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

Abstract

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the “Community-Workplace” model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and social distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue.Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

Uptake of SARS-CoV-2 workplace testing programs, March 2020 to March 2021

“…Testing programs are most helpful in certain working conditions 3 . For example, settings where employees work indoors without distancing for extended periods of time may particularly benefit from testing given the airborne nature of SARS-CoV-2 transmission 10 .…”

“…Modeling 3 and pilot studies 4,5 have shown that SARS-CoV-2 diagnostic testing programs are effective and feasible tools for limiting workplace transmission. Data on whether and how employers are testing their employees could provide important context for occupational health experts and public health officials as they formulate guidance that shapes employer precautions.…”

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

“…However, overly simplistic models can yield erroneous conclusions regarding real-world control strategies, so one must carefully balance model simplicity against the complex realistic elements most relevant to the problem at hand. Conventional compartmental COVID-19 control models are typically based on systems of ordinary differential equations (ODE's) (16,17,18,19,20,21,22,23,24,25). While ODE disease models provide a level of mathematical tractability, they necessitate the coupling of symptom status to specific model compartments, and this structural constraint can result in unnatural or unrealistic representations of symptom onset and presymtomatic transmission with potential unintended consequences on model behavior and real-world interpretations.…”

“…Although useful as simple baseline examples, these models may overestimate the efficacy of symptom-based COVID-19 controls due to the absence of presymptomatic transmission. A second more complicated class of ODE models includes a presymptomatic compartment where individuals are infectious but not yet symptomatic before transitioning to an infectious symptomatic compartment (22,23,24,25). This approach, while more realistic, presents challenges when interpreting results for real world decision making via comparisons to the simpler class of models.…”

Higher education responses to COVID-19 in the United States: Evidence for the impacts of university policy

“…Because of this, the COVID-19 pandemic presented a particular challenge for IHEs during the Fall 2020 semester [2][3][4][5][6][7]. On the one hand, bringing students back for on-campus and in-person education introduced the risk that an IHE would contribute to or exacerbate large regional outbreaks [8][9][10][11][12][13][14][15][16]; on the other hand, postponing students' return to campus may bring economic or social hardship to the communities in which the IHEs are embedded [17][18][19][20], since IHEs are often large sources of employment for counties across the United States. As a result, IHEs instituted a variety of "reopening" strategies during the Fall 2020 semester [21][22][23][24][25][26][27][28][29][30][31][32].…”