4.7 Article

Spatio-temporal variations of typhoid and paratyphoid fevers in Zhejiang Province, China from 2005 to 2015

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SCIENTIFIC REPORTS
卷 7, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41598-017-05928-3

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资金

  1. Key Project in Soft Science by the Science and Technology Department of Zhejiang Province [2015C25027]
  2. province-ministry project of the Medical Health Scientific Research Fund of Zhejiang Province [2016154578]
  3. Medical Research Programme of Zhejiang Province [2016KYB056]
  4. Medical Health Scientific Research Fund of Zhejiang Province [2015KY070]
  5. National Scholarship Fund [201606325034]
  6. Soft Science Key Project of Hangzhou Municipal Science Committee [20160834M03]

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Typhoid and paratyphoid are two common enteric infectious diseases with serious gastrointestinal symptoms. Data was collected of the registered cases in Zhejiang Province from 2005 to 2015. The epidemiological characteristics were investigated and high-risk regions were detected with descriptive epidemiological methods and in-depth spatio-temporal statistics. A sharp decline in the incidences of both diseases was observed. The seasonal patterns were identified with typhoid and paratyphoid, one in summer from May to September was observed from 2005 to 2010 and the other lesser one in spring from January to March only observed from 2005 to 2007. The men were more susceptible and the adults aged 20 to 60 constituted the major infected population. The farmers were more likely to get infected, especially to typhoid. The Wilcoxon sum rank test proved that the incidences in the coastal counties were significantly higher than the inland. Besides, a positive autocorrelation was obtained with typhoid fever in global autocorrelation analysis but not with paratyphoid fever. Local autocorrelation analysis and spatio-temporal scan statistics revealed that high-risk clusters were located mainly in the coastal regions with typhoid fever but scattered across the province with paratyphoid fever. The spatial risks were evaluated quantitatively with hierarchical Bayesian models.

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