Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Abstract

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

Acceptability of Community Saliva Testing in Controlling the COVID-19 Pandemic: Lessons Learned from Two Case Studies in Nursing Homes and Schools

“…These lessons are complementary to other studies that aim to identify evidence-based good practices on how to implement an operational system of Covid-19 population-based testing. 16–18 …”

Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System

“…Reporting of DES study was conducted according to the Strengthening the Reporting of Empirical Simulation Studies (STRESS-DES) guidelines [ 41 ]. Its use has been reported in other healthcare DES studies [ 33 , 40 ].…”

“…DES was selected instead of other modelling techniques such as system dynamics because the problem under study involves constrained resources [ 18 ]. The model would allow for random variation in inputs over time, as entities change in state and flow through queues and activities [ 38 , 39 , 40 ]. Entities can also be modelled at the individual level with variability in arrival and service times [ 33 ].…”

On the role of data, statistics and decisions in a pandemic

“…the model jumps from the time of one event to the time of the next) and that events are discrete (mutually exclusive) . Model applications in COVID-19 include optimizations of processes with scarce resources such as bed capacities (Melman et al, 2021) or testing stations (Saidani et al, 2021) and laboratory processes (Gralla, 2020).…”