Bayesian spatial modelling of localised SARS-CoV-2 transmission through mobility networks across England

Abstract

In the early phases of growth, resurgent epidemic waves of SARS-CoV-2 incidence have been characterised by localised outbreaks. Therefore, understanding the geographic dispersion of emerging variants at the start of an outbreak is key for situational public health awareness. Using telecoms data, we derived mobility networks describing the movement patterns between local authorities in England, which we have used to inform the spatial structure of a Bayesian BYM2 model. Surge testing interventions can result in spatio-temporal sampling bias, and we account for this by extending the BYM2 model to include a random effect for each timepoint in a given area. Simulated-scenario modelling and real-world analyses of each variant that became dominant in England were conducted using our BYM2 model at local authority level in England. Simulated datasets were created using a stochastic metapopulation model, with the transmission rates between different areas parameterised using telecoms mobility data. Different scenarios were constructed to reproduce real-world spatial dispersion patterns that could prove challenging to inference, and we used these scenarios to understand the performance characteristics of the BYM2 model. The model performed better than unadjusted test positivity in all the simulation-scenarios, and in particular when sample sizes were small, or data was missing for geographical areas. Through the analyses of emerging variant transmission across England, we found a reduction in the early growth phase geographic clustering of later dominant variants as England became more interconnected from early 2022 and public health interventions were reduced. We have also shown the recent increased geographic spread and dominance of variants with similar mutations in the receptor binding domain, which may be indicative of convergent evolution of SARS-CoV-2 variants.

Fig 1. The mobility network overlaid on a hex map of England, where the edge weight describes the rate of journeys between different LTLA’s, normalised to take values in [0,1].

Created from geographical files using the House of Commons Library, which is under the Open Parliament License v3.0.

Fig 2. A clustered bar chart of the reduction in the average L1 error, for each scenario, variation, and model.

Fig 3. Gene target and sequencing data coverage over time for RT-PCR positive tests in England.

Fig 4. The BYM2 estimated model positivity of the Alpha variant as a proportion of sequenced tests from 12th November 2020 to 21st March 2022.

Created from geographical files using the House of Commons Library, which is under the Open Parliament License v3.0.

Fig 5. The BYM2 estimated model positivity of the Delta variant as a proportion of sequenced tests from 28th April 2021 to 1st July 2021.

Created from geographical files using the House of Commons Library, which is under the Open Parliament License v3.0.

Fig 6. The BYM2 estimated model positivity of the Omicron BA.1 variant as a proportion of sequenced tests from 11th October 2021 to 15th January 2022.

Created from geographical files using the House of Commons Library, which is under the Open Parliament License v3.0.

Fig 7. The BYM2 estimated model positivity of the Omicron BA.2 variant as a proportion of sequenced tests from 20th December 2021 to 2nd May 2022.

Created from geographical files using the House of Commons Library, which is under the Open Parliament License v3.0.

Fig 8. The BYM2 estimated model positivity of the Omicron BA.5 variant as a proportion of sequenced tests from 31st May 2022 to 4th September 2022.

Created from geographical files using the House of Commons Library, which is under the Open Parliament License v3.0.