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Background Coronavirus Disease is one of the most highly infectious diseases was reported worldwide as pandemic. This infectious virus transmits through several methods among individuals, for controlling this transmission most effective nonpharmacological strategy to controlling the infections is Quarantine the affected or suspected asymptomatic individuals. Aim To assess the psychological impact of quarantine period on asymptomatic individuals with COVID-19. Methodology Descriptive research design was used in the study. A total of 380 individuals approached for the study, participants on a structured research proforma and Depression Anxiety and stress scale. Result The average age of the quarantine people was 33.5 years and 72% of them are males, all are educated and 66% of them are well qualified. Stress was severe or extremely severe among 46% of participants followed by anxiety and depression is 14 and 8 respectively. Conclusion Control freedom of quarantine period and limited almost nil interaction with others worsens their psychological health and daily functioning. Psychological distress was multifolded due to lack of proper mental health facilities and the availability of factual information about the virus.

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, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, has presented tough times for countries all over the world with number of cases and casualties running in millions. While virologists and doctors have spent sleepless nights to come up with a potent vaccine, the work life of government personnel including administrative staffs, hospital employees etc. has not been any easier. Amidst this turmoil, the common question crossing every mind is concerned with the statistics about this infection including expected number of infections, peak prediction etc. We try to answer these questions by analyzing the time series data of Covid-19 infections for certain hard-hit countries and states in India. A series of machine and deep learning models have been built to capture the infection distribution so that these models could predict the fate of this infection in the near future. We also make an attempt to predict the time when active cases would cease to increase.