Overview of different models for predicting COVID-19 Cases

Authors

  • Editor IJSMI

Abstract

During the month of Dec 2019 Chinese people from Wuhan the capital of Hubei Province were affected by the Virus called ‘Covid-19’ (initially it was called as Novel Coronavirus (2019-nCoV)) which was originated from the Sea food market as per the Chinese Government reports. During the first week of January 2020 Chinese Authorities identified this new virus [1]. It slowly started spreading to other parts of China. During the month January it started spreading to Thailand, South Korea and Japan through travellers from the Wuhan City [1].  Number of papers have already published in predicting the COVID-19 cases using mathematical and artificial intelligence modelling [2, 3, 4, 5]. This paper provides evaluation of different predictive models such as statistical models, machine learning and deep learning models for predicting the COVID cases which was declared as ‘Pandemic’ by World Health Organization (WHO). This paper used situation reports of WHO [1] as the basis for the predicting the number of cases which was reported daily and included country wise statistics. The focus of the paper is on academic part of the problem. Open Source statistical software R along with GUI tool R Studio [6, 7] is used to build the forecasting model.

Published

2020-03-31

How to Cite

IJSMI, E. (2020). Overview of different models for predicting COVID-19 Cases. International Journal of Statistics and Medical Informatics, 13(1). Retrieved from http://ijsmi.com/Journal/index.php/IJSMI/article/view/20