Prediction of seasonal influenza epidemics in Tehran using artificial neural networks
Fatemeh Saberian, Ali Zamani, Mohammad Mahdi Gooya, Payman Hemmati, Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab · 2014
Prediction of seasonal influenza epidemics is certainly a forming and effective step towards taking appropriate preventive actions. Improvement on public informing, decreasing the number of infected cases, undesirable effects and deaths due to influenza and also increasing vigilance of Iranian Influenza Surveillance System (IISS), have been practical goals of this research. A forecasting system has been designed and developed using Artificial Neural Networks (ANNs). It is a novel research as a novel dataset has been exploited. The data are categorized in two groups of climatic parameters (temperature, humidity, precipitation, wind speed & sea level pressure) and number of patients (number of total referrals and number of patients with Influenza-Like Illnesses (ILI)). In order to evaluate the model performance, different cost functions are defined and results indicate that the model provides the possibility of a satisfactory forecasting and is practically helpful to achieve the objectives already claimed.