Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou, 310058, China.
Hainan Institute of Zhejiang University, Sanya, 572000, China.
Sci Data. 2024 Feb 27;11(1):244. doi: 10.1038/s41597-024-03085-7.
Infectious disease outbreaks transcend the medical and public health realms, triggering widespread panic and impeding socio-economic development. Considering that self-limiting diarrhoea of sporadic cases is usually underreported, the Salmonella outbreak (SO) study offers a unique opportunity for source tracing, spatiotemporal correlation, and outbreak prediction. To summarize the pattern of SO and estimate observational epidemiological indicators, 1,134 qualitative reports screened from 1949 to 2023 were included in the systematic review dataset, which contained a 506-study meta-analysis dataset. In addition to the dataset comprising over 50 columns with a total of 46,494 entries eligible for inclusion in systematic reviews or input into prediction models, we also provide initial literature collection datasets and datasets containing socio-economic and climate information for relevant regions. This study has a broad impact on advancing knowledge regarding epidemic trends and prevention priorities in diverse salmonellosis outbreaks and guiding rational policy-making or predictive modeling to mitigate the infringement upon the right to life imposed by significant epidemics.
传染病的爆发不仅涉及医学和公共卫生领域,还会引发广泛的恐慌并阻碍社会经济的发展。鉴于偶发性自限性腹泻通常报告不足,沙门氏菌病爆发(SO)研究为溯源、时空关联和爆发预测提供了独特的机会。为了总结 SO 的模式并估计观察性流行病学指标,从 1949 年至 2023 年,共筛选了 1134 份定性报告,这些报告被纳入了系统评价数据集,其中包含 506 项研究的荟萃分析数据集。除了包含 50 多列、总计 46494 项符合纳入系统评价或输入预测模型标准的数据集外,我们还提供了初始文献收集数据集和包含相关地区社会经济及气候信息的数据集。本研究对推进不同沙门氏菌病爆发的流行趋势和预防重点相关知识、指导合理的政策制定或预测模型以减轻重大疫情对生命权的侵犯具有广泛影响。