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The availability of effective direct-acting antiviral therapy for hepatitis C virus (HCV) has led to a need for inclusive screening pathways. Since 2016, the World Health Organization (WHO) has advocated for the elimination of hepatitis C virus (HCV) as a public health threat by 2030. Some research also predicted the elimination of virus c by this year. In 2017 there was a screening project in nine Upper Egypt Provinces including Giza, Fayoum, Beni-Suef, Minya, Assiut, Sohag, Qena, Luxor and Aswan with a total number of two million citizens screened. As this was a limited screening, we had to forecast the prevalence in the other provinces. Now there is a new screening program which gave us new accurate data. Based on the new data, this paper proposed sufficient attributes and statistics about the rate of HCV infection and capability of healthcare servers. These attributes include gender, Socioeconomic and education characteristic in different age groups in each province. The new screening strategic plan will cover all of Egyptian citizens aging from 15 to 79; and it's done in three phases. The first and second phases of the screening are completed but the third phase is not finished yet. So, we predicted the remaining data in the third phase by using artificial neural network, as it is an accurate prediction machine-learning tool. The artificial neural network helped to train the data of some phases and test the prediction data of the other phases with higher performance. As the data collected from two phases were not sufficient to the neural network to predict the number of HCV patients in the third phase, we had to use the Interpolating Methods to increase the data. Using the artificial neural network and queuing mathematical model, will predict which year virus C will be eliminated. According to treatment protocol of HCV will be expected the total cost of HCV patient waited in the queue. |
