Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model
In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers fr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2022-02-01
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| Series: | Open Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/comp-2020-0221 |
| _version_ | 1811187433863118848 |
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| author | Gupta Koyel Datta Dwivedi Rinky Sharma Deepak Kumar |
| author_facet | Gupta Koyel Datta Dwivedi Rinky Sharma Deepak Kumar |
| author_sort | Gupta Koyel Datta |
| collection | DOAJ |
| description | In the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak. |
| first_indexed | 2024-04-11T14:02:17Z |
| format | Article |
| id | doaj.art-27c957a727b749e297e5a7a301bf42da |
| institution | Directory Open Access Journal |
| issn | 2299-1093 |
| language | English |
| last_indexed | 2024-04-11T14:02:17Z |
| publishDate | 2022-02-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Computer Science |
| spelling | doaj.art-27c957a727b749e297e5a7a301bf42da2022-12-22T04:20:01ZengDe GruyterOpen Computer Science2299-10932022-02-01121273610.1515/comp-2020-0221Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR modelGupta Koyel Datta0Dwivedi Rinky1Sharma Deepak Kumar2Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, IndiaDepartment of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, IndiaDepartment of Information Technology, Netaji Subhas University of Technology, New Delhi, IndiaIn the year 2019, during the month of December, the first case of SARS-CoV-2 was reported in China. As per reports, the virus started spreading from a wet market in the Wuhan City. The person infected with the virus is diagnosed with cough and fever, and in some rare occasions, the person suffers from breathing inabilities. The highly contagious nature of this corona virus disease (COVID-19) caused the rapid outbreak of the disease around the world. India contracted the disease from China and reported its first case on January 30, 2020, in Kerala. Despite several counter measures taken by Government, India like other countries could not restrict the outbreak of the epidemic. However, it is believed that the strict policies adopted by the Indian Government have slowed the rate of the epidemic to a certain extent. This article proposes an adaptive SEIR disease model and a sequence-to-sequence (Seq2Seq) learning model to predict the future trend of COVID-19 outbreak in India and analyze the performance of these models. Optimization of hyper parameters using RMSProp is done to obtain an efficient model with lower convergence time. This article focuses on evaluating the performance of deep learning networks and epidemiological models in predicting a pandemic outbreak.https://doi.org/10.1515/comp-2020-0221covid-19sequence to sequenceseirtransmission rate |
| spellingShingle | Gupta Koyel Datta Dwivedi Rinky Sharma Deepak Kumar Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model Open Computer Science covid-19 sequence to sequence seir transmission rate |
| title | Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model |
| title_full | Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model |
| title_fullStr | Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model |
| title_full_unstemmed | Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model |
| title_short | Predicting and monitoring COVID-19 epidemic trends in India using sequence-to-sequence model and an adaptive SEIR model |
| title_sort | predicting and monitoring covid 19 epidemic trends in india using sequence to sequence model and an adaptive seir model |
| topic | covid-19 sequence to sequence seir transmission rate |
| url | https://doi.org/10.1515/comp-2020-0221 |
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