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In Procedia computer science

A global pandemic on March 11th of 2020, which was initially renowned by the World Health Organization (WHO) revealed the coronavirus (the COVID-19 epidemic). Coronavirus was flown in -December 2019 in Wuhan, Hubei region in China. Currently, the situation is enlarged by the infection in more than 200 countries all over the world. In this situation it was rising into huge forms in Bangladesh too. Modulated with a public dataset delivered by the IEDCR health authority, we have produced a sustainable prognostic method of COVID-19 outbreak in Bangladesh using a deep learning model. Throughout the research, we forecasted up to 30 days in which per day actual prediction was confirmed, death and recoveries number of people. Furthermore, we illustrated that long short-term memory (LSTM) demands the actual output trends among time series data analysis with a controversial study that exceeds random forest (RF) regression and support vector regression (SVR), which both are machine learning (ML) models. The current COVID-19 outbreak in Bangladesh has been considered in this paper. Here, a well-known recurrent neural network (RNN) model in order to referred by the LSTM network that has forecasted COVID-19 cases on per day infected scenario of Bangladesh from May 15th of 2020 till June 15th of 2020. Added with a comparative study that drives into the LSTM, SVR, RF regression which is processed by the RMSE transmission rate. In all respects, in Bangladesh the gravity of COVID-19 has become excessive nowadays so that depending on this situation public health sectors and common people need to be aware of this situation and also be able to get knowledge of how long self-lockdown will be maintained. So far, to the best of our knowledge LSTM based time series analysis forecasting infectious diseases is a well-done formula.

Mohammad Masum Abu Kaisar, Khushbu Sharun Akter, Keya Mumenunnessa, Abujar Sheikh, Hossain Syed Akhter

2020

COVID-19, COVID-19 transmission, Deep Learning, LSTM, ML, RFR, SVR, Time Series Forecasting