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Geospatial analysis in managing medical facilities for combating disaster triggered by the COVID-19 pandemic

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dc.contributor.authorUsman F.en_US
dc.contributor.authorHamim S.A.en_US
dc.contributor.authorGumano H.N.en_US
dc.contributor.authorJamil F.en_US
dc.contributor.authorid55812540000en_US
dc.contributor.authorid57200660053en_US
dc.contributor.authorid57223223109en_US
dc.contributor.authorid57223227883en_US
dc.date.accessioned2023-05-29T09:08:12Z
dc.date.available2023-05-29T09:08:12Z
dc.date.issued2021
dc.descriptionViruses; Geo-spatial analysis; Geo-spatial data; Infectious disease; Local parameters; Medical facility; Monitoring purpose; World Health Organization; Wuhan , China; Disastersen_US
dc.description.abstractA new virus emerged, which initially called Novel Corona Virus 2019 and later officially named Coronavirus disease 2019, COVID-19. The COVID-19 spread globally in less than a year since its outbreak in Wuhan, China, as the epicenter. The pandemic was beginning at the end of December 2019, and the World Health Organization just announced as a pandemic in early March 2020. With extremely fast commuting people, the spread of the contagious virus tremendously fast. The world was not ready to face this unprecedented situation. This paper presents an effort to fight the Covid-19 pandemic in Palembang City, the capital of South Sumatra Province, Indonesia. This study utilized information provided by the authority and convert it into geospatial data. Daily based data has been captured by providing tools for the enforcements to collect the data for monitoring purposes as well. The Susceptible, Infected, and Recovered (SIR) model is used in this study to determine the need for medical facility demand and map the dispersion of parameters in elevating infectious diseases. The SIR model is used to determine the effect of social distancing in the community to flatten the curve. The local parameters were used in the lowest administrative boundary of the district. Predictive demand for referred medical facilities can be delineated. From this study, the predicted peak of infected cases has a good agreement with the actual total cases. The result of the analysis can be used to manage the medical facilities to accommodate the demand. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12061
dc.identifier.doi10.1088/1755-1315/708/1/012061
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85105274028
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105274028&doi=10.1088%2f1755-1315%2f708%2f1%2f012061&partnerID=40&md5=a7560b7e693509e5a624941bd0573bad
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26246
dc.identifier.volume708
dc.publisherIOP Publishing Ltden_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Earth and Environmental Science
dc.titleGeospatial analysis in managing medical facilities for combating disaster triggered by the COVID-19 pandemicen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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