IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2018i8p1740-d163570.html
   My bibliography  Save this article

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

Author

Listed:
  • Xueli Wang

    (School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Moqin Zhou

    (School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Jinzhu Jia

    (School of Public Health, Center of Statistical Science, Peking University, Beijing 100871, China)

  • Zhi Geng

    (School of Mathematical Sciences, Center of Statistical Science, Peking University, Beijing 100871, China)

  • Gexin Xiao

    (China National Center for Food Safety Risk Assessment, Beijing 100022, China)

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.

Suggested Citation

  • Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2018. "A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases," IJERPH, MDPI, vol. 15(8), pages 1-13, August.
  • Handle: RePEc:gam:jijerp:v:15:y:2018:i:8:p:1740-:d:163570
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/8/1740/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/15/8/1740/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eunjoo Yang & Hyun Woo Park & Yeon Hwa Choi & Jusim Kim & Lkhagvadorj Munkhdalai & Ibrahim Musa & Keun Ho Ryu, 2018. "A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks," IJERPH, MDPI, vol. 15(5), pages 1-18, May.
    2. Michael Höhle & Matthias an der Heiden, 2014. "Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011," Biometrics, The International Biometric Society, vol. 70(4), pages 993-1002, December.
    3. James Kaufman & Justin Lessler & April Harry & Stefan Edlund & Kun Hu & Judith Douglas & Christian Thoens & Bernd Appel & Annemarie Käsbohrer & Matthias Filter, 2014. "A Likelihood-Based Approach to Identifying Contaminated Food Products Using Sales Data: Performance and Challenges," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-10, July.
    4. Claudia Czado & Tilmann Gneiting & Leonhard Held, 2009. "Predictive Model Assessment for Count Data," Biometrics, The International Biometric Society, vol. 65(4), pages 1254-1261, December.
    5. Lin Wang & Joseph T. Wu, 2018. "Characterizing the dynamics underlying global spread of epidemics," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2019. "Addendum: Wang et al. A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases. Int. J. Environ. Res. Public Health , 2018, 15(8):1740; doi:10.3390/ijerph15081740," IJERPH, MDPI, vol. 16(8), pages 1-3, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xueli Wang & Moqin Zhou & Jinzhu Jia & Zhi Geng & Gexin Xiao, 2019. "Addendum: Wang et al. A Bayesian Approach to Real-Time Monitoring and Forecasting of Chinese Foodborne Diseases. Int. J. Environ. Res. Public Health , 2018, 15(8):1740; doi:10.3390/ijerph15081740," IJERPH, MDPI, vol. 16(8), pages 1-3, April.
    2. Vasiliki Christou & Konstantinos Fokianos, 2014. "Quasi-Likelihood Inference For Negative Binomial Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(1), pages 55-78, January.
    3. James Mitchell & Martin Weale, 2023. "Censored density forecasts: Production and evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 714-734, August.
    4. Reese Richardson & Emile Jorgensen & Philip Arevalo & Tobias M. Holden & Katelyn M. Gostic & Massimo Pacilli & Isaac Ghinai & Shannon Lightner & Sarah Cobey & Jaline Gerardin, 2022. "Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Lu, Ye & Suthaharan, Neyavan, 2023. "Electricity price spike clustering: A zero-inflated GARX approach," Energy Economics, Elsevier, vol. 124(C).
    6. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    7. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    8. Ashish Gupta & Amit Deokar & Lakshmi Iyer & Ramesh Sharda & Dave Schrader, 2018. "Big Data & Analytics for Societal Impact: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(2), pages 185-194, April.
    9. Zou, Yang & Xiong, Zhongyang & Zhang, Pu & Wang, Wei, 2018. "Social contagions on multiplex networks with different reliability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 728-735.
    10. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    11. Snyder, Ralph D. & Ord, J. Keith & Beaumont, Adrian, 2012. "Forecasting the intermittent demand for slow-moving inventories: A modelling approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 485-496.
    12. Jun Cai & Bo Xu & Karen Kie Yan Chan & Xueying Zhang & Bing Zhang & Ziyue Chen & Bing Xu, 2019. "Roles of Different Transport Modes in the Spatial Spread of the 2009 Influenza A(H1N1) Pandemic in Mainland China," IJERPH, MDPI, vol. 16(2), pages 1-15, January.
    13. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    14. Nicholas G. Reich & Justin Lessler & Krzysztof Sakrejda & Stephen A. Lauer & Sopon Iamsirithaworn & Derek A. T. Cummings, 2016. "Case Study in Evaluating Time Series Prediction Models Using the Relative Mean Absolute Error," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 285-292, July.
    15. Dag Tjøstheim, 2012. "Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 413-438, September.
    16. Antonio Bracale & Pasquale De Falco, 2015. "An Advanced Bayesian Method for Short-Term Probabilistic Forecasting of the Generation of Wind Power," Energies, MDPI, vol. 8(9), pages 1-22, September.
    17. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held, 2022. "Session 3 of the RSS Special Topic Meeting on Covid‐19 Transmission: Replies to the discussion," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 158-164, November.
    18. David Harris & Gael M. Martin & Indeewara Perera & Don S. Poskitt, 2017. "Construction and visualization of optimal confidence sets for frequentist distributional forecasts," Monash Econometrics and Business Statistics Working Papers 9/17, Monash University, Department of Econometrics and Business Statistics.
    19. Birgit Schrödle & Leonhard Held, 2011. "A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$," Computational Statistics, Springer, vol. 26(2), pages 241-258, June.
    20. Oliver Stoner & Theo Economou, 2020. "Multivariate hierarchical frameworks for modeling delayed reporting in count data," Biometrics, The International Biometric Society, vol. 76(3), pages 789-798, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:15:y:2018:i:8:p:1740-:d:163570. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.