Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries.
Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries.
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作者：
Bryant P;Elofsson A
随着欧洲各国政府发布非药物干预措施（NPI），如社交距离和学校关闭，这些国家的流动模式发生了变化。大多数州在类似的时间点实施了类似的NPI。然而，不同的国家和人口对非营利机构的反应可能不同，这些差异导致流动模式发生变化，从而改变了流行病的发展。我们建立了一个贝叶斯模型，估计在给定的一天的死亡人数依赖于基本生殖数，R0的变化，由于流动模式的差异。我们使用Google移动报告中的移动数据，分为五个不同的类别：零售和娱乐，杂货和药房，中转站，工作场所和住宅。每个流动性类别的重要性，预测R0的变化估计通过模型。流动性方面的变化与政府实行的非营利机构有相当大的重叠，突出表明政府采取行动改变人口行为的重要性。所有类别的流动性变化与1个月后的死亡率高度相关。据估计，杂货店和药店部门内的流动减少是R0减少的主要原因。我们的模型预测了3周的流行预测，使用流动模式变化的实时观察，这可以为政府提供关于其NPI效果的直接反馈。该模型准确地预测了大多数国家的变化，但高估了瑞典和丹麦的非营利机构的影响，低估了法国和比利时的影响。我们还注意到，基于基本再生数R0的所有流行病学模型的指数性质会导致小的误差对预测结果产生广泛的影响。
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. 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. 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. 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.
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影响因子：
64.8
作者：
Lloyd-Smith JO;Schreiber SJ;Kopp PE;Getz WM
通讯作者：
Getz WM
DOI：
10.1016/s1473-3099(20)30243-7
发表时间：
2020-06-01
期刊：
The Lancet. Infectious diseases
影响因子：
--
作者：
Verity, Robert;Okell, Lucy C;Ferguson, Neil M
通讯作者：
Ferguson, Neil M
影响因子：
2.4
作者：
Brooks, SP;Gelman, A
通讯作者：
Gelman, A
影响因子：
3.8
作者：
Thompson, R. N.;Stockwin, J. E.;Cori, A.
通讯作者：
Cori, A.
影响因子：
64.8
作者：
Cauchemez, Simon;Valleron, Alain-Jacques;Ferguson, Neil M.
通讯作者：
Ferguson, Neil M.