Optimized Forecasting Method for Weekly Influenza Confirmed Cases

Mohammed A. A. Al-qaness, Ahmed A. Ewees, Hong Fan and Mohamed Abd Elaziz
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Mohammed A. A. Al-qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Ahmed A. Ewees: Department of e-Systems, University of Bisha, Bisha 61922, Saudi Arabia
Hong Fan: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

IJERPH, 2020, vol. 17, issue 10, 1-12

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 R 2 .

Keywords: public health; forecasting; ANFIS; sine cosine algorithm; flower pollination algorithm; weekly influenza confirmed cases (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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