Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
J.A.P. acknowledges support from National Institutes of Health Grant R01-GM-131404.
We thank the Editor, the guest editors, and the anonymous reviewer for constructive comments. Computing resources were provided by Stanford University research computing center (Sherlock cluster).
Lorenzo Cappello. Jaehee Kim. Sifan Liu. Julia A. Palacios. "Statistical Challenges in Tracking the Evolution of SARS-CoV-2." Statist. Sci. 37 (2) 162 - 182, May 2022. https://doi.org/10.1214/22-STS853