Investigation on COVID-19 by using Machine Learning Techniques
K Suresh, Adwitiya Mukhopadhyay
Abstract
The COVID-19 pandemic had brought about a standstill to many activities across the world. Health experts, doctors and academic investigators across the globe have been attempting to come to terms with the trying demands posed on the human population due to the pandemic. This paper attempts to develop a precise model for examination of and forecasting effective measures to be implemented during different situations to limit the impact of COVID-19. It also addresses various trials and tests faced while using machine learning algorithms. For the experimental analysis different parameters such as countries (China, America, India, South Africa and Italy), month, types of measures to be undertaken (awareness campaigns, economic measures, domestic travel restrictions, health screening at airports and psychological assistance involving medical social work) and date of implementation details are considered. COVID-19 epidemic determent procedures by recognizing, evaluating danger situation and probable paths of epidemic using a machine-learning technique have been explored. A proposed methodology to forecast extension of lockdown in order to exterminate COVID-19 is presented wherein SVM regression technique is used for prediction of actual extension of lockdown during the pandemic situation.