Cargando…
Spatial Components in Disease Modelling
Modelling of infectious diseases could help gain further understanding of their diffusion processes that provide knowledge on the detection of epidemics and decision making for future infection control measures. Conventional disease transmission models are inadequate in considering the diverse natur...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122710/ http://dx.doi.org/10.1007/978-3-642-12156-2_30 |
_version_ | 1783515477500231680 |
---|---|
author | Kwong, Kim-hung Lai, Poh-chin |
author_facet | Kwong, Kim-hung Lai, Poh-chin |
author_sort | Kwong, Kim-hung |
collection | PubMed |
description | Modelling of infectious diseases could help gain further understanding of their diffusion processes that provide knowledge on the detection of epidemics and decision making for future infection control measures. Conventional disease transmission models are inadequate in considering the diverse nature of a society and its location-specific factors. A new approach incorporating stochastic and spatial factors is necessary to better reflect the situation. However, research on risk factors in disease diffusion is limited in numbers. This paper mapped the different phases of spatial diffusion of SARS in Hong Kong to explore the underlying spatial factors that may have interfered and contributed to the transmission patterns of SARS. Results of the current study provide important bases to inform relevant environmental attributes that could potentially improve the spatial modelling of an infectious disease. |
format | Online Article Text |
id | pubmed-7122710 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71227102020-04-06 Spatial Components in Disease Modelling Kwong, Kim-hung Lai, Poh-chin Computational Science and Its Applications – ICCSA 2010 Article Modelling of infectious diseases could help gain further understanding of their diffusion processes that provide knowledge on the detection of epidemics and decision making for future infection control measures. Conventional disease transmission models are inadequate in considering the diverse nature of a society and its location-specific factors. A new approach incorporating stochastic and spatial factors is necessary to better reflect the situation. However, research on risk factors in disease diffusion is limited in numbers. This paper mapped the different phases of spatial diffusion of SARS in Hong Kong to explore the underlying spatial factors that may have interfered and contributed to the transmission patterns of SARS. Results of the current study provide important bases to inform relevant environmental attributes that could potentially improve the spatial modelling of an infectious disease. 2010 /pmc/articles/PMC7122710/ http://dx.doi.org/10.1007/978-3-642-12156-2_30 Text en © Springer-Verlag Berlin Heidelberg 2010 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 | Article Kwong, Kim-hung Lai, Poh-chin Spatial Components in Disease Modelling |
title | Spatial Components in Disease Modelling |
title_full | Spatial Components in Disease Modelling |
title_fullStr | Spatial Components in Disease Modelling |
title_full_unstemmed | Spatial Components in Disease Modelling |
title_short | Spatial Components in Disease Modelling |
title_sort | spatial components in disease modelling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122710/ http://dx.doi.org/10.1007/978-3-642-12156-2_30 |
work_keys_str_mv | AT kwongkimhung spatialcomponentsindiseasemodelling AT laipohchin spatialcomponentsindiseasemodelling |