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

Abstract

During the early waves of COVID-19, a frequent modelling challenge for OR practitioners was the lack of relevant historical data on important model inputs, such as uncertainty distributions. This is common and often critical in OR projects that support decision-making during new crises, in the aftermath of disasters, and generally, when modelling systems that are new or currently do not exist. Guidance for OR practitioners on systematic approaches for this modelling challenge is generally limited, and, in particular, omits important aspects of OR projects that support decisions during crises. These include, e.g., requiring urgent decision support, possible extreme parameter values, only a few experts being available. This paper proposes a novel iterative multi-method framework to address this gap in the literature. Our framework combines uncertainty elicitation, mathematical scoring rules and problem structuring with Discrete Event Simulation, with a focus on crisis decision-making. It provides optimised and transparent weighted combinations for simulation parameters. Our case study is motivated by our experience of simulation modelling during the early COVID-19 pandemic. We faced this modelling challenge for arrival number uncertainties when building a simulation of “no appointment necessary” COVID-19 walk-through testing to provide decision-makers with a better understanding of long waiting time risks.

Keywords:

• COVID-19 testing
• discrete event simulation
• mathematical scoring rules
• uncertainty quantification
• problem structuring

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1 It is caused by Severe Acute Respiratory Syndrome Coronavirus 2, its medical terms are SARS-CoV-2 for the virus and COVID-19 for the disease. We use the latter throughout this paper.

2 A simplified and freely downloadable version, including elements of drive-through testing, has been available since March 2020 as one of the first simulations for mass testing in this pandemic from: https://blog.simul8.com/covid-19-drive-through-testing-simulation-tutorial/

3 Other background distributions are possible and for wide ranges a log-uniform distribution is more appropriate.

4 Obtained by uniformly distributing the mass per interval, i.e., interval mass divided by its length, to not add unspecified assumptions to the assessments of the experts.

5 Assuming that within each timeslot, the arrival rates are (roughly) constant while arrivals are independent, so that the exponential distribution is a suitable approximate model for comparison.