COVID 19 Outbreak Prediction and Forecasting in Bangladesh using Machine Learning Algorithm

Abstract

In this time, Novel Corona Virus is an important issue in the world, it also named COVID 19. This virus has been come from Wuhan, China in last December 2019. This virus has created critical circumstances in the whole world especially Bangladesh. The outbreak of COVID 19 is increasing gradually in Bangladesh. To predict and forecasting COVID 19 in Bangladesh we have used machine learning ML Linear Regression model. LR model is useful to predict the outbreak of COVID 19 in Bangladesh. It can be helped efficiently to predict some common numerical data like observation day, tested case, affected case, death case, recover cases, and forecast the number of upcoming cases for the next 30 days in Bangladesh. Our paper to study to analyze the epidemic growth of the COVID 19 in Bangladesh. We have applied the mathematical regression model to analyze the prediction and forecast for the effective threat of the COVID 19 in Bangladesh. The main objective of this paper how to predict the virus affected cases, recover cases, death cases, tested cases, and forecasting the future situation of Bangladesh. S M Abdullah Al Shuaeb | Md. Kamruzaman | Mohammad Al-Amin "COVID-19 Outbreak Prediction and Forecasting in Bangladesh using Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38068.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/38068/covid19-outbreak-prediction-and-forecasting-in-bangladesh-using-machine-learning-algorithm/s-m-abdullah-al-shuaeb

Authors and Affiliations

S M Abdullah Al Shuaeb | Md. Kamruzaman | Mohammad Al-Amin

Keywords

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  • EP ID EP692295
  • DOI -
  • Views 101
  • Downloads 0

How To Cite

S M Abdullah Al Shuaeb, Md. Kamruzaman (2020). COVID 19 Outbreak Prediction and Forecasting in Bangladesh using Machine Learning Algorithm. International Journal of Trend in Scientific Research and Development, 5(1), -. https://europub.co.uk/articles/-A-692295