IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0219190.html
   My bibliography  Save this article

Quantifying uncertainty about future antimicrobial resistance: Comparing structured expert judgment and statistical forecasting methods

Author

Listed:
  • Abigail R Colson
  • Itamar Megiddo
  • Gerardo Alvarez-Uria
  • Sumanth Gandra
  • Tim Bedford
  • Alec Morton
  • Roger M Cooke
  • Ramanan Laxminarayan

Abstract

The increase of multidrug resistance and resistance to last-line antibiotics is a major global public health threat. Although surveillance programs provide useful current and historical information on the scale of the problem, the future emergence and spread of antibiotic resistance is uncertain, and quantifying this uncertainty is crucial for guiding decisions about investment in antibiotics and resistance control strategies. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts in France, Spain, and the United Kingdom projected that resistance for five pairs on the World Health Organization’s priority pathogens list (E. coli and K. pneumoniae resistant to third-generation cephalosporins and carbapenems and MRSA) would remain below 50% in 2026. In Italy, although upper bounds of 90% credible ranges exceed 50% resistance for some pairs, the medians suggest Italy will sustain or improve its current rates. We compare these expert projections to statistical forecasts based on historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts, demonstrating the potential of structured expert judgment as a tool for better understanding the uncertainty about future antibiotic resistance.

Suggested Citation

  • Abigail R Colson & Itamar Megiddo & Gerardo Alvarez-Uria & Sumanth Gandra & Tim Bedford & Alec Morton & Roger M Cooke & Ramanan Laxminarayan, 2019. "Quantifying uncertainty about future antimicrobial resistance: Comparing structured expert judgment and statistical forecasting methods," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-18, July.
    • Handle: RePEc:plo:pone00:0219190
    DOI: 10.1371/journal.pone.0219190
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0219190
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0219190&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0219190?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. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Bolger, Fergus & Wright, George, 2017. "Use of expert knowledge to anticipate the future: Issues, analysis and directions," International Journal of Forecasting, Elsevier, vol. 33(1), pages 230-243.
    3. Willy Aspinall, 2010. "A route to more tractable expert advice," Nature, Nature, vol. 463(7279), pages 294-295, January.
    4. Colson, Abigail R. & Cooke, Roger M., 2017. "Cross validation for the classical model of structured expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 109-120.
    5. J. L. Bamber & W. P. Aspinall, 2013. "An expert judgement assessment of future sea level rise from the ice sheets," Nature Climate Change, Nature, vol. 3(4), pages 424-427, April.
    6. W P Aspinall & R M Cooke & A H Havelaar & S Hoffmann & T Hald, 2016. "Evaluation of a Performance-Based Expert Elicitation: WHO Global Attribution of Foodborne Diseases," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-14, March.
    7. Cooke, Roger M. & Goossens, Louis L.H.J., 2008. "TU Delft expert judgment data base," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 657-674.
    8. Roger M. Cooke, 2015. "Messaging climate change uncertainty," Nature Climate Change, Nature, vol. 5(1), pages 8-10, January.
    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. M. Z. A. M. Jaffar & A. N. Zailan, 2021. "Antimicrobial Resistance (Amr)-Forecast For 30 Countries In Europe," Science Heritage Journal (GWS), Zibeline International Publishing, vol. 5(2), pages 44-48, November.

    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. Colson, Abigail R. & Cooke, Roger M., 2017. "Cross validation for the classical model of structured expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 109-120.
    2. Abigail R Colson & Roger M Cooke, 2018. "Expert Elicitation: Using the Classical Model to Validate Experts’ Judgments," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 12(1), pages 113-132.
    3. Jeremy Rohmer & Eric Chojnacki, 2021. "Forecast of environment systems using expert judgements: performance comparison between the possibilistic and the classical model," Environment Systems and Decisions, Springer, vol. 41(1), pages 131-146, March.
    4. Alexander M. R. Bakker & Domitille Louchard & Klaus Keller, 2017. "Sources and implications of deep uncertainties surrounding sea-level projections," Climatic Change, Springer, vol. 140(3), pages 339-347, February.
    5. Cooke, Roger M., 2014. "Deep and Shallow Uncertainty in Messaging Climate Change," RFF Working Paper Series dp-14-11, Resources for the Future.
    6. Ren, Xin & Nane, Gabriela F. & Terwel, Karel C. & van Gelder, Pieter H.A.J.M., 2024. "Measuring the impacts of human and organizational factors on human errors in the Dutch construction industry using structured expert judgement," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    8. Hanea, A.M. & McBride, M.F. & Burgman, M.A. & Wintle, B.C. & Fidler, F. & Flander, L. & Twardy, C.R. & Manning, B. & Mascaro, S., 2017. "I nvestigate D iscuss E stimate A ggregate for structured expert judgement," International Journal of Forecasting, Elsevier, vol. 33(1), pages 267-279.
    9. Laura Diaz Anadon & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2016. "Expert views - and disagreements - about the potential of energy technology R&D," Climatic Change, Springer, vol. 136(3), pages 677-691, June.
    10. Martine J. Barons & Lael E. Walsh & Edward E. Salakpi & Linda Nichols, 2024. "A Decision Support System for Sustainable Agriculture and Food Loss Reduction under Uncertain Agricultural Policy Frameworks," Agriculture, MDPI, vol. 14(3), pages 1-21, March.
    11. Nogal, Maria & Morales Nápoles, Oswaldo & O’Connor, Alan, 2019. "Structured expert judgement to understand the intrinsic vulnerability of traffic networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 136-152.
    12. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    13. Anca M. Hanea & Marissa F. McBride & Mark A. Burgman & Bonnie C. Wintle, 2018. "The Value of Performance Weights and Discussion in Aggregated Expert Judgments," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1781-1794, September.
    14. Flandoli, F. & Giorgi, E. & Aspinall, W.P. & Neri, A., 2011. "Comparison of a new expert elicitation model with the Classical Model, equal weights and single experts, using a cross-validation technique," Reliability Engineering and System Safety, Elsevier, vol. 96(10), pages 1292-1310.
    15. Rongen, G. & Morales-Nápoles, O. & Kok, M., 2022. "Expert judgment-based reliability analysis of the Dutch flood defense system," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    16. Döpke, Jörg & Müller, Karsten & Tegtmeier, Lars, 2018. "The economic value of business cycle forecasts for potential investors – Evidence from Germany," Research in International Business and Finance, Elsevier, vol. 46(C), pages 445-461.
    17. World Health Organization, Foodborne Epidemiology Reference Group, Source Attribution Task Force, 2016. "Research Synthesis Methods in an Age of Globalized Risks: Lessons from the Global Burden of Foodborne Disease Expert Elicitation," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 191-202, February.
    18. JL Bamber & WP Aspinall & RM Cooke, 2016. "A commentary on “how to interpret expert judgment assessments of twenty-first century sea-level rise” by Hylke de Vries and Roderik SW van de Wal," Climatic Change, Springer, vol. 137(3), pages 321-328, August.
    19. Hathout, Michel & Vuillet, Marc & Carvajal, Claudio & Peyras, Laurent & Diab, Youssef, 2019. "Expert judgments calibration and combination for assessment of river levee failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 377-392.
    20. Despoina Makariou & Pauline Barrieu & George Tzougas, 2021. "A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures," Risks, MDPI, vol. 9(6), pages 1-25, June.

    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:plo:pone00:0219190. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.