# Quantifying transmission and immunity dynamics of multiple SARS-CoV-2 variants using models and epidemic data from a highly populated area

## TL;DR

This study uses models and data to track how SARS-CoV-2 variants spread and how immunity changes over time in a large city.

## Contribution

The study introduces a method to quantify transmission and immunity dynamics of multiple SARS-CoV-2 variants using nested compartmental models.

## Key findings

- Transmission rates were lowest for the ancestral strain and highest for the Omicron variant.
- Vaccine-induced and infection-induced immunity wane at similar rates.
- Transmission varied over time, possibly influenced by host behavior or viral strain switching.

## Abstract

Identifying temporal patterns in dynamics of acute, immunizing infectious diseases informs our understanding of transmission, epidemic prediction, and disease control. However, for emerging pathogens like SARS-CoV-2, temporal dynamics remain underinformed, even though COVID-19 cases varied greatly over time. Using nested compartmental models, we quantified transmission and immune dynamics in part of Columbus, the capital city of the state of Ohio, United States (US). We parameterized models using state-reported COVID-19 case counts and wastewater-based surveillance (WWS) for SARS-CoV-2. We used the models to reconstruct transmission and the rate of waning immunity in three distinct pandemic phases from April 2020 to August 2022. On average, transmission rates were lowest for the ancestral strain and highest for the Omicron variant. Transmission did not display consistent seasonal changes but did vary through time in ways that might have been influenced by host behavior or viral strain switching. Our findings also indicate that vaccine-induced and infection-induced SARS-CoV-2 immunity wane at similar rates. Gaining a better understanding of population-level transmission and immune dynamics following the emergence of a novel pathogen can inform future public health interventions including vaccine schedules.