IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v40y2020i11p2442-2461.html
   My bibliography  Save this article

Demonstrating the Benefits of Predictive Bayesian Dose–Response Relationships Using Six Exposure Studies of Cryptosporidium parvum

Author

Listed:
  • Frederick Bloetscher
  • Daniel Meeroff
  • Sharon C. Long
  • Jeanine D. Dudle

Abstract

A conventional dose–response function can be refitted as additional data become available. A predictive dose–response function in contrast does not require a curve‐fitting step, only additional data and presents the unconditional probabilities of illness, reflecting the level of information it contains. In contrast, the predictive Bayesian dose–response function becomes progressively less conservative as more information is included. This investigation evaluated the potential for using predictive Bayesian methods to develop a dose–response for human infection that improves on existing models, to show how predictive Bayesian statistical methods can utilize additional data, and expand the Bayesian methods for a broad audience including those concerned about an oversimplification of dose–response curve use in quantitative microbial risk assessment (QMRA). This study used a dose–response relationship incorporating six separate data sets for Cryptosporidium parvum. A Pareto II distribution with known priors was applied to one of the six data sets to calibrate the model, while the others were used for subsequent updating. While epidemiological principles indicate that local variations, host susceptibility, and organism strain virulence may vary, the six data sets all appear to be well characterized using the Bayesian approach. The adaptable model was applied to an existing data set for Campylobacter jejuni for model validation purposes, which yielded results that demonstrate the ability to analyze a dose–response function with limited data using and update those relationships with new data. An analysis of the goodness of fit compared to the beta‐Poisson methods also demonstrated correlation between the predictive Bayesian model and the data.

Suggested Citation

  • Frederick Bloetscher & Daniel Meeroff & Sharon C. Long & Jeanine D. Dudle, 2020. "Demonstrating the Benefits of Predictive Bayesian Dose–Response Relationships Using Six Exposure Studies of Cryptosporidium parvum," Risk Analysis, John Wiley & Sons, vol. 40(11), pages 2442-2461, November.
  • Handle: RePEc:wly:riskan:v:40:y:2020:i:11:p:2442-2461
    DOI: 10.1111/risa.13552
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.13552
    Download Restriction: no

    File URL: https://libkey.io/10.1111/risa.13552?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lailai Chen & Helena Geys & Shaun Cawthraw & Arie Havelaar & Peter Teunis, 2006. "Dose Response for Infectivity of Several Strains of Campylobacter jejuni in Chickens," Risk Analysis, John Wiley & Sons, vol. 26(6), pages 1613-1621, December.
    2. Peter F. M. Teunis & Cynthia L. Chappell & Pablo C. Okhuysen, 2002. "Cryptosporidium Dose‐Response Studies: Variation Between Hosts," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 475-485, June.
    3. Peter F. M. Teunis & Cynthia L. Chappell & Pablo C. Okhuysen, 2002. "Cryptosporidium Dose Response Studies: Variation Between Isolates," Risk Analysis, John Wiley & Sons, vol. 22(1), pages 175-185, February.
    Full references (including those not matched with items on IDEAS)

    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. K. D. M. Pintar & A. Fazil & F. Pollari & D. Waltner‐Toews & D. F. Charron & S. A. McEwen & T. Walton, 2012. "Considering the Risk of Infection by Cryptosporidium via Consumption of Municipally Treated Drinking Water from a Surface Water Source in a Southwestern Ontario Community," Risk Analysis, John Wiley & Sons, vol. 32(7), pages 1122-1138, July.
    2. Lailai Chen & Helena Geys & Shaun Cawthraw & Arie Havelaar & Peter Teunis, 2006. "Dose Response for Infectivity of Several Strains of Campylobacter jejuni in Chickens," Risk Analysis, John Wiley & Sons, vol. 26(6), pages 1613-1621, December.
    3. Régis Pouillot & Pascal Beaudeau & Jean‐Baptiste Denis & Francis Derouin & AFSSA Cryptosporidium Study Group, 2004. "A Quantitative Risk Assessment of Waterborne Cryptosporidiosis in France Using Second‐Order Monte Carlo Simulation," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 1-17, February.
    4. Anna Makri & Reza Modarres & Rebecca Parkin, 2004. "Cryptosporidiosis Susceptibility and Risk: A Case Study," Risk Analysis, John Wiley & Sons, vol. 24(1), pages 209-220, February.
    5. Peter Teunis & Katsuhisa Takumi & Kunihiro Shinagawa, 2004. "Dose Response for Infection by Escherichia coli O157:H7 from Outbreak Data," Risk Analysis, John Wiley & Sons, vol. 24(2), pages 401-407, April.
    6. S. R. Petterson, 2016. "Application of a QMRA Framework to Inform Selection of Drinking Water Interventions in the Developing Context," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 203-214, February.
    7. Vegard Nilsen & John Wyller, 2016. "QMRA for Drinking Water: 1. Revisiting the Mathematical Structure of Single‐Hit Dose‐Response Models," Risk Analysis, John Wiley & Sons, vol. 36(1), pages 145-162, January.
    8. Michael J. Messner & Philip Berger, 2016. "Cryptosporidium Infection Risk: Results of New Dose‐Response Modeling," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1969-1982, October.
    9. Michael Greenberg & Charles Haas & Anthony Cox & Karen Lowrie & Katherine McComas & Warner North, 2012. "Ten Most Important Accomplishments in Risk Analysis, 1980–2010," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 771-781, May.
    10. Tingting Gao & Rong Chen & Yanzheng Liu & Xiaochang C. Wang & Yuyou Li, 2018. "Construction of a Dose–Illness Relationship via Modeling Morbidity and Application to Risk Assessment of Wastewater Reuse," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1672-1684, August.
    11. Philip J. Schmidt & Katarina D. M. Pintar & Aamir M. Fazil & Edward Topp, 2013. "Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1677-1693, September.
    12. James D. Englehardt & Jeff Swartout, 2006. "Predictive Bayesian Microbial Dose‐Response Assessment Based on Suggested Self‐Organization in Primary Illness Response: Cryptosporidium parvum," Risk Analysis, John Wiley & Sons, vol. 26(2), pages 543-554, April.

    More about this item

    Statistics

    Access and download statistics

    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:wly:riskan:v:40:y:2020:i:11:p:2442-2461. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    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.