Statistical Challenges in Tracking the Evolution of SARS-CoV-2

TL;DR

This paper reviews the statistical methods used in genomic surveillance of SARS-CoV-2, discusses current challenges, and proposes new approaches for tracking emerging variants during the pandemic.

Contribution

It provides a comprehensive overview of existing models and introduces a novel method for monitoring the rise of new variants in real-time.

Findings

Current models effectively track virus spread and evolution.

Statistical challenges hinder accurate interpretation of genomic data.

A new method improves detection of emerging variants.

Abstract

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.