COVID-19 Time Series Forecasting of Daily Cases, Deaths Caused and Recovered Cases using Long Short Term Memory Networks

Bodapati, Suraj; Bandarupally, Harika; Trupthi, MC

doi:10.1109/iccca49541.2020.9250863

Cited by 30 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gupta et al [65] forecasted COVID-19 cases of India using support vector machines, prophet, and linear regression models. Similarly, Bodapati et al [66] forecasted the COVID-19 daily cases, deaths caused and recovered cases with the help of LSTM networks for the whole world. Chaurasia and Pal [67] used several forecasting models such as simple average, single exponential smoothing, Holt winter method, and ARIMA models for COVID-19 pandemic.…”

Section: Modelling and Forecasting Covid-19mentioning

confidence: 99%

Deep learning via LSTM models for COVID-19 infection forecasting in India

2022

View full text Add to dashboard Cite

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low; however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

show abstract

Section: Modelling and Forecasting Covid-19mentioning

confidence: 99%

Deep learning via LSTM models for COVID-19 infection forecasting in India

2022

View full text Add to dashboard Cite

show abstract

“…Covid-19 used in this study is daily time-series data from March 11, 2020 to January 4, 2021. Time series data is a series of data sets ordered by time sequence [11]. Table 1 shows the Covid-19 data used in this study.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The error value close to zero indicates that the predicted number is getting closer to the actual result. Besides, this method is also the best model for dynamic data, which suddenly changes drastically in various cases [11]. Data on the addition of corona cases in Indonesia fluctuate in the number of thousands every day.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

Adhinata¹,

Rakhmadani²

2021

teknika

View full text Add to dashboard Cite

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

show abstract

“…had proposed the LSTM model for the prediction of total infected cases for different cities in China. The proposed model had done the forecasting for 12 days ahead with an accuracy of 77.895% [14] [26].…”

Section: Introductionmentioning

confidence: 99%

Explainable AI Framework for COVID-19 Prediction in Different Provinces of India

Pandianchery¹,

E.A²,

Sowmya³

et al. 2022

Preprint

View full text Add to dashboard Cite

In 2020, covid-19 virus had reached more than 200 countries. Till December 20th 2021, 221 nations in the world had collectively reported 275M confirmed cases of covid-19 & total death toll of 5.37M. Many countries which include United States, India, Brazil, United Kingdom, Russia etc were badly affected by covid-19 pandemic due to the large population. The total confirmed cases reported in this country are 51.7M, 34.7M, 22.2M, 11.3M, 10.2M respectively till December 20, 2021. This pandemic can be controlled with the help of precautionary steps by government & civilians of the country. The early prediction of covid-19 cases helps to track the transmission dynamics & alert the government to take the necessary precautions. Recurrent Deep learning algorithms is a data driven model which plays a key role to capture the patterns present in time series data. In many literatures, the Recurrent Neural Network (RNN) based model are proposed for the efficient prediction of COVID-19 cases for different provinces. The study in the literature doesn't involve the interpretation of the model behaviour & robustness.In this study, The LSTM model is proposed for the efficient prediction of active cases in each provinces of India. The active cases dataset for each province in India is taken from John Hopkin's publicly available dataset for the duration from 10th June, 2020 to 4th August, 2021. The proposed LSTM model is trained on one state i.e., Maharashtra and tested for rest of the provinces in India. The concept of "Explainable AI" is involved in this study for the better interpretation & understanding of the model behaviour. The proposed model is used to forecast the active cases in India from 16th December, 2021 to 5th March, 2022. It is notated that there will be a emergence of third wave on January, 2022 in India.

show abstract

COVID-19 Time Series Forecasting of Daily Cases, Deaths Caused and Recovered Cases using Long Short Term Memory Networks

Cited by 30 publications

References 12 publications

Deep learning via LSTM models for COVID-19 infection forecasting in India

Deep learning via LSTM models for COVID-19 infection forecasting in India

Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

Explainable AI Framework for COVID-19 Prediction in Different Provinces of India

Contact Info

Product

Resources

About