Simulation input modelling in the absence of historical data for decision support during crises: Experience with assessing demand uncertainties for simulating walk-through testing in the early waves of COVID-19

References

• Ackermann, F., & Eden, C. (2011). Making strategy: Mapping out strategic success. London.
   Google Scholar
• Altay, N., & Green, W. G. III, (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475–493. https://doi.org/10.1016/j.ejor.2005.05.016
   Web of Science ®Google Scholar
• Bamber, J. L., & Aspinall, W. P. (2013). An expert judgement assessment of future sea level rise from the ice sheets. Nature Climate Change, 3(4), 424–427. https://doi.org/10.1038/nclimate1778
   Web of Science ®Google Scholar
• Beeler, M. F., Aleman, D. M., & Carter, M. W. (2014). A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza. Journal of the Operational Research Society, 65(4), 497–511. https://doi.org/10.1057/jors.2013.148
   Web of Science ®Google Scholar
• Bolger, F. (2018). The selection of experts for (probabilistic) expert knowledge elicitation. In L. Dias, J. Quigley, A. Morton (Eds.), Elicitation. Springer International Series in Operations Research and Management Science (pp. 393–443). Cham.
   Google Scholar
• Botín, A. J., Campbell, N. A., & Guzmán, R. (2015). A discrete-event simulation tool for real-time management of pre-production development fleets in a block-caving project. International Journal of Mining, Reclamation and Environment, 29(5), 347–356.
   Web of Science ®Google Scholar
• Cahill, W., Render, M. (1999). Dynamic simulation modeling of ICU bed availability. In 1999 Winter Simulation Conference Proceedings ‘Simulation-A Bridge to the Future’ (pp. 1573–1576).
   Google Scholar
• Cederman, L. E., & Weidmann, N. B. (2017). Predicting armed conflict: Time to adjust our expectations? Science (New York, N.Y.), 355(6324), 474–476.
   PubMed Web of Science ®Google Scholar
• Cheng, T.-M., & Wu, H.-T. (2006). Simulation with fuzzy durations. International Journal of Applied Science and Engineering, 4(2), 189–203.
   Google Scholar
• Chick, S. E. (2006). Subjective probability and Bayesian methodology. Handbooks in Operations Research and Management Science, 13, 225–257.
   Google Scholar
• Chief Medical Officer Directorate. (2020). Coronavirus (COVID-19): daily data for Scotland. Edinburgh: Scottish Government.
   Google Scholar
• Cooke, R. M. (1991). Experts in uncertainty: Opinion and subjective probability in science. Oxford University Press.
   Google Scholar
• Cooke, R. M. (2004). The anatomy of the squizzel: The role of operational definitions in representing uncertainty. Reliability Engineering & System Safety, 85(1–3), 313–319. https://doi.org/10.1016/j.ress.2004.03.019
   Web of Science ®Google Scholar
• Currie, C. S., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., & Tako, A. A. (2020). How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 14(2), 83–97. https://doi.org/10.1080/17477778.2020.1751570
   Web of Science ®Google Scholar
• Dassisti, M., & Galantucci, L. M. (2005). Pseudo-fuzzy discrete-event simulation for on-line production control. Computers & Industrial Engineering, 49(2), 266–286. https://doi.org/10.1016/j.cie.2004.12.015
   Web of Science ®Google Scholar
• Dassisti, M., Galantucci, L. M., Cicirelli, G. (2000). On-line production control via discrete event simulation: An industrial application. In 16th International Conference on Computer-Aided Production Engineering, the Institution of Mechanical Engineers (pp. 203–212).
   Google Scholar
• Eden, C. (1988). Cognitive mapping. European Journal of Operational Research, 36(1), 1–13. https://doi.org/10.1016/0377-2217(88)90002-1
   Web of Science ®Google Scholar
• European Food and Safety Authority (EFSA). (2014). Guidance on expert knowledge elicitation in food and feed safety risk assessment. EFSA Journal. 12(6), 3734.
   Google Scholar
• Excalibur. (2020). Excalibur Software. www.lighttwist-software.com/excalibur.
   Google Scholar
• Flynn, E. F., Kuhn, E., Shaik, M., Tarr, E., Scattolini, N., & Ballantine, A. (2020). Drive-through COVID-19 testing during the 2020 pandemic: A safe, efficient, and scalable model for pediatric patients and healthcare workers. Academic Pediatrics, 20(6), 753–755. https://doi.org/10.1016/j.acap.2020.05.018
   PubMed Web of Science ®Google Scholar
• Franco, L. A., & Montibeller, G. (2010). Facilitated modelling in operational research. European Journal of Operational Research, 205(3), 489–500. https://doi.org/10.1016/j.ejor.2009.09.030
   Web of Science ®Google Scholar
• French, S. (2011). Aggregating expert judgement. Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas, 105(1), 181–206. https://doi.org/10.1007/s13398-011-0018-6
   Web of Science ®Google Scholar
• Garthwaite, P. H., Chilcott, J. B., Jenkinson, D. J., & Tappenden, P. (2008). Use of expert knowledge in evaluating costs and benefits of alternative service provisions: A case study. International Journal of Technology Assessment in Health Care, 24(3), 350–357. https://doi.org/10.1017/S026646230808046X
   PubMed Web of Science ®Google Scholar
• Günal, M. M., & Pidd, M. (2010). Discrete event simulation for performance modelling in health care: A review of the literature. Journal of Simulation, 4(1), 42–51. https://doi.org/10.1057/jos.2009.25
   Web of Science ®Google Scholar
• Hanea, A.M., Nane, G.F., Bedford, T. and French, S. (Eds.) (2021). Expert Judgement in Risk and Decision Analysis. Springer International Series in Operations Research and Management Science (293). Springer.
