Koh Dae-Hyup, Duong Monica, Kipshidze Nodar, Pitzer Virginia E, Kim Jong-Hoon
Epidemiology, Public Health, Impact, International Vaccine Institute, Seoul, Korea.
Graduate School of Public Health, Yonsei University, Seoul, Korea.
Sci Data. 2025 Jan 16;12(1):94. doi: 10.1038/s41597-024-04289-7.
This article presents a comprehensive dataset compiling reported cases of typhoid fever from culture-confirmed outbreaks across various geographical locations from 2000 through 2022, categorized into daily, weekly, and monthly time series. The dataset was curated by identifying peer-reviewed epidemiological studies available in PubMed, OVID-Medline, and OVID-Embase. Time-series incidence data were extracted from plots using WebPlotDigitizer, followed by verification of a subset of the dataset. The primary aim of this dataset is to serve as a foundational tool for researchers and policymakers, enabling the development of robust, model-based strategies for the control of typhoid fever outbreaks. The article describes the method by which the dataset has been compiled and how the quality of the data has been verified. Furthermore, it discusses the dataset's potential applications in optimizing vaccination campaigns, improving public health planning, and tailoring interventions to specific epidemiologic contexts. This article contributes significantly to the field of infectious disease modeling, offering a valuable resource for enhancing typhoid fever control measures globally.
本文展示了一个综合数据集,该数据集汇编了2000年至2022年期间各地通过培养确诊的伤寒热暴发报告病例,并按每日、每周和每月时间序列进行分类。该数据集是通过识别PubMed、OVID-Medline和OVID-Embase中同行评审的流行病学研究来策划的。时间序列发病率数据是使用WebPlotDigitizer从图表中提取的,随后对数据集的一个子集进行了验证。该数据集的主要目的是作为研究人员和政策制定者的基础工具,以便制定强有力的、基于模型的伤寒热暴发控制策略。本文描述了汇编数据集的方法以及数据质量的验证方式。此外,还讨论了该数据集在优化疫苗接种运动、改善公共卫生规划以及针对特定流行病学背景定制干预措施方面的潜在应用。本文对传染病建模领域做出了重大贡献,为全球加强伤寒热控制措施提供了宝贵资源。