| Title | A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases |
| Authors | Wang, Xueli Zhou, Moqin Jia, Jinzhu Geng, Zhi Xiao, Gexin |
| Affiliation | Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China. Peking Univ, Ctr Stat Sci, Sch Publ Hlth, Beijing 100871, Peoples R China. Peking Univ, Ctr Stat Sci, Sch Math Sci, Beijing 100871, Peoples R China. China Natl Ctr Food Safety Risk Assessment, Beijing 100022, Peoples R China. |
| Keywords | Bayesian hierarchical model foodborne disease nowcasting reporting delay right truncation UNITED-STATES REPORTING DELAYS ILLNESS BURDEN OUTBREAK FOOD AIDS |
| Issue Date | 2018 |
| Publisher | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH |
| Citation | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. 2018, 15(8). |
| Abstract | 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. |
| URI | http://hdl.handle.net/20.500.11897/517846 |
| ISSN | 1660-4601 |
| DOI | 10.3390/ijerph15081740 |
| Indexed | SCI(E) PubMed SSCI |
| Appears in Collections: | 数学科学学院 |
