Abstract
The recent outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2), also called COVID-19, is a major global health problem due to an increase in mortality and morbidity. The virus disturbs the respirational process of a human being and is highly spreadable. The current distressing COVID-19 pandemic has caused heavy financial crashing and the assets and standards of the highly impacted countries being compromised. Therefore, prediction methods should be devised, supporting the development of recovery strategies. To make accurate predictions, understanding the natural progression of the disease is very important.
The developed novel mathematical models may help the policymakers and government control the infection and protect society from this pandemic infection. Due to the nature of the data, the uncertainty may lead to an error in the estimation. In this scenario, the uncertainty arises due to the dynamic rate of change based on time in the infectious count because of the different stages of lockdowns, population density, social distancing, and many other reasons concerning demography. The period between exposure to the virus and the first symptom of infection is large compared to other viruses. It is mandatory to follow the infected persons.
The exposure needs to be controlled to prevent the spreading in the long term, and the infected people must be in isolation for the above-mentioned period to avoid short-term infections. Officials need to know about the long-term scenario as well as the shortterm for policymaking. Many studies are focusing on long-term forecasting using mathematical modelling. For the short-term prediction, this paper proposed two algorithms: 1) to predict next-day count from the past 2 days data irrespective of population size with less error rate and 2) to predict the next M days based on the deviation of the rate of change in previous N-days active cases.
The proposed methods can be adopted by government officials, researchers, and medical professionals by developing a mobile application. So that they can use it whenever and wherever necessary. The mobile health (M-Health) App. helps the user to know the status of the pandemic state and act accordingly.