A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2

No method is available to systematically study SARS-CoV-2 transmission dynamics using the data that rideshare companies share with government agencies. We developed a proof-of-concept method for the analysis of SARS-CoV-2 transmissions between rideshare passengers and drivers.

To assess whether this method could enable hypothesis testing about SARS-CoV-2, we repeated ten 200-day agent-based simulations of SARS-CoV-2 propagation within the Los Angeles County rideshare network. Assuming data access for 25% of infections, we estimated an epidemiologist's ability to analyze the observable infection patterns to correctly identify a baseline viral variant A, as opposed to viral variant A with mask use (50% reduction in viral particle exchange), or a more infectious viral variant B (300% higher cumulative viral load).

Simulations had an average of 190,387 potentially infectious rideshare interactions, resulting in 409 average diagnosed infections. Comparison of the number of observed and expected passenger-to-driver infections under each hypothesis demonstrated our method's ability to consistently discern large infectivity differences (viral variant A vs. viral variant B) given partial data from one large city, and to discern smaller infectivity differences (viral variant A vs. viral variant A with masks) given partial data aggregated across multiple cities.

This novel statistical method suggests that, for the present and subsequent pandemics, government-facilitated analysis of rideshare data combined with diagnosis records may augment efforts to better understand viral transmission dynamics and to measure changes in infectivity associated with nonpharmaceutical interventions and emergent viral strains.