Scientific Data (Feb 2024)

Salmonellosis outbreak archive in China: data collection and assembly

  • Zining Wang,
  • Chenghu Huang,
  • Yuhao Liu,
  • Jiaqi Chen,
  • Rui Yin,
  • Chenghao Jia,
  • Xiamei Kang,
  • Xiao Zhou,
  • Sihao Liao,
  • Xiuyan Jin,
  • Mengyao Feng,
  • Zhijie Jiang,
  • Yan Song,
  • Haiyang Zhou,
  • Yicheng Yao,
  • Lin Teng,
  • Baikui Wang,
  • Yan Li,
  • Min Yue

DOI
https://doi.org/10.1038/s41597-024-03085-7
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 7

Abstract

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Abstract 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.