School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Public Health, Center of Statistical Science, Peking University, Beijing 100871, China.
Int J Environ Res Public Health. 2018 Aug 13;15(8):1740. doi: 10.3390/ijerph15081740.
Foodborne diseases have a big impact on public health and are often underreported. This is because a lot of patients delay treatment when they suffer from foodborne diseases. In Hunan Province (China), a total of 21,226 confirmed foodborne disease cases were reported from 1 March 2015 to 28 February 2016 by the Foodborne Surveillance Database (FSD) of the China National Centre for Food Safety Risk Assessment (CFSA). The purpose of this study was to make use of the daily number of visiting patients to forecast the daily true number of patients. Our main contribution is that we take the reporting delays into consideration and propose a Bayesian hierarchical model for this forecast problem. The data shows that there were 21,226 confirmed cases reported among 21,866 visiting patients, a proportion as high as 97%. Given this observation, the Bayesian hierarchical model was established to predict the daily true number of patients using the number of visiting patients. We propose several scoring rules to assess the performance of different nowcasting procedures. We conclude that Bayesian nowcasting with consideration of right truncation of the reporting delays has a good performance for short-term forecasting, and could effectively predict the epidemic trends of foodborne diseases. Meanwhile, this approach could provide a methodological basis for future foodborne disease monitoring and control strategies, which are crucial for public health.
食源性疾病对公众健康有重大影响，但往往报告不足。这是因为很多患者在患食源性疾病时会延迟治疗。在中国湖南省，2015 年 3 月 1 日至 2016 年 2 月 28 日，中国食品安全风险评估中心（CFSA）的食源性疾病监测数据库（FSD）共报告了 21226 例确诊食源性疾病病例。本研究旨在利用每日就诊人数预测每日真实患者人数。我们的主要贡献是考虑了报告延迟，并为该预测问题提出了一个贝叶斯层次模型。数据显示，在 21866 名就诊患者中报告了 21226 例确诊病例，比例高达 97%。鉴于这一观察结果，我们建立了贝叶斯层次模型，使用就诊人数来预测每日真实患者人数。我们提出了几种评分规则来评估不同即时预测程序的性能。我们得出结论，考虑到报告延迟的右截断的贝叶斯即时预测对于短期预测具有良好的性能，并且可以有效预测食源性疾病的流行趋势。同时，该方法可以为未来的食源性疾病监测和控制策略提供方法学基础，这对公众健康至关重要。
