Open AccessCOVID-19 Forecasting: A Statistical Approach
Author(s) -
Arti Saxena,
Falak Bhardwaj,
Vijay Kumar
Publication year - 2021
Publication title -
bangladesh journal of medical science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.255
H-Index - 10
eISSN - 2079-6854
pISSN - 2076-0299
DOI - 10.3329/bjms.v20i5.55401
Subject(s) - covid-19 , pandemic , medicine , disease , public health , medical science , autoregressive integrated moving average , statistics , infectious disease (medical specialty) , time series , virology , outbreak , pathology , medical education , mathematics
Background: SARS-coronavirus-2 is a new virus infecting people and causing COVID-19 disease. The disease is causing a worldwide pandemic. Although some people never develop any signs or symptoms of disease when they are infected, other people are at very high risk for severe disease and death.Objective: If we’re able to intervene to prevent even some transmission, we can dramatically reduce the number of cases. And this is the public health goal for controlling COVID-19.Methods: This article initializes an approach for comparatively accurate values prediction of new cases and deaths for a particular day in order to be considered for preventive measures. The three statistical analysis methods considered for forecasting are Fbprophet, Moving average and the Autoregressive Integrated Moving Average algorithm.Results: The results obtained are in-line with the past and present trend of COVID-19 data collected from WHO website.Conclusion: The output is satisfactory for further consideration.Bangladesh Journal of Medical Science Vol.20(5) 2021 p.85-96