Drivers of COVID-19 variant wave dynamics: inferring oncoming wave size using global data with genomics

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Abstract
The continued evolution of the SARS-CoV-2 virus drove waves of infection worldwide throughout the pandemic. These evolutionary dynamics posed significant challenges for public health forecasting and, specifically, for predicting the size of COVID-19 waves. In this work we leverage a range of global public data, with a focus on features derived from pathogen genomic sequences, to model and predict the relative size of COVID-19 waves (as compared to the previous wave) across countries. Focusing on Omicron BA.1 and BA.2, we develop statistical models to assess the predictive power of these data in forecasting future variant-driven wave peaks. We find that, while forecasting wave size is a challenging task, variables such as genomic variant characteristics, prior wave dynamics, and demographic features e.g. life expectancy were informative, whereas seasonality was not. Our results show that the importance of features changed markedly between Omicron waves, reflecting the evolving epidemiological and genomic landscape. This work provides insights into improving predictive models for future outbreaks and pandemics, and prioritizing data collection efforts to enhance forecasting accuracy.

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