Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China
Identifieur interne : 001081 ( Main/Exploration ); précédent : 001080; suivant : 001082Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China
Auteurs : Chunxiang Cao [République populaire de Chine] ; Wei Chen [République populaire de Chine] ; Sheng Zheng [République populaire de Chine] ; Jian Zhao [République populaire de Chine] ; Jinfeng Wang [République populaire de Chine] ; Wuchun Cao [République populaire de Chine]Source :
- BioMed Research International [ 2314-6133 ] ; 2016.
Abstract
Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.
Url:
DOI: 10.1155/2016/7247983
PubMed: 27597972
PubMed Central: 5002496
Affiliations:
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type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Chen, Wei" sort="Chen, Wei" uniqKey="Chen W" first="Wei" last="Chen">Wei Chen</name><affiliation wicri:level="1"><nlm:aff id="I1">State Key Laboratory of Remote Sensing Science, Beijing 100101, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Remote Sensing Science, Beijing 100101</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zheng, Sheng" sort="Zheng, Sheng" uniqKey="Zheng S" first="Sheng" last="Zheng">Sheng Zheng</name><affiliation wicri:level="1"><nlm:aff id="I1">State Key Laboratory of Remote Sensing Science, Beijing 100101, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Remote Sensing Science, Beijing 100101</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zhao, Jian" sort="Zhao, Jian" uniqKey="Zhao J" first="Jian" last="Zhao">Jian Zhao</name><affiliation wicri:level="1"><nlm:aff id="I1">State Key Laboratory of Remote Sensing Science, Beijing 100101, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Remote Sensing Science, Beijing 100101</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><affiliation wicri:level="1"><nlm:aff id="I2">Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China</nlm:aff><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Geographic Sciences and Natural Resources Research, Chinese 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respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Shuman, S" uniqKey="Shuman S">S. Shuman</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Chomczynski, P" uniqKey="Chomczynski P">P. Chomczynski</name></author><author><name sortKey="Sacchi, N" uniqKey="Sacchi N">N. Sacchi</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Eames, K T D" uniqKey="Eames K">K. T. D. Eames</name></author><author><name sortKey="Keeling, M J" uniqKey="Keeling M">M. J. Keeling</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lipsitch, M" uniqKey="Lipsitch M">M. Lipsitch</name></author><author><name sortKey="Cohen, T" uniqKey="Cohen T">T. 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Aihara</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Cao, Chunxiang" sort="Cao, Chunxiang" uniqKey="Cao C" first="Chunxiang" last="Cao">Chunxiang Cao</name></noRegion><name sortKey="Cao, Wuchun" sort="Cao, Wuchun" uniqKey="Cao W" first="Wuchun" last="Cao">Wuchun Cao</name><name sortKey="Chen, Wei" sort="Chen, Wei" uniqKey="Chen W" first="Wei" last="Chen">Wei Chen</name><name sortKey="Wang, Jinfeng" sort="Wang, Jinfeng" uniqKey="Wang J" first="Jinfeng" last="Wang">Jinfeng Wang</name><name sortKey="Zhao, Jian" sort="Zhao, Jian" uniqKey="Zhao J" first="Jian" last="Zhao">Jian Zhao</name><name sortKey="Zheng, Sheng" sort="Zheng, Sheng" uniqKey="Zheng S" first="Sheng" last="Zheng">Sheng Zheng</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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