ArticlePloS one2021
Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Within-laboratory SARS-CoV-2 real time PCR testing operations in Nepal: a simulation-based analysis.The Lancet regional health. Southeast Asia · 2025Article
- Assessment of Rapid Antigen Diagnostic Tests at Mass Events: Identifying Optimal Floor Plan Configurations for Enhanced Efficiency.Healthcare (Basel, Switzerland) · 2024Article
- A proactive/reactive mass screening approach with uncertain symptomatic cases.PLoS computational biology · 2024Article
- Large-scale emergency medical services scheduling during the outbreak of epidemics.Annals of operations research · 2023Article
- Using simulation modelling and systems science to help contain COVID-19: A systematic review.Systems research and behavioral science · 2022Article
- Heterogeneity in testing for infectious diseases.Royal Society open science · 2022Article
- Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System.Healthcare (Basel, Switzerland) · 2022Article
- On an optimal testing strategy for workplace settings operating during the COVID-19 pandemic.PloS one · 2022Article
- On the role of data, statistics and decisions in a pandemic.Advances in statistical analysis : AStA : a journal of the German Statistical Society · 2022Article
- Acceptability of Community Saliva Testing in Controlling the COVID-19 Pandemic: Lessons Learned from Two Case Studies in Nursing Homes and Schools.Patient preference and adherence · 2022Article
- From disgusting and complicated to simple and brilliant: Implementation perspectives and lessons learned from users and rejectors of mail-in SARS-CoV-2 gargle tests.Frontiers in public health · 2022Article
- Circular economy as a key for industrial value chain resilience in a post-COVID world: what do future engineers think?Procedia CIRP · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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