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Volume 15
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Addendum published on 23 April 2019, see Int. J. Environ. Res. Public Health 2019, 16(8), 1442.
Article

A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases

by
Xueli Wang
1,
Moqin Zhou
1,
Jinzhu Jia
2,
Zhi Geng
3 and
Gexin Xiao
4,*
1
School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
2
School of Public Health, Center of Statistical Science, Peking University, Beijing 100871, China
3
School of Mathematical Sciences, Center of Statistical Science, Peking University, Beijing 100871, China
4
China National Center for Food Safety Risk Assessment, Beijing 100022, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(8), 1740; https://doi.org/10.3390/ijerph15081740
Received: 13 June 2018 / Revised: 5 August 2018 / Accepted: 10 August 2018 / Published: 13 August 2018
(This article belongs to the Section Health Behavior, Chronic Disease and Health Promotion)
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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 apply 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. View Full-Text
Keywords: Bayesian hierarchical model; foodborne disease; nowcasting; reporting delay; right truncation Bayesian hierarchical model; foodborne disease; nowcasting; reporting delay; right truncation
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MDPI and ACS Style

Wang, X.; Zhou, M.; Jia, J.; Geng, Z.; Xiao, G. A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases. Int. J. Environ. Res. Public Health 2018, 15, 1740. https://doi.org/10.3390/ijerph15081740

AMA Style

Wang X, Zhou M, Jia J, Geng Z, Xiao G. A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases. International Journal of Environmental Research and Public Health. 2018; 15(8):1740. https://doi.org/10.3390/ijerph15081740

Chicago/Turabian Style

Wang, Xueli, Moqin Zhou, Jinzhu Jia, Zhi Geng, and Gexin Xiao. 2018. "A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases" International Journal of Environmental Research and Public Health 15, no. 8: 1740. https://doi.org/10.3390/ijerph15081740

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