH Forcing Data applied to ERA-Interim (WFDEI) as a reference.
- Incorporated biased-corrected CMIP6 model data for historical and future projections.
Main Results:
- Projected significant temperature increases, particularly in eastern Senegal under SSP245 and SSP585 scenarios.
- Validated LMM's ability to simulate historical malaria seasonality, with peak transmission from September to November.
- Indicated a potential decrease in malaria incidence in some regions under future extreme scenarios.
Conclusions:
- Climate change, particularly rising temperatures, will alter malaria transmission patterns in Senegal.
- The LMM provides valuable insights for predicting malaria incidence under various climate scenarios.
- Findings are crucial for effective malaria control planning and implementation in Senegal.
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