Analysis of second wave of COVID-19 cases in Nepal with a logistic model - Joshi, Radha Krishna; Bhatt, Sarita; Lamichhane, Tika Ram; Ghimire, Madhav Prasad (Asian Journal of Medical Sciences, 2021)
Background: COVID-19, caused by SARS-CoV-2, is a newly identified highly infectious disease. It has affected almost every country including Nepal causing a pandemic situation. Most of the properties of SARS-CoV-2 are not known and still under intense investigation. Due to high mutation rate, it reappears in many countries in the form of new variant. In Nepal, second wave impact of COVID-19 is mainly caused by newly found delta variant of SARS-CoV-2. In this case, the mathematical modelling is noted to play important role to understand control strategies for the spread of coronavirus. Aims and Objective: To analyze the second wave impact by modelling the data of COVID-19 cases in Nepal. Materials and Methods: We have analyzed COVID-19 daily cases and deaths reported by Ministry of Health and Population, Government of Nepal from April 1 to May 31, 2021. A logistic model has been used to present the trend line of COVID-19 infection in Nepal, based on the law of population growth developed by Verhulst. Results: The results show a good fit between observed and predicted data by logistic model as indicated by coefficient of determination having value near to unity. The point of inflection from the logistic model predicted a maximum of 9951 daily new cases. The maximum number of cumulative cases estimated at the end of second wave was found to be 307293 with 95% confidence interval. Conclusion: Logistic model properly describes the growth of COVID-19 cases with time. This type of data modelling and analysis will be very useful in predicting the upcoming trend of COVID-19 in Nepal as a basis for making health policy management by the government.