   Google Scholar
• Hoel, P. G. (1954). Introduction to mathematical statistics. Wiley.
   Google Scholar
• Howick, S., & Ackermann, F. (2011). Mixing OR methods in practice: Past, present and future directions. European Journal of Operational Research, 215(3), 503–511. https://doi.org/10.1016/j.ejor.2011.03.013
   Web of Science ®Google Scholar
• Jose, V. R. R., Nau, R. F., & Winkler, R. L. (2008). Scoring rules, generalized entropy, and utility maximization. Operations Research, 56(5), 1146–1157. https://doi.org/10.1287/opre.1070.0498
   Web of Science ®Google Scholar
• Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. Heuristics and Biases: The Psychology of Intuitive Judgment, 49, 49–81.
   Google Scholar
• Kandemir, C., Reynolds, C., Tom, Q., Fisher, K., Devine, R., Herszenhorn, E., Koh, S. L., & Evans, D. (2020). Using discrete event simulation to explore food wasted in the home. Journal of Simulation, 1–21. https://doi.org/10.1080/17477778.2020.1829515
   Web of Science ®Google Scholar
• Karnon, J., Stahl, J., Brennan, A., Caro, J. J., Mar, J., & Moller, J. (2012). Modeling using discrete event simulation: A report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Medical Decision Making: An International Journal of the Society for Medical Decision Making, 32(5), 701–711. https://doi.org/10.1177/0272989X12455462
   PubMed Web of Science ®Google Scholar
• Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79–86. https://doi.org/10.1214/aoms/1177729694
   Google Scholar
• Lee, D., & Lee, J. (2020). Testing on the Move South Korea’s rapid response to the COVID-19 pandemic. Transportation Research Interdisciplinary Perspectives, 5, 100111–100111. https://doi.org/10.1016/j.trip.2020.100111
   PubMedGoogle Scholar
• Lindley, D. V. (1982). Scoring rules and the inevitability of probability. International Statistical Review / Revue Internationale de Statistique, 50(1), 1–11. https://doi.org/10.2307/1402448
   Web of Science ®Google Scholar
• Mallapaty, S. (2020). The mathematical strategy that could transform coronavirus testing. Nature, 583(7817), 504–505. https://doi.org/10.1038/d41586-020-02053-6
   PubMed Web of Science ®Google Scholar
• Nikolopoulos, K., Tsinopoulos, C., & Vasilakis, C. (2021). Operational research in the time of COVID-19: The ‘science for better’ or worse in the absence of hard data. Journal of the Operational Research Society, 1–2. https://doi.org/10.1080/01605682.2021.1930208
   Web of Science ®Google Scholar
• Oakley, J. E., & O’Hagan, A. (2007). Uncertainty in prior elicitations: A nonparametric approach. Biometrika, 94(2), 427–441. https://doi.org/10.1093/biomet/asm031
   Web of Science ®Google Scholar
• O’Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E., & Rakow, T. (2006). Uncertain judgements: Eliciting experts’ probabilities. Wiley and Sons.
   Google Scholar
• Pilgrim, H., Tappenden, P., Chilcott, J., Bending, M., Trueman, P., Shorthouse, A., & Tappenden, J. (2009). The costs and benefits of bowel cancer service developments using discrete event simulation. Journal of the Operational Research Society, 60(10), 1305–1314. https://doi.org/10.1057/jors.2008.109
   Web of Science ®Google Scholar
• Quigley, J., Colson, A., Aspinall, W., & Cooke, R. M. (2018). Elicitation in the classical model. In L. Dias, J. Quigley, A. Morton (Eds), Elicitation (pp. 15–36). Springer International Series in Operations Research and Management Science.
   Google Scholar
• Renn, O., Burns, W. J., Kasperson, J. X., Kasperson, R. E., & Slovic, P. (1992). The social amplification of risk: Theoretical foundations and empirical applications. Journal of Social Issues, 48(4), 137–160. https://doi.org/10.1111/j.1540-4560.1992.tb01949.x
   Web of Science ®Google Scholar
• Robinson, S. (2005). Automated Analysis of Simulation Output Data. In 2005 Winter Simulation Conference Proceedings (pp. 763–770). IEEE.
