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Application of Sub-Seasonal to Seasonal (S2S) Weather Forecasts in Predicting Recent Malaria Occurrence in Nigeria Using a Regional Scale Dynamical Malaria Model 1,2*Eniola Olaniyan, 2Elijah A. Adefisan, 2Ahmed A. Balogun, 3John A. Oyedepo, and Kamoru A. Lawal1,4 1Nigerian Meteorological Agency, Abuja, Nigeria. 2Department of Meteorology and Climate Science, Federal University of Technology, Akure, Nigeria. 3Federal University of Agriculture, Abeokuta, Nigeria. 4African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa. *Corresponding Author’s Contact Detail: E-mail Address ✉: olaniyan.eniola67@gmail.com Accepted June 22, 2020 The need to develop a robust Malaria Early Warning System (MEWS) in a right time-scale just for effective action, is growing in Nigeria due to the yearly recorded number of deaths from malaria disease. This paper uses two hierarchical evaluations technique to investigate the skill of VECTRI model in predicting malaria incidences in Nigeria. It evaluates the skill of three S2S models – China Meteorological Administration (CMA), European Centre for Medium-Range Weather Forecasts (ECMWF), and United Kingdom Meteorological Office (UKMO) in driving the VECTRI model. The simulated Entomological Inoculation Rate (EIR) from observation driven VECTRI is also evaluated with the simulated EIR from the three S2S models. The results show that VECTRI model driven by the observed station rainfall and temperature can simulate the hyper-endemic characteristics of malaria occurrence in Nigeria. This suggests that simulated EIR could be used as a measure of interpolation for reporting cases of malaria in Nigeria. The three S2S models used in driving the VECTRI-Model also reproduced the EIR that signifies the hyper-endemic nature of malaria cases in Nigeria, but with different characteristics over the climatological zones. Besides, the models also reproduced the inter-annual variability of the malaria cases over each zone with different inherent biases. The simulated EIR from the S2S-driven-VECTRI increases from the Gulf of Guinea (GoG) to the Sahel following the population profiles. Notwithstanding the inherent biases, the prospect of using VECTRI-Malaria model as a MEWS driven by S2S prediction system is potentially strong and economically viable. Key words: S2S models, Malaria Early Warning System (MEWS), Entomological Inoculation Rate (EIR), VECTRI-model, Nigeria. Full Text PDF (2.81 MB) |
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