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...
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| Veröffentlicht in: | Environmental science and pollution research international 2022-03, Vol.29 (12), p.18271-18281 |
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| 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. |
| doi_str_mv | 10.1007/s11356-021-17046-9 |
| format | Article |
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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.</description><identifier>ISSN: 0944-1344</identifier><identifier>ISSN: 1614-7499</identifier><identifier>EISSN: 1614-7499</identifier><identifier>DOI: 10.1007/s11356-021-17046-9</identifier><identifier>PMID: 34687416</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 infection ; Deep Learning ; Disease transmission ; Earth and Environmental Science ; Ecotoxicology ; Environment ; Environmental Chemistry ; Environmental Health ; Environmental science ; Epidemiology ; evolution ; Geographic information systems ; Health care ; Health care facilities ; health services ; Humans ; India ; India - epidemiology ; infrastructure ; Mathematical models ; Morbidity ; Mortality ; pandemic ; Pandemics ; Remote sensing ; Research Article ; Risk Assessment ; SARS-CoV-2 ; Viral diseases ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Environmental science and pollution research international, 2022-03, Vol.29 (12), p.18271-18281</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>2021. 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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.</description><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>COVID-19 infection</subject><subject>Deep Learning</subject><subject>Disease transmission</subject><subject>Earth and Environmental Science</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Chemistry</subject><subject>Environmental Health</subject><subject>Environmental science</subject><subject>Epidemiology</subject><subject>evolution</subject><subject>Geographic information systems</subject><subject>Health care</subject><subject>Health care facilities</subject><subject>health services</subject><subject>Humans</subject><subject>India</subject><subject>India - epidemiology</subject><subject>infrastructure</subject><subject>Mathematical models</subject><subject>Morbidity</subject><subject>Mortality</subject><subject>pandemic</subject><subject>Pandemics</subject><subject>Remote sensing</subject><subject>Research Article</subject><subject>Risk Assessment</subject><subject>SARS-CoV-2</subject><subject>Viral diseases</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>0944-1344</issn><issn>1614-7499</issn><issn>1614-7499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkVtv1DAQhS0EokvhD_CALCEhXkJnEl9iHpDQcumWSpUQ8Gp5E2fxkjiLJ6nUf4-XbcvlAZ5G1nxzZo4PY48RXiCAPiHESqoCSixQg1CFucMWqFAUWhhzly3ACFFgJcQRe0C0BSjBlPo-O6qEqrVAtWDbj4G-cUfkiQYfJz52fHnxZfWmQMN3LrZ-CA2fKcQNb73f8d67FPevYWx9z7sx8bNnH3iIfBXb4F5yx9tAUwrNlNnLjLjo-isK9JDd61xP_tF1PWaf3739tDwtzi_er5avz4tGgp4KWepGVlqBWivZuMYIqDWqNUoQBlrtEcVays545WUr2q7OZkqEuuuybVVXx-zVQXc3rwffNtlVcr3dpTC4dGVHF-yfnRi-2s14aWtZKQMyCzy_Fkjj99nTZIdAje97F_04ky2VllJVEuH_qKyFNjWWOqNP_0K345zy3-wFqwzWgCpT5YFq0kiUfHd7N4Ldp24Pqducuv2ZujV56Mnvjm9HbmLOQHUAKLfixqdfu_8h-wPl57Wm</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Wani, Manzoor A.</creator><creator>Farooq, Junaid</creator><creator>Wani, Danish Mushtaq</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7SN</scope><scope>7T7</scope><scope>7TV</scope><scope>7U7</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-6727-7010</orcidid></search><sort><creationdate>20220301</creationdate><title>Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis</title><author>Wani, Manzoor A. ; Farooq, Junaid ; Wani, Danish Mushtaq</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c507t-527c537606b65cac9408716b150490d7e114b55f9e6e5d4df84682108ff749683</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aquatic Pollution</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>COVID-19 - 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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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>34687416</pmid><doi>10.1007/s11356-021-17046-9</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6727-7010</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Coronaviruses COVID-19 COVID-19 - epidemiology COVID-19 infection Deep Learning Disease transmission Earth and Environmental Science Ecotoxicology Environment Environmental Chemistry Environmental Health Environmental science Epidemiology evolution Geographic information systems Health care Health care facilities health services Humans India India - epidemiology infrastructure Mathematical models Morbidity Mortality pandemic Pandemics Remote sensing Research Article Risk Assessment SARS-CoV-2 Viral diseases Waste Water Technology Water Management Water Pollution Control |
| title | Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis |
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