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Article: Spatio-temporal and stochastic modelling of the severe acute respiratory syndrome (SARS)

TitleSpatio-temporal and stochastic modelling of the severe acute respiratory syndrome (SARS)
Authors
Issue Date2013
PublisherGnosisGIS. The Journal's web site is located at http://www.geospatialhealth.unina.it
Citation
Geospatial Health, 2013, v. 8 n. 1, p. 183-192 How to Cite?
AbstractThis study describes the development of a spatio-temporal disease model based on the episodes of severe acute respiratory syndrome (SARS) that took place in Hong Kong in 2003. In contrast to conventional, deterministic modelling approaches, the model described here is predominantly spatial. It incorporates stochastic processing of environmental and social variables that interact in space and time to affect the patterns of disease transmission in a community. The model was validated through a comparative assessment between actual and modelled distribution of diseased locations. Our study shows that the inclusion of location-specific characteristics satisfactorily replicates the spatial dynamics of an infectious disease. The Pearson's correlation coefficients for five trials based on 3-day aggregation of disease counts for 1-3, 4-6 and 7-9 day forecasts were 0.57-0.95, 0.54-0.86 and 0.57-0.82, respectively, while the correlation based on 5-day aggregation for the 1-5 day forecast was 0.55-0.94 and 0.58-0.81 for the 6-10 day forecast. The significant and strong relationship between actual results and forecast is encouraging for the potential development of an early warning system for detecting this type of disease outbreaks.
Persistent Identifierhttp://hdl.handle.net/10722/201034
ISSN
2015 Impact Factor: 1.093
2015 SCImago Journal Rankings: 0.650

 

DC FieldValueLanguage
dc.contributor.authorLai, PCen_US
dc.contributor.authorKwong, KHen_US
dc.contributor.authorWong, FHTen_US
dc.date.accessioned2014-08-21T07:10:34Z-
dc.date.available2014-08-21T07:10:34Z-
dc.date.issued2013en_US
dc.identifier.citationGeospatial Health, 2013, v. 8 n. 1, p. 183-192en_US
dc.identifier.issn1827-1987-
dc.identifier.urihttp://hdl.handle.net/10722/201034-
dc.description.abstractThis study describes the development of a spatio-temporal disease model based on the episodes of severe acute respiratory syndrome (SARS) that took place in Hong Kong in 2003. In contrast to conventional, deterministic modelling approaches, the model described here is predominantly spatial. It incorporates stochastic processing of environmental and social variables that interact in space and time to affect the patterns of disease transmission in a community. The model was validated through a comparative assessment between actual and modelled distribution of diseased locations. Our study shows that the inclusion of location-specific characteristics satisfactorily replicates the spatial dynamics of an infectious disease. The Pearson's correlation coefficients for five trials based on 3-day aggregation of disease counts for 1-3, 4-6 and 7-9 day forecasts were 0.57-0.95, 0.54-0.86 and 0.57-0.82, respectively, while the correlation based on 5-day aggregation for the 1-5 day forecast was 0.55-0.94 and 0.58-0.81 for the 6-10 day forecast. The significant and strong relationship between actual results and forecast is encouraging for the potential development of an early warning system for detecting this type of disease outbreaks.-
dc.languageengen_US
dc.publisherGnosisGIS. The Journal's web site is located at http://www.geospatialhealth.unina.iten_US
dc.relation.ispartofGeospatial Healthen_US
dc.titleSpatio-temporal and stochastic modelling of the severe acute respiratory syndrome (SARS)en_US
dc.typeArticleen_US
dc.identifier.emailLai, PC: pclai@hku.hken_US
dc.identifier.emailKwong, KH: h0110454@hkusua.hku.hken_US
dc.identifier.emailWong, FHT: fhtwong@hku.hken_US
dc.identifier.authorityLai, PC=rp00565en_US
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.4081/gh.2013.65-
dc.identifier.pmid24258894-
dc.identifier.scopuseid_2-s2.0-84888120791-
dc.identifier.hkuros234282en_US
dc.identifier.volume8en_US
dc.identifier.issue1en_US
dc.publisher.placeItalyen_US

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