Analysis and Prediction of COVID-19 Using Growth Analysis Models: A Case Study - Lone, Kalimullah; Sofi, Shabir Ahmad (Cloud Computing and Data Science, 2024)
The coronavirus disease 2019 outbreak has added to the development of novel methods to study the epidemiological and predictive nature of the pandemic. Mining such data is necessary as this data is full of trends and information. Using data mining techniques allows us to extract and process such data to predict the pandemic's trends and behavior. Analysis, evaluation, and prediction are performed on Jammu and Kashmir's data during the period from the 9th of March 2020 to the 10th of February 2021. The work is done on the dataset of patients provided by the Department of Information and Public Relations, Government of Jammu and Kashmir. Various mathematical models and techniques were used to predict the virus's spread and occurrence with the help of symptoms. We aim to propose a model to predict the virus occurrence based on the symptoms and epidemiological nature of the pandemic. The purpose of this study is to understand the virus occurrence and distribution. The work has helped our government to find the most infected areas and future challenges to tackle any such pandemic. The trends and behavior of the virus in Jammu and Kashmir were studied. People under observation, people tested for the virus, positive, negative, recovered, active, and deaths were keenly observed. The prediction to find an infected patient was carried out with the help of symptoms. The results obtained from the prediction model are verified with the actual results.