Study population and setting
This study combined a model of within-host viral growth and decay with transmission models to estimate the potential impact of analytical test sensitivity (limit of detection of 1,000 vs. 100,000 RNA copies), frequency (every 1, 3, 7 or 14 days) and turnaround time (0, 1 or 2 days) on the effectiveness of a population-wide surveillance program. Individuals were assumed to self-isolate either upon receiving a positive test or upon the start of symptoms, until they were no longer infectious. Transmission simulations were conducted using two different models, with different levels of complexity, including an agent-based model with age-specific and household patterns of potentially infectious contacts based on data from New York City; and a university-like setting, where individuals mix at random and infections are constantly introduced from an outside source.
Summary of Main Findings
Testing daily or every three days with effective isolation after positive test results both reduced the effective reproduction number below one regardless of the test sensitivity, and as long as results were returned in fewer than two days. For weekly testing, if results were returned on the same day, a higher sensitivity test led to less than half the cases than when a low sensitivity test was used; however, even slight delays in reporting test results reduced this margin between the tests significantly. Fortnightly testing was ineffective in all cases. Results were similar across the two different models similar to the city and university settings.
The study simulated a range of viral load trajectories in infected individuals, so it could explicitly relate the limit of detection to whether a case would be detected. Results were replicated across two different models and were robust to a range of assumptions about the relationship between viral load and infectiousness, and rate of imported infections.
The study assumed that the whole population took part in the surveillance program, there was 100% compliance in self-isolation, and that tests detected all cases where the viral load was above the limit of detection. Although the supplementary material provides methods to adjust for false negatives and test refusal (which is mathematically the same as only testing a proportion of the population), numerical results were not shown. The study also assumed that a self-isolating individual could not transmit at all; however, in some settings (e.g., within households), individuals may not be able to self-isolate perfectly. The study did not consider self-isolation of contacts of confirmed cases; including this would increase effectiveness of all the surveillance programs, but the relative increase in effectiveness for each program is unknown. The maximum delay considered, 3 days, is still shorter than that currently available in many places. Although the city setting considered different types of contacts, superspreading events due to an individual having a much-greater-than-average number of contacts was not considered.
Workplaces, schools and universities are planning for reopening, and tests with different performance characteristics, cost and turnaround time are becoming available. This study helps designers of surveillance programs to understand the tradeoff between test frequency, sensitivity and turnaround time in reducing the number of cases. The number of cases with and without surveillance for different model assumptions can be calculated using an interactive web-application (https://larremorelab.github.io/covid-calculator3).
This review was posted on: 21 July 2020