Accuracy of Inferences About the Reproductive Number and Superspreading Potential of SARS-CoV-2 with Incomplete Contact Tracing Data

Henry Bayly; Winnie Mei; Debra Egeren; Madison Stoddard; Arijit Chakravarty; Laura F White

doi:10.21203/rs.3.rs-3760127/v1

Accuracy of Inferences About the Reproductive Number and Superspreading Potential of SARS-CoV-2 with Incomplete Contact Tracing Data

Res Sq [Preprint]. 2023 Dec 29:rs.3.rs-3760127. doi: 10.21203/rs.3.rs-3760127/v1.

Authors

Henry Bayly¹, Winnie Mei², Debra Egeren³, Madison Stoddard⁴, Arijit Chakravarty⁴, Laura F White¹

Affiliations

¹ Boston University School of Public Health.
² University of Washington School of Public Health.
³ Stanford University.
⁴ Fractal Therapeutics.

Abstract

The basic reproductive number (R₀) and superspreading potential $(k)$ are key epidemiological parameters that inform our understanding of a disease's transmission. Often these values are estimated using the data obtained from contact tracing studies. Here we performed a simulation study to understand how incomplete data due to preferential contact tracing impacted the accuracy and inferences about the transmission of SARS-CoV-2. Our results indicate that as the number of positive contacts traced decreases, our estimates of R₀ tend to decrease and our estimates of ktend to increase. Notably, when there are large amounts of positive contacts missed in the tracing process, we can conclude that there is no indication of superspreading even if we know there is. The results of this study highlight the need for a unified public health response to transmissible diseases.

Publication types

Preprint