Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach
The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Interdisciplinary Perspectives on Infectious Diseases |
| Online Access: | http://dx.doi.org/10.1155/2022/8570089 |
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| author | Iqramul Haq Md. Ismail Hossain Ahmed Abdus Saleh Saleheen Md. Iqbal Hossain Nayan Mafruha Sultana Mila |
| author_facet | Iqramul Haq Md. Ismail Hossain Ahmed Abdus Saleh Saleheen Md. Iqbal Hossain Nayan Mafruha Sultana Mila |
| author_sort | Iqramul Haq |
| collection | DOAJ |
| description | The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named “susceptible-infectious-recovered (SIR)” and an additive regression model named “Facebook PROPHET Procedure” were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase. |
| format | Article |
| id | doaj-art-045284abdb2c460a860125c154ca74a7 |
| institution | Kabale University |
| issn | 1687-7098 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Interdisciplinary Perspectives on Infectious Diseases |
| spelling | doaj-art-045284abdb2c460a860125c154ca74a72025-02-03T01:22:57ZengWileyInterdisciplinary Perspectives on Infectious Diseases1687-70982022-01-01202210.1155/2022/8570089Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning ApproachIqramul Haq0Md. Ismail Hossain1Ahmed Abdus Saleh Saleheen2Md. Iqbal Hossain Nayan3Mafruha Sultana Mila4Department of Agricultural StatisticsDepartment of StatisticsDepartment of StatisticsQuality Services and ComplianceDepartment of StatisticsThe outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named “susceptible-infectious-recovered (SIR)” and an additive regression model named “Facebook PROPHET Procedure” were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase.http://dx.doi.org/10.1155/2022/8570089 |
| spellingShingle | Iqramul Haq Md. Ismail Hossain Ahmed Abdus Saleh Saleheen Md. Iqbal Hossain Nayan Mafruha Sultana Mila Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach Interdisciplinary Perspectives on Infectious Diseases |
| title | Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach |
| title_full | Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach |
| title_fullStr | Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach |
| title_full_unstemmed | Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach |
| title_short | Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach |
| title_sort | prediction of covid 19 pandemic in bangladesh dual application of susceptible infective recovered sir and machine learning approach |
| url | http://dx.doi.org/10.1155/2022/8570089 |
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