About: Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China

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About: Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
Creator	Christakos, George Han, Wei-Guo Meng, Bin Wang, Jin-Feng
Source	Medline; PMC
abstract	BACKGROUND: Severe Acute Respiratory Syndrome (SARS) was first reported in November 2002 in China, and spreads to about 30 countries over the next few months. While the characteristics of epidemic transmission are individually assessed, there are also important implicit associations between them. METHODS: A novel methodological framework was developed to overcome barriers among separate epidemic statistics and identify distinctive SARS features. Individual statistics were pair-wise linked in terms of their common features, and an integrative epidemic network was formulated. RESULTS: The study of associations between important SARS characteristics considerably enhanced the mainstream epidemic analysis and improved the understanding of the relationships between the observed epidemic determinants. The response of SARS transmission to various epidemic control factors was simulated, target areas were detected, critical time and relevant factors were determined. CONCLUSION: It was shown that by properly accounting for links between different SARS statistics, a data-based analysis can efficiently reveal systematic associations between epidemic determinants. The analysis can predict the temporal trend of the epidemic given its spatial pattern, to estimate spatial exposure given temporal evolution, and to infer the driving forces of SARS transmission given the spatial exposure distribution.
has issue date	2008-04-26(xsd:dateTime)
bibo:doi	10.1093/pubmed/fdn023
bibo:pmid	18441347
has license	cc-by-nc
sha1sum (hex)	d0c1b37380fd7838db1f135fd702c8fd0cd67361
schema:url	https://doi.org/10.1093/pubmed/fdn023
resource representing a document's title	Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China
has PubMed Central identifier	PMC2518065
has PubMed identifier	18441347
schema:publication	J Public Health (Oxf)
resource representing a document's body	covid:d0c1b37380fd7838db1f135fd702c8fd0cd67361#body_text
is schema:about of	named entity 'ABOUT' named entity 'EPIDEMIC' named entity 'TRANSMISSION' named entity 'IMPORTANT' named entity 'While' named entity 'SCIENCES' covid:arg/d0c1b37380fd7838db1f135fd702c8fd0cd67361 named entity 'SEVERE ACUTE RESPIRATORY SYNDROME' named entity 'CHARACTERISTICS' named entity 'ASSOCIATIONS' named entity 'ASSESSED' named entity 'BACKGROUND' named entity 'RESEARCH' named entity 'INSTITUTE' named entity 'GEOGRAPHICAL' named entity 'NOVEMBER' named entity 'SPREADS' named entity 'REPORTED' named entity 'MONTHS' named entity 'NATURAL RESOURCES' named entity 'CHINA' named entity 'COUNTRIES' named entity 'transmission' named entity 'epidemic' named entity 'Severe Acute Respiratory Syndrome' named entity 'GeoDA' named entity 'epidemic' named entity 'disease mechanisms' named entity 'MatLab' named entity 'epidemic' named entity 'algorithm' named entity 'SARS' named entity 'SARS' named entity 'SARS epidemic' named entity 'GIS' named entity 'Beijing' named entity 'disease mechanisms' named entity 'GIS' named entity 'SARS' named entity 'public health' named entity 'SARS epidemic' named entity 'SARS' named entity 'hierarchical clustering' named entity 'epidemic spread' named entity 'SARS' named entity 'SARS' named entity 'SEIR' named entity 'SARS epidemic' named entity 'epidemic' named entity 'random field' named entity 'infection' named entity 'coronavirus' named entity 'Severe Acute respiratory Syndrome' named entity 'mathematical model' named entity 'coronavirus' named entity 'SARS' named entity 'SARS' named entity 'GIS' named entity 'SARS' named entity 'coronavirus' named entity 'SEIR' named entity 'epidemic' named entity 'epidemic' named entity 'stochastic' named entity 'scientific knowledge' named entity '2003 outbreak' named entity 'epidemic' named entity 'epidemic' named entity 'db3' named entity 'SARS' named entity 'epidemic' named entity 'Population density' named entity 'evolution' named entity 'data analysis' named entity 'spatial analysis' named entity 'randomized control trials' named entity 'SARS' named entity 'epidemic'

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