A model to analyze rideshare data to surveil novel strains of SARS-CoV-2

Safranek, C. W.; Scheinker, D.

Background: The emergence of novel, potentially vaccine-resistant strains of SARS-CoV-2 poses a serious risk to public health. The interactions between passengers and drivers facilitated by rideshare platforms such as Uber are, essentially, a series of partially standardized, random experiments of SARS-CoV-2 transmission. Rideshare companies share data with government health agencies, but no statistical method is available to aggregate these data for the systematic study of the transmission dynamics of COVID-19. Methods: We develop a proof-of-concept model for the analysis of data from rideshare interactions merged with COVID-19 diagnosis records. Using simulated data with rideshare volumes, disease prevalence, and diagnosis rates based on a large US city, we use the model to test hypotheses about the emergence of viral strains and their transmission characteristics in the presence of non-pharmaceutical interventions and superspreaders. Findings: Data from 10 simulated trials of SARS-CoV-2 propagation within the Los Angeles rideshare network resulted in an average of 190,387.1 potentially infectious rideshare interactions. Assuming access to data on 25% of the total estimated infections (Partial Reporting), these interactions resulted in an average of 409.0 diagnosed rideshare infections given our transmission model assumptions. For each of the 10 simulated trials, analysis given Partial Reporting could consistently differentiate between a baseline strain and an emergent, more infectious viral strain, enabling hypothesis testing about transmission characteristics. Interpretation: Simulated evaluation of a novel statistical model suggests that rideshare data combined with COVID-19 diagnosis data have the potential to automate continued surveillance of emergent novel strains of SARS-CoV-2 and their transmission characteristics.