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Supplemental Information 5: Model results, Part 2.

Sciprofile linkPatrick Bryant, Arne Elofsson
Published: 15 September 2020
 by  PeerJ
 in PeerJ
PeerJ , Volume 8; doi:10.7717/peerj.9879/supp-5

Abstract: Background As governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change. Methods We build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R0 is estimated through the model. Findings The changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R0. Interpretation Our model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R0 cause small errors to have extensive effects on the predicted outcome.
Keywords: Mobility Patterns / Bayesian model / infection rate / Non-pharmaceutical Interventions / Mcmc Simulations / COVID-19

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