Spatio-temporal and stochastic modelling of severe acute respiratory syndrome
This 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2013 |
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| Erschienen: |
2013 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
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| Enthalten in: |
Geospatial health - 8(2013), 1 vom: 16. Nov., Seite 183-92 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lai, Poh-Chin [VerfasserIn] |
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| Themen: |
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| Anmerkungen: |
Date Completed 25.07.2014 Date Revised 22.11.2013 published: Print Citation Status MEDLINE |
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| Förderinstitution / Projekttitel: |
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NLM232901023 |
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| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a This 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 | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Wong, Ho-Ting |e verfasserin |4 aut | |
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