Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions

Abstract

Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%-53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARS-CoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions.

Fig 1. High-level overview of the common structure of both models used in this study.

Dark nodes are observed, light nodes are inferred. See S2 Appendix for a detailed model graph for each individual model

Fig 2. The inferred seasonal R multiplier Γ(t) of the combined models estimate, with 50% and 95% credible intervals.

Gray boxes indicate data range of each dataset, i.e. 22nd January to 30th May 2020 for Brauner et al. and 1st August 2020 to 9th January 2021 for Sharma et al. The zero-width credible intervals around April 1 and October 1 owe to the fact that we model Γ(t) as a seasonal multiplier for R relative to the mid-spring and mid-fall, respectively, which implies that Γ(t) is assumed to be exactly equal to one for these dates.

Fig 3. Posterior distributions of the R reduction on July 1 relative to January 1 with median, 50% and 95% credible intervals.

Fig 4. Comparison of the inferred peak-to-trough R reduction effect of seasonality (combined from both models) to the NPI reductions inferred by Brauner et al. [23], with 50% and 95% CIs.

The seasonal effect is lower than the combined NPI effect but higher than or comparable to the individual NPI effects.