Corona virus disease (COVID-19) is a contagious infection caused by a novel virus. It causes severe flu-like respiratory illness with cough, fever, and in some extreme cases may cause acute respiratory distress. The corona virus disease 2019 (COVID-19) which started in the city of Wuhan in Hubei province of China, soon spread to other countries of the world. The rapid spread of this Corona virus disease throughout the world lead it to be declared a pandemic by WHO (World Health Organization) on March 11, 2020. This study focused on the outbreak of the disease in the Europe continent and tries to predict its trends. As on 7th May,2020, Spain has been identified to have the maximum number of confirmed COVID-19 cases in Europe and second in the world following United States of America. The COVID-19 dataset for Europe (European Centre for Disease Prevention and Control) is compiled and a generalized regression neural network model optimized with flower pollination algorithm (FPA-GRNN) is constructed to predict the epidemic pattern.
More than 185 countries get affected in the present SARS-COV-2 (aka COVID-19 or n-Corona virus) epidemic. Experts from different domains are contributing a lot to combat this pandemic effectively. Data analysts have made lots of efforts, IT experts also to forecast the severity of infected cases, the death rate, recovery rate, and other health indicators using various statistical and machine learning models. These forecasting models may motivate and help the policymakers to make the decisions based on these predicted results. Although any prediction on such a pandemic problem is non-monotonic and uncertain, these predictions may help us plan to take the needful actions while dealing with this tragic situation. Lots of symptomatic and asymptotic human behavior are observed while investigating the cases. Also, many countries are making dynamic changes in their policies to combat this pandemic. Therefore, such heterogeneous and dynamic changes in policies and human behavior make the problem of proposing an accurate predicting model more complex. We used machine learning methods to monitor the COVID-19 impact country-wise and developed a FOCOMO model to forecast the severity worldwide till the mid of Oct 2020. We referred to the COVID-19 dataset publicly shared by John Hopkins University. We proposed a purely predictive model to monitor the pandemic (without claiming it as perfect or accurate) into three Phases: i) categorization of countries based on the severity of COVID-19 cases, ii) prediction of daily case reporting, and iii) normalization of flattening the forecasting curve based on deaths and recovery rate. These forecasting models will help monitor and plan the human behaviors and severity of the pandemic country-wise and will provide an opportunity to take some corrective actions for shaping the future.
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