Speaker
Description
A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. The effectiveness of these programs depends not only on which test you use, but also how you use it. Here, we introduce testing effectiveness, TE—the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale—and a model that estimates TE by incorporating within-host pathogen dynamics, test specifications and usage, and human behavior. This model places various intuitions about disease mitigation via testing on firm quantitative ground, and exposes important testing-associated variables and behaviors to in-silico experimentation and optimization.
Using TE to guide recommendations, we show that rapid diagnostic tests should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus (RSV), but delayed by up to 2 days to control omicron-era SARS-CoV-2. Furthermore, while rapid tests are superior to RT-qPCR for control of founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer test-to-answer turnaround times. These results demonstrate the importance of incorporating within-host pathogen kinetics to predict the impact of between-host transmission interventions.
The observations of differing TE’s for different pathogens under the same testing scenarios raise an intriguing possibility: widespread use of diagnostic tests may lead to differential selection pressures for one pathogen or variant over others. New diagnostics such as multiplexed RSV/Flu/SARS-CoV-2 tests target multiple pathogens, while single-target diagnostics target multiple strains of the same pathogen. The shift in selection coefficients due to the differing transmission-reducing power of testing can be estimated as a function of TE. Thus, the model presented here estimates not only the impact of testing on transmission, but also the heterogeneous impact of testing on multi-pathogen and variant selection landscapes.