   Google Scholar
• Robinson, S. (2014). Simulation: The practice of model development and use. Palgrave MacMillan.
   Google Scholar
• Robinson, S., Brooks, R. J., Kotiadis, K., & Van Der Zee, D. J. (2011). Conceptual Modeling for discrete-event simulation. Taylor and Francis.
   Google Scholar
• Scottish Government. (2021). Coronavirus (COVID-19) update: First Minister’s statement – 21 December 2021. Coronavirus in Scotland, Public Safety and Emergencies.
   Google Scholar
• Sepulveda, J. A., Thompson, W. J., Baesler, F. F., Alvarez, M. I., Cahoon, L. E. (1999). The use of simulation for process improvement in a cancer treatment center. In 1999 Winter Simulation Conference Proceedings ‘Simulation-A Bridge to the Future’ (Vol. 2, pp. 1541–1548).
   Google Scholar
• Simul8 Software, www.Simul8.com
   Google Scholar
• Sohrabi, C., Alsafi, Z., O'Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosifidis, C., & Agha, R. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery (London, England), 76, 71–76. https://doi.org/10.1016/j.ijsu.2020.02.034
   PubMed Web of Science ®Google Scholar
• Stahl, J. E., Furie, K. L., Gleason, S., & Gazelle, G. S. (2003). Stroke: Effect of implementing an evaluation and treatment protocol compliant with NINDS recommendations. Radiology, 228(3), 659–668. https://doi.org/10.1148/radiol.2283021557
   PubMed Web of Science ®Google Scholar
• Tako, A. A. (2015). Exploring the model development process in discrete-event simulation: Insights from six expert modellers. Journal of the Operational Research Society, 66(5), 747–760. https://doi.org/10.1057/jors.2014.52
   Web of Science ®Google Scholar
• Tako, A. A., & Kotiadis, K. (2015). PartiSim: A multi-methodology framework to support facilitated simulation modelling in healthcare. European Journal of Operational Research, 244(2), 555–564. https://doi.org/10.1016/j.ejor.2015.01.046
   Web of Science ®Google Scholar
• Tanne, J. H., Hayasaki, E., Zastrow, M., Pulla, P., Smith, P., & Rada, A. G. (2020). Covid-19: How doctors and healthcare systems are tackling coronavirus worldwide. British Medical Journal, 368, 1–5. https://doi.org/10.1136/bmj.m1090
   Web of Science ®Google Scholar
• Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207–232. https://doi.org/10.1016/0010-0285(73)90033-9
   Web of Science ®Google Scholar
• Werner, C., Bedford, T., & Quigley, J. (2021). Mapping conditional scenarios for knowledge structuring in (tail) dependence elicitation. Journal of the Operational Research Society, 72(4), 889–907. https://doi.org/10.1080/01605682.2019.1700767
   Web of Science ®Google Scholar
• Willis, G., Cave, S., & Kunc, M. (2018). Strategic workforce planning in healthcare: A multi-methodology approach. European Journal of Operational Research, 267(1), 250–263. https://doi.org/10.1016/j.ejor.2017.11.008
   Web of Science ®Google Scholar
• Wood, R. M. (2020). Modelling the impact of COVID-19 on elective waiting times. Journal of Simulation, 16(1), 101–109.
   Web of Science ®Google Scholar
• Wood, R. M., Pratt, A. C., Kenward, C., McWilliams, C. J., Booton, R. D., Thomas, M. J., Bourdeaux, C. P., & Vasilakis, C. (2021). The value of triage during periods of intense COVID-19 demand: Simulation modeling study. Medical Decision Making: An International Journal of the Society for Medical Decision Making, 41(4), 393–407. https://doi.org/10.1177/0272989X21994035
   PubMed Web of Science ®Google Scholar
• World Health Organization. (WHO). 2020. Director-General’s opening remarks at the media briefing on COVID-19-11 March (2020). Available from https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19—11-march-2020.
   Google Scholar
• Wright, G., & Cairns, G. (2011). Scenario thinking: Practical approaches to the future. Palgrave Macmillan.
   Google Scholar
• Wright, G., & Goodwin, P. (2009). Decision making and planning under low levels of predictability: Enhancing the scenario method. International Journal of Forecasting, 25(4), 813–825. https://doi.org/10.1016/j.ijforecast.2009.05.019
   Web of Science ®Google Scholar
• Wu, L., & AbouRizk, S. (2021). Numerical-based approach for updating simulation input in real time. Journal of Computing in Civil Engineering, 35(2), 04020067. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000948
   Web of Science ®Google Scholar
• Zhang, H., Tam, C. M., & Li, H. (2005). Modeling uncertain activity duration by fuzzy number and discrete-event simulation. European Journal of Operational Research, 164(3), 715–729. https://doi.org/10.1016/j.ejor.2004.01.035
   Web of Science ®Google Scholar