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A Real-time Model to Forecast the Outbreak of Covid-19 using LSTM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 21 |
| Year of Publication: 2022 |
| Authors: Omkar Bhoite, Sohail Ahmad, Saurabh Wagh, Ketan Gaikwad, Shalaka Deore, Shubhangi Ingle |
10.5120/ijca2022922244
|
Omkar Bhoite, Sohail Ahmad, Saurabh Wagh, Ketan Gaikwad, Shalaka Deore, Shubhangi Ingle . A Real-time Model to Forecast the Outbreak of Covid-19 using LSTM. International Journal of Computer Applications. 184, 21 ( Jul 2022), 60-64. DOI=10.5120/ijca2022922244
Abstract
Deep Learning based forecasting models have been in use for a long time and they have proven their significance in problems including time series forecasting and improve the accuracy and efficiency of the results for given problem. These models have long been utilization domains that required that identification and fiction of main factors of information. Based on review of work done in the field of forecasting, this study demonstrates the potential of LSTM algorithm to forecast the rise and fall in number of active cases and deaths of Covid 19 patients using real time input provided by John Hopkins University which is available on GitHub and updated on a daily basis. In short, a real time Covid 19 outbreak forecasting model implemented using long short term memory networks algorithm. The use of LSTM is suggested to improve the efficiency and accuracy of the presently available models and make predictions of 2 parameters including the number of active cases and the number of deaths for the upcoming 10 days. The goal was to analyze the algorithm by comparing the results of prediction and actual reports for a period of 60 days and forecast the number of newly confirmed and death cases of the disease for upcoming 10 days. To develop an algorithm faster than existing systems and use the most recently available data for a higher range of input and calculate the latest trend.
References
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