Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries
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Abstract
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, R 0 , 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 R 0 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 R 0 .
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, R 0 cause small errors to have extensive effects on the predicted outcome.
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SciScore for 10.1101/2020.04.13.20063644: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:It should be noted here that one limitation of our model is that it does not take herd-immunity effects into account, which should be reached when around 60-80 % of the population is infected 20, but it is unlikely that sufficiently high infection has been reached yet for this to have a significant effect. Another limitation of the model is the assumption that the impact of each relative mobility change has the same relative impact …
SciScore for 10.1101/2020.04.13.20063644: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:It should be noted here that one limitation of our model is that it does not take herd-immunity effects into account, which should be reached when around 60-80 % of the population is infected 20, but it is unlikely that sufficiently high infection has been reached yet for this to have a significant effect. Another limitation of the model is the assumption that the impact of each relative mobility change has the same relative impact across all countries and across time. Likely both more detailed mobility data and intermixing patterns need to be considered, metrics that are not available. The number of cases are also highly dependent on having the correct infection-fatality-rate (ifr). This quantity is only modelled for the age group 50-59 years and does thereby not take into account the attack rates for the whole of each country’s population (see methods section). If a country managed to avoid the elderly being infected, that would lower the ifr 21, which could explain prediction differences to some extent. The model validation, both by a leave-one-country-out analysis and by predicting a three week forecast, ensures the model’s robustness. The countries where the errors stand out are Denmark and Sweden, with over-predicted estimates, and Belgium and France, under-predicted. We note that these two pairs of countries are close both geographically and culturally22,23, possibly explaining the systematic differences. The differences may also be caused by differences in reporting b...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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- No protocol registration statement was detected.
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