Tuesday, 9 January 2018: 9:30 AM
Room 17B (ACC) (Austin, Texas)
An extensive literature review has shown that climate has an important impact on malaria, a vector-borne disease which is a public health problem, particularly in the Sub-Saharan Africa. Climate information is useful to understand the malaria behavior at regional scale. This study consists of simulations of malaria parameters using the Liverpool Malaria Model (LMM). Malaria model outputs are compared with observed malaria cases recorded by the National Malaria Control Program in Senegal (NMCP). The different reanalysis datasets used to drive the LMM are: 20th century reanalysis, NCEP reanalysis, ERA40, and ERA-Interim. The simulated malaria parameters are EIR (Entomological Inoculation Rates), HBR (Human Biting Rate), and Nm (Number of adult mosquitoes). The relationship between observed and simulated malaria parameters is presented. In addition, we employ the S4CAST model (Sea Surface temperature based Statistical Seasonal Forecast model) to explore the malaria outbreaks predictability over Sahel. We use observed SST as predictor field due to its direct influence on rainfall and temperature, and also others related variables like malaria. We examine the leading MCA covariability mode to evaluate and quantify the predictability of different variables in relationship with SST.
The findings highlight the association between malaria parameters and two climatic factors, temperature and rainfall. A lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November corresponding to the second peak of temperature occurring in October. Observed malaria data from the National Malaria control Programme (NMCP) and outputs from meteorological data used in this study were compared. The malaria model outputs are consistent with observed malaria of the National Malaria control Programme (NMCP). Explored simulations with reanalysis data sets over a longer time period show a significantly decreased during the 1970s and 1980s over Senegal.
With preliminary results of this work, it is found a causal or coincident relationship between El Niño and malaria parameters. However, these findings were highlighted by coupling the LMM UNILIV and S4CAST UCM models in order to predict malaria parameters some months in advance.
In the framework of applying forecast on health issue, these results are expected to be useful for decision makers who plan public health measures in affected countries in Sahel and elsewhere.
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