Controlling COVID-19 via test-trace-quarantine

Abstract

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We performed this analysis using Covasim, an open-source agent-based model, which was calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we found that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.

Low case numbers enable long-term stable pandemic control without lockdowns

“…Given our informed assumptions about these parameters, our model shows that TTI can only compensate a basic reproduction number R 0 of 3.3 -if contagious contacts are also reduced to at most 61 % (95% CI: [47,76]) compared to pre-COVID. This is in agreement with the results of other studies (37,38,40,43,44).…”

“…Quantitatively, our assumptions regarding the efficiency of TTI are in agreement with those of other modeling studies. Agent-based models with detailed contact structures (37,38) and mean-field models (39)(40)(41)(42) both agree that TTI measures are an essential contribution for the control of the pandemic but typically do not suffice alone. Their success strongly depends on their implementation: Fast testing, rigorous isolation, and a large proportion of traced contacts are essential.…”

“…1G). However, if case numbers exceed the TTI capacity limit, a considerably stronger reduction in contacts is required to reach the stable regime, so that k crit t = 42 % (95% CI: [38,47]) (Fig. 2 B, Fig.…”

Risk of sustained SARS-CoV-2 transmission in Queensland, Australia

“…These increases can be mitigated by a sustained increase in testing and subsequent Q/I, but the required increase in testing (10-fold) far exceeds the maximum number of daily tests performed in Queensland since the beginning of the pandemic (∼ 35,000 tests per day). Nevertheless, our results support a strong role for Q/I in reducing the spread of COVID-19 in ultra-low transmission environments [20]. This is further supported by the Australian experience in which, other than the 2020 Victorian second wave, outbreaks of COVID-19 have been rare, small, and effectively contained.…”

Covasim: an agent-based model of COVID-19 dynamics and interventions

“…Overall, the design principle we followed with Covasim was to make common usage patterns as simple as possible, while still giving the user the ability to customize virtually all aspects of the simulation. For example, Covasim comes pre-loaded with demographic data for each country (Section 2.4), but users can also define custom populations and contact networks down to the level of a single city (25) or even university (26). In addition, Covasim includes six built-in interventions (Section 2.5), but custom interventions of arbitrary complexity can also be defined (Fig.…”