Visualizing and Forecasting Trends of Covid-19 for Large Scale Epidemic Prevention

Samatha Juluri*, Madhavi Gudavalli **
* Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, Telangana, India.
** Department of Computer Science and Engineering, JNTUK University College of Engineering, Narasaraopet, Andhra Pradesh, India.
Periodicity:December - February'2020
DOI : https://doi.org/10.26634/jit.9.1.17342

Abstract

The pandemic of Corona Virus Disease (COVID-19) has shaken the globe with its wide spread, resulting in human deaths due to limited understanding of medical community and scarcity of medical resources. The rapid rise in the number of COVID-19 incidents has promoted the need for public awareness and effective preventive measures to control the disastrous effects of this epidemic. In the current context, it is necessary to predict the trend of COVID-19 to support the public health sector to effectively prevent and control this epidemic in order to save mankind. Traditional pandemic models consume lot of time to predict the number of infections as these models consider all individuals with corona virus have the same infection rate. These prediction results can only provide general trends which are not useful for epidemic control and prevention at the right time. Therefore, we proposed a machine learning approach for scenario analysis and forecasting time series data of COVID-19 to visualize its impact globally and accelerate the containment of the virus. The results analyzed through this forecasting model will help the people better understand the potential implications of corona virus and predict the possible future cases of COVID-19.

Keywords

COVID-19, Data Wrangling, Epidemic, Machine Learning, Forecasting, Prediction.

How to Cite this Article?

Juluri, S., and Gudavalli, M. (2020). Visualizing and Forecasting Trends of Covid-19 for Large Scale Epidemic Prevention. i-manager's Journal on Information Technology, 9(1), 14-21. https://doi.org/10.26634/jit.9.1.17342

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