An Innovative Non-Pharmaceutical Intervention to Mitigate SARS-CoV02 Spread: Probability Sampling to Identify and Isolate Asymptomatic Cases

Abstract

AbstractStudies estimate that a substantial proportion of SARS-CoV-2 transmission occurs through individuals who do not exhibit symptoms. Mitigation strategies test only those who are moderately to severely symptomatic, excluding the substantial portion of cases that are asymptomatic yet still infectious and likely responsible for a large proportion of the virus’ spread (1-8). While isolating asymptomatic cases will be necessary to effectively control viral spread, these cases are functionally invisible and there is no current method to identify them for isolation. To address this major omission in COVID-19 control, we develop a strategy, Sampling-Testing-Quarantine (STQ), for identifying and isolating individuals with asymptomatic SARS-CoV-2 in order to mitigate the epidemic. STQ uses probability sampling in the general population, regardless of symptoms, then isolates the individuals who test positive along with their household members who are high probability for asymptomatic infections. To test the potential efficacy of STQ, we use an agent-based model, designed to computationally simulate the epidemic in the Seattle with infection parameters, like R0 and asymptomatic fraction, derived from population data. Our results suggest that STQ can substantially slow and decrease the spread of COVID-19, even in the absence of school and work shutdowns. Results also recommend which sampling techniques, frequency of implementation, and population subject to isolation are most efficient in reducing spread with limited numbers of tests.Significance StatementA substantial portion of SARS-CoV-2 infections are spread through asymptomatic carriers. Until a vaccine is developed, research indicates an urgent need to identify these asymptomatic infections to control COVID-19, but there is currently no effective strategy to do so. In this study, we develop such a strategy, a procedure called Sampling-Testing-Quarantine (STQ), that combines techniques from survey methods for sampling from the general population and testing and isolation techniques from epidemiology. With computational simulations, we demonstrate that STQ procedures can dramatically decrease and slow COVID-19 spread, even in the absence of widespread work, school, and community lockdowns. We also find particular implementation strategies (including sampling techniques, frequencies of implementation, and people who are subject to isolation) are most efficient in mitigating spread.