Background:
The coronavirus disease pandemic was initiated in Wuhan province of mainland China in December 2019 and has spread over the world.
Objective:
This study analyses the effects of COVID 19 based on Likely Positive Cases and fatality in India during and after the lockdown period from 24 March 2020 to 24 May 2020.
Methods:
Python has been used as the main programming language for data analysis and forecasting using the Prophet Model, a time series analysis model. The dataset has been preprocessed by grouping together the days for total numbers of cases and deaths on few selected dates and removed missing values present in some states.
Results:
The Prophet model performs better in terms of precision on the real data. Prediction depicts that during the lockdown, the total cases were rising but in a controlled manner with an accuracy of 87%. After the relaxation of lockdown rules, the predictions have shown an obstreperous situation with an accuracy of 60%.
Conclusion:
The resilience could have been better if the lockdown with strict norms was continued without much relaxation. The situation after lockdown has been found to be uncertain as observed by the experimental study conducted in this work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.