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Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis
The coronavirus disease 2019 (COVID-19) is an ongoing pandemic with high morbidity and mortality rates. Current epidemiological studies urge the need of implementing sophisticated methods to appraise the evolution of COVID-19. In this study, we analysed the data for 228 days (1 May to 15 December 20...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536905/ https://www.ncbi.nlm.nih.gov/pubmed/34687416 http://dx.doi.org/10.1007/s11356-021-17046-9 |
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author | Wani, Manzoor A. Farooq, Junaid Wani, Danish Mushtaq |
author_facet | Wani, Manzoor A. Farooq, Junaid Wani, Danish Mushtaq |
author_sort | Wani, Manzoor A. |
collection | PubMed |
description | The coronavirus disease 2019 (COVID-19) is an ongoing pandemic with high morbidity and mortality rates. Current epidemiological studies urge the need of implementing sophisticated methods to appraise the evolution of COVID-19. In this study, we analysed the data for 228 days (1 May to 15 December 2020) of daily incidence of COVID-19 cases for a district level analysis in the region of Jammu and Kashmir in the northern Himalayan belt of India. We used a deep learning-based incremental learning technique to model the current trend of COVID-19 transmission and to predict the future trends with 60-day forecasting. The results not only indicate high rates of morbidity and mortality but also forecast high rise in the incidence of COVID-19 in different districts of the study region. We used geographic information system (GIS) for storing, analysing, and presenting the spread of COVID-19 which provides key insights in understanding, planning, and implementing mitigating measures to tackle the current spread of the pandemic and its possible future scenarios. The existing disparity in health care facilities at district level is shown in relation to the spread of disease. The study results also highlight the need to upgrade health care infrastructure in the study region to control the current and future pandemics. These results could be useful for administration and scientific community to develop efficient short-term and long-term strategies against such diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-021-17046-9. |
format | Online Article Text |
id | pubmed-8536905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85369052021-10-25 Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis Wani, Manzoor A. Farooq, Junaid Wani, Danish Mushtaq Environ Sci Pollut Res Int Research Article The coronavirus disease 2019 (COVID-19) is an ongoing pandemic with high morbidity and mortality rates. Current epidemiological studies urge the need of implementing sophisticated methods to appraise the evolution of COVID-19. In this study, we analysed the data for 228 days (1 May to 15 December 2020) of daily incidence of COVID-19 cases for a district level analysis in the region of Jammu and Kashmir in the northern Himalayan belt of India. We used a deep learning-based incremental learning technique to model the current trend of COVID-19 transmission and to predict the future trends with 60-day forecasting. The results not only indicate high rates of morbidity and mortality but also forecast high rise in the incidence of COVID-19 in different districts of the study region. We used geographic information system (GIS) for storing, analysing, and presenting the spread of COVID-19 which provides key insights in understanding, planning, and implementing mitigating measures to tackle the current spread of the pandemic and its possible future scenarios. The existing disparity in health care facilities at district level is shown in relation to the spread of disease. The study results also highlight the need to upgrade health care infrastructure in the study region to control the current and future pandemics. These results could be useful for administration and scientific community to develop efficient short-term and long-term strategies against such diseases. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-021-17046-9. Springer Berlin Heidelberg 2021-10-23 2022 /pmc/articles/PMC8536905/ /pubmed/34687416 http://dx.doi.org/10.1007/s11356-021-17046-9 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article Wani, Manzoor A. Farooq, Junaid Wani, Danish Mushtaq Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
title | Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
title_full | Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
title_fullStr | Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
title_full_unstemmed | Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
title_short | Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
title_sort | risk assessment of covid-19 pandemic using deep learning model for j&k in india: a district level analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536905/ https://www.ncbi.nlm.nih.gov/pubmed/34687416 http://dx.doi.org/10.1007/s11356-021-17046-9 |
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