COVID-19: Early evening curfews and mobility

Velias, Alina; Georganas, Sotiris; Vandoros, Sotiris

doi:10.1016/j.socscimed.2021.114538

Cited by 18 publications

(8 citation statements)

References 20 publications

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“…They do not seem to increase incidences. The latter result seems to be important in connection with recent research based on experimental data 16 and mobility data 17 , respectively, which show that night curfews might fuel disease dynamics.…”

Section: Discussionsupporting

confidence: 59%

Measuring the effect of COVID-19-related night curfews in a bundled intervention within Germany

Haas

Götz

Heim

2022

Sci Rep

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We estimate the impact of local night curfews in Hesse, the fifth most populous federal state in Germany, on the growth of incidences of COVID-19 cases residing within the “second wave” of the pandemic. Thereby, we take advantage of the fact that all counties had the same measures in place with the only difference that some additionally had to implement night curfews due to state regulations. This allows us to identify the effect of night curfews as a salient part of a bundled intervention. In our case where different other measures are already in place, night curfews had at best a limited effect in slowing down the spread of the pandemic. The effect is not significantly different from zero.

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Section: Discussionsupporting

confidence: 59%

Measuring the effect of COVID-19-related night curfews in a bundled intervention within Germany

Haas

Götz

Heim

2022

Sci Rep

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show abstract

“…For instance, Ghasemi et al [22] provide evidence that a nighttime curfew in Ontario/Canada substantially reduced nighttime mobility. Similarly, Velias et al [33] show that a temporal extension of a nighttime curfew in the Attica region of Greece led to a small decrease in mobility. These are important insights, but mobility data are not a direct measure of the pandemic situation and reductions in mobility do not necessarily translate into fewer Covid infections.…”

Section: Introductionmentioning

confidence: 84%

The effect of a nighttime curfew on the spread of COVID-19

2023

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“…When mobility trends are low and stringency index is high, the probability of an increasing number of cases is significantly lower. Focusing on positive trends, SHAP values have an increasing slope and color of dots gradually becomes blue, which suggests that increasing mobility trends from grocery and pharmacy places increases the probability of new cases [52,53].…”

Section: Feature Understanding and Feature Explanationmentioning

confidence: 99%

“…From −20% to 20% SHAP values start to increase, while dots begin to have blue color, describing instances from places with lower stringency index. The function distribution suggests that reducing movements to places such as restaurants, cafes, shopping centers, etc., with tight government responses reduce the probability of reporting high new daily cases [52,53].…”

Section: Feature Understanding and Feature Explanationmentioning

confidence: 99%

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

et al. 2022

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The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model’s predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations.

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COVID-19: Early evening curfews and mobility

Cited by 18 publications

References 20 publications

Measuring the effect of COVID-19-related night curfews in a bundled intervention within Germany

Measuring the effect of COVID-19-related night curfews in a bundled intervention within Germany

The effect of a nighttime curfew on the spread of COVID-19

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

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