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Optimized Forecasting Method for Weekly Influenza Confirmed Cases
Faculty
Science
Year:
2020
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohamed El Sayed Ahmed Muhamed
Staff Zu Site
Abstract In Staff Site
Journal:
International Journal of Environmental Research and Public Health MDPI
Volume:
Keywords :
Optimized Forecasting Method , Weekly Influenza Confirmed
Abstract:
Influenza epidemic is a serious threat to the entire world, which causes thousands of death every year and can be considered as a public health emergency that needs to be more addressed and investigated. Forecasting influenza incidences or confirmed cases is very important to do the necessary policies and plans for governments and health organizations. In this paper, we present an enhanced adaptive neuro-fuzzy inference system (ANFIS) to forecast the weekly confirmed influenza cases in China and the USA using official datasets. To overcome the limitations of the original ANFIS, we use two metaheuristics, called flower pollination algorithm (FPA) and sine cosine algorithm (SCA), to enhance the prediction of the ANFIS. The proposed FPASCA-ANFIS is evaluated using two datasets collected from the CDC and WHO websites. Furthermore, it was compared to some previous state-of-the-art approaches. Experimental results confirmed that the FPASCA-ANFIS outperformed the compared methods using variant measures, including RMSRE, MAPE, MAE, and
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