Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance
Christopher J. Banks, Ewan Colman, Anthony Wood, Thomas Doherty,, Rowland R. Kao

TL;DR
This study used a spatial agent-based model combined with early surveillance data to explore plausible scenarios of Omicron's transmission and immune escape, informing early intervention strategies and highlighting the importance of prior immunity landscape.
Contribution
The paper presents a novel modeling approach integrating early surveillance data to simulate Omicron's outbreak dynamics and assess intervention impacts.
Findings
Model accurately captured initial Omicron outbreak dynamics.
Early interventions would have limited effect due to Omicron's transmission advantage.
Prior immunity landscape significantly influences outbreak outcomes.
Abstract
We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant. Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCOVID-19 epidemiological studies · SARS-CoV-2 and COVID-19 Research · Influenza Virus Research Studies
