Forecasting COVID-19 New Cases Using Transformer Deep Learning Model
Patil, S.; Mollaei, P.; farimani, A. B.
Show abstract
Making accurate forecasting of COVID-19 cases is essential for healthcare systems, with more than 650 million cases as of 4 January,1 making it one of the worst in history. The goal of this research is to improve the precision of COVID-19 case predictions in Russia, India, and Brazil, a transformer-based model was developed. Several researchers have implemented a combination of CNNs and LSTMs, Long Short-Term Memory (LSTMs), and Convolutional Neural Networks (CNNs) to calculate the total number of COVID-19 cases. In this study, an effort was made to improve the correctness of the models by incorporating recent advancements in attention-based models for time-series forecasting. The resulting model was found to perform better than other existing models and showed improved accuracy in forecasting. Using the data from different countries and adapting it to the model will enhance its ability to support the worldwide effort to combat the pandemic by giving more precise projections of cases.
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks 96%
- Forecasting virus outbreaks with social media data via neural ordinary differential equations 96%
- Application and Significance of SIRVB Model in Analyzing COVID-19 Dynamics 94%
Similar papers in this journal
- Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach 93%
- BertNDA: a Model Based on Graph-Bert and Multi-scale Information Fusion for ncRNA-disease Association Prediction 93%
- Evaluating Explanations from AI Algorithms for Clinical Decision-Making: A Social Science-based Approach 93%
Similar papers in this journal
- Users’ Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts 92%
- Dynamics and Development of the COVID-19 Epidemics in the US: a Compartmental Model with Deep Learning Enhancement 92%
- Fear of Infection and Sufficient Vaccine Reservation Information Might Drive Rapid Coronavirus Disease 2019 Vaccination in Japan: Evidence from Twitter Analysis 92%
Similar papers in this journal
- A Machine Learning Approach to Differentiate Between COVID-19 and Influenza Infection Using Synthetic Infection and Immune Response Data 95%
- Deep reinforcement learning framework for controlling infectious disease outbreaks in the context of multi-jurisdictions 92%
- Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.