Optimizing COVID-19 control with asymptomatic surveillance testing in a university environment

Abstract

The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities are developing asymptomatic surveillance testing labs, to augment existing NPIs and control outbreaks on campus. We built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. We find that behavioral interventions offer a cost-effective means of epidemic control: group size limits of twelve or fewer greatly reduce superspreading, and rapid isolation of symptomatic infections can halt rising epidemics, depending on the frequency of asymptomatic transmission in the population. Surveillance testing can overcome uncertainty surrounding asymptomatic infections, with the most effective approaches prioritizing frequent testing with rapid turnaround time to isolation over test sensitivity. Importantly, contact tracing amplifies population-level impacts of all infection isolations, making even delayed interventions effective. Combination of behavior-based NPIs and asymptomatic surveillance also reduces variation in daily case counts to produce more predictable epidemics. Furthermore, targeted, intensive testing of a minority of high transmission risk individuals can effectively control the COVID-19 epidemic for the surrounding population. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies for the 2021 year.Significance StatementWe built a COVID-19 dynamical model to advise strategies for optimal epidemic control in a university environment. Unique from previous work, our model combines behavior-based non-pharmaceutical interventions with asymptomatic surveillance; we find that a multi-factorial intervention approach uniting group size limits to deter superspreading, rapid symptom-based isolation, and contact tracing, with asymptomatic surveillance that prioritizes test frequency and turnaround time over test sensitivity, offers the most effective COVID-19 control. Contact tracing can amplify intervention gains from even extensively delayed isolations, and targeted, intensive testing of a high transmission risk minority can control epidemics for the surrounding community. We provide reopening recommendations and an easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies in the upcoming year.

Quantification of the tradeoff between test sensitivity and test frequency in COVID-19 epidemic - a multi-scale modeling approach

“…Our findings support the conclusions of Paltiel, et al [32], who found that frequent (every 2 days), low-sensitivity testing might be necessary in order to allow for college reopening. Moreover, several modeling studies have found that diagnostic testing of symptomatic patients alone is insufficient for outbreak control, and must be supplemented by randomized surveillance testing of the asymptomatic population [8, 11, 22, 32]. Indeed, under randomized and uniformly distributed surveillance testing of the entire non-isolated population, we find that frequent testing of the entire population can flatten the daily incidence curve and significantly decrease the cumulative size of the outbreak.…”

Quantification of the Tradeoff between Test Sensitivity and Test Frequency in a COVID-19 Epidemic—A Multi-Scale Modeling Approach

“…Our findings support the conclusions of Paltiel, et al [ 6 ], who found that frequent (every 2 days), low-sensitivity testing might be necessary in order to allow for college reopening. Moreover, several modeling studies have found that diagnostic testing of symptomatic patients alone is insufficient for outbreak control, and must be supplemented by randomized surveillance testing of the asymptomatic population [ 6 , 25 , 42 , 43 ]. Indeed, under randomized and uniformly distributed surveillance testing of the entire non-isolated population, we find that frequent testing of the entire population can flatten the daily incidence curve and significantly decrease the cumulative size of the outbreak.…”

The impact of varying class sizes on epidemic spread in a university population

“…As we have stated, we have relatively modest ambitions in this study of exploring how the size of mixing groups (considered as classes here) impact the time course of epidemics in partitioned populations. Other studies have provided highly detailed analyses of models with many realistic assumptions, contact networks and NPIs included (Ferguson et al, 2020; Kucharski et al, 2020; Firth et al, 2020; Kain et al, 2021) including in university settings (Bahl et al, 2020; Brook et al, 2020; Cashore et al, 2020; Lopman et al, 2020). If our study were to be applied to more realistic scenarios or to form the basis of decision making, some key further additions would be necessary.…”

“…As countries move to reopen these settings, an important question is how classes can be organised to minimise further disruption to students’ education whilst limiting epidemic spread. There have been some excellent, in-depth modelling studies of infection spread in educational settings, especially universities, with a range of NPIs included, often with a focus on testing and isolation strategies (Bahl et al, 2020; Brook et al, 2020; Cashore et al, 2020; Lopman et al, 2020). Here we focus on the question of how class sizes may impact an epidemic.…”