ArticleScientific reports2017
Spatio-temporal variations of typhoid and paratyphoid fevers in Zhejiang Province, China from 2005 to 2015.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Identifying High-Risk Spatiotemporal Clusters of Mushroom Poisoning in Subtropical China: A Retrospective Surveillance Study in Zhejiang Province (2012-2023).Foods (Basel, Switzerland) · 2026Article
- Epidemiological characteristics and spatial clustering analysis of typhoid and paratyphoid fevers in China, 2014-2022.BMC infectious diseases · 2025Article
- Spatio-temporal pattern and associate factors study on intestinal infectious diseases based on panel model in Zhejiang Province.BMC public health · 2024Article
- Spatial and temporal clustering of typhoid fever in an urban slum of Dhaka City: Implications for targeted typhoid vaccination.PLoS neglected tropical diseases · 2024Article
- Spatio-temporal pattern and associate factors of intestinal infectious diseases in Zhejiang Province, China, 2008-2021: a Bayesian modeling study.BMC public health · 2023Article
- Dynamic Incidence of Typhoid Fever over a 10-Year Period (2010-2019) in Kibera, an Urban Informal Settlement in Nairobi, Kenya.The American journal of tropical medicine and hygiene · 2023Article
- Spatial-temporal and phylogenetic analyses of epidemiologic data to help understand the modes of transmission of endemic typhoid fever in Samoa.PLoS neglected tropical diseases · 2022Article
- Spatio-temporal variation on syphilis from 2005 to 2018 in Zhejiang Province, China.Frontiers in public health · 2022Article
- Impact of Temperature and Rainfall on Typhoid/Paratyphoid Fever in Taizhou, China: Effect Estimation and Vulnerable Group Identification.The American journal of tropical medicine and hygiene · 2021Article
- Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020.International journal of environmental research and public health · 2021Article
- Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models.PloS one · 2020Article
- Spatial Heterogeneity and Temporal Trends in Malaria on the Thai⁻Myanmar Border (2012⁻2017): A Retrospective Observational Study.Tropical medicine and infectious disease · 2019Article
- Temporal, spatial and household dynamics of Typhoid fever in Kasese district, Uganda.PloS one · 2019Article
- The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China.Medicine · 2018Article
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9 authors.
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Abstract
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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