Abstract
BackgroundAs 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.MethodsWe 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.FindingsThe 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.InterpretationOur 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.
Highlights
In December 2019 a new coronavirus (COVID-19) emerged in Wuhan, China
To limit the spread of the virus, European countries introduced non-pharmaceutical interventions (NPIs) similar to China’s. These NPIs include social distancing, school closures, limiting international travel and lockdown[3].All of these NPIs result in behavioural changes, which can be traced through mobility data from tracking the location of mobile phones
We simulate a three week forecast from 30 March to 19 April using data up to 29 March from the European Centre of Disease Control (ECDC) in the form of number of deaths per day, and relative mobility data estimated by Google[4].According to the model, most countries appear to have their epidemic under control (April 19) (Table 1)
Summary
In December 2019 a new coronavirus (COVID-19) emerged in Wuhan, China. China implemented a quick strategy of suppression by locking the Wuhan province down on January 231,and implementing social distancing procedures nationwide, with a successful outcome 2.Still, the virus rapidly spread across the world through our increasingly interconnected flight network, and shortly arrived in Europe. To limit the spread of the virus, European countries introduced non-pharmaceutical interventions (NPIs) similar to China’s. These NPIs include social distancing, school closures, limiting international travel and lockdown[3].All of these NPIs result in behavioural changes, which can be traced through mobility data from tracking the location of mobile phones. 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. It is likely different countries and populations respond differently to the same NPIs and that these differences are reflected in the epidemic development
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