Identifying optimal COVID-19 testing strategies for schools and businesses: Balancing testing frequency, individual test technology, and cost

Abstract

Background COVID-19 test sensitivity and specificity have been widely examined and discussed, yet optimal use of these tests will depend on the goals of testing, the population or setting, and the anticipated underlying disease prevalence. We model various combinations of key variables to identify and compare a range of effective and practical surveillance strategies for schools and businesses. Methods We coupled a simulated data set incorporating actual community prevalence and test performance characteristics to a susceptible, infectious, removed (SIR) compartmental model, modeling the impact of base and tunable variables including test sensitivity, testing frequency, results lag, sample pooling, disease prevalence, externally-acquired infections, symptom checking, and test cost on outcomes including case reduction and false positives. Findings Increasing testing frequency was associated with a non-linear positive effect on cases averted over 100 days. While precise reductions in cumulative number of infections depended on community disease prevalence, testing every 3 days versus every 14 days (even with a lower sensitivity test) reduces the disease burden substantially. Pooling provided cost savings and made a high-frequency approach practical; one high-performing strategy, testing every 3 days, yielded per person per day costs as low as $1.32. Interpretation A range of practically viable testing strategies emerged for schools and businesses. Key characteristics of these strategies include high frequency testing with a moderate or high sensitivity test and minimal results delay. Sample pooling allowed for operational efficiency and cost savings with minimal loss of model performance.

A holistic approach for suppression of COVID-19 spread in workplaces and universities

“…Such models can present the projected impact of various testing strategies, allowing an employer to make an informed decision on the most appropriate strategy. Models that give these insights have been explored in a university setting [ 3 – 7 ] and in a healthcare setting [ 8 ] but have not been thoroughly explored across different workplace settings.…”

“…This consideration is also important in a university setting, although since college campuses are often relatively self-contained a model may choose to ignore the ongoing influence of the community. Lopman et al [ 6 ] and Lyng et al [ 7 ] capture the impact of the community by including a continuous rate of spontaneous infection in the university population. Paltiel et al [ 4 ] instead add regular exogenous ‘shocks’ of infection to the university population to simulate the impact of the community, while Gressman et al [ 5 ] include a 25% chance that one member of the university population becomes spontaneously infected each day.…”

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

“…As the statewide program, SHIELD Illinois, is currently working to increase current testing capacity to serve institutions nationally and entities in Illinois that have expressed interest in the new technology [45,46], such results can be useful for decision-makers willing to implement a similar testing procedure in their respective contexts (e.g., an organization, a city) with more or fewer samples to be processed each day. As reminded by Lyng et al (2021), the optimal use of COVID-19 tests will depend on different parameters such as the goals of testing, the population, or setting [2]. Last but not least, by following and applying the six principles of reporting simulation studies [38], the present DES model and its results can be reproduced, the model can be reused to investigate further hypotheses in the same application area or to…”

“…In accordance with the Centers for Disease Control and Prevention (CDC), proactive testing for COVID-19 infection is a key factor in determining where and how the SARS-CoV-2 virus is spreading within a population. The early identification of infected people leads to more rapid treatment and isolation for them, as well as for those who were exposed to them [1][2][3]. This type of monitoring is essential to reduce the spread of the disease (CDC, 2020).…”

Parallel Implementation of Smith-Waterman Algorithm on FPGA

“…The amount of raw data further increased the occurrence of the COVID-19 pandemic. Disease caused by the SARS-CoV-2 virus has been spreading worldwide and has been declared a pandemic by the World Health Organization [6,7].…”