scholarly journals Time-series clustering for home dwell time during COVID-19: what can we learn from it?

2020 ◽  
Author(s):  
Xiao Huang ◽  
Zhenlong Li ◽  
Junyu Lu ◽  
Sicheng Wang ◽  
Hanxue Wei ◽  
...  
Keyword(s):  
Time Series ◽  
Dwell Time ◽  
Statistical Significance ◽  
Mitigation Strategies ◽  
Time Series Clustering ◽  
Social Distancing ◽  
Socioeconomic Variables ◽  
Disadvantaged Groups ◽  
Socially Disadvantaged ◽  
At Home

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph, and further assess the statistical significance of sixteen demographic/socioeconomic variables from five major categories. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures. Policymakers need to carefully evaluate the inevitable trade-off among different groups, making sure the outcomes of their policies reflect interests of the socially disadvantaged groups.

scholarly journals Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?

2020 ◽  
Vol 9 (11) ◽  
pp. 675
Author(s):  
Xiao Huang ◽  
Zhenlong Li ◽  
Junyu Lu ◽  
Sicheng Wang ◽  
Hanxue Wei ◽  
...  
Keyword(s):  
Time Series ◽  
Dwell Time ◽  
Mitigation Strategies ◽  
Time Series Clustering ◽  
Social Distancing ◽  
Socioeconomic Variables ◽  
Fine Grained ◽  
Disadvantaged Groups ◽  
Socially Disadvantaged ◽  
At Home

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.

scholarly journals The Politics of Pandemics: The Effect of Stay-At-Home Orders on COVID-19 Mitigation

2021 ◽  
pp. 1-23
Author(s):  
Shawn Patterson
Keyword(s):  
Public Health ◽  
Party Politics ◽  
Mitigation Strategies ◽  
State Governments ◽  
Synthetic Control ◽  
Social Distancing ◽  
Control Approach ◽  
Contemporary Politics ◽  
Hospital Systems ◽  
At Home

Abstract The COVID-19 pandemic has upended every aspect of American life. State governments responded quickly to protect public health and stabilize overwhelmed hospital systems. The most restrictive policy, the stay-at-home order, was seen by public health officials as a cornerstone of successful state mitigation strategies. But like many aspects of contemporary politics, support for these efforts took on a distinctly partisan hue. In this paper, I argue that party politics significantly affected state policy responses to COVID-19, which in turn limited mitigation efforts. To this point, I first demonstrate that Democratic governors were faster and more likely to adopt stay-at-home orders than Republicans. Next, using a synthetic control approach, I show that these orders caused residents to practice greater social distancing. Finally, I find that greater social distancing worked to “flattened the curve” by limiting the growth of COVID-19 cases. Together these findings show how party politics affected state pandemic responses and have important long-term implications as states begin lifting restrictions.

scholarly journals Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

2021 ◽  
Author(s):  
Jenni A. Shearston ◽  
Micaela E. Martinez ◽  
Yanelli Nunez ◽  
Markus Hilpert
Keyword(s):  
Time Series ◽  
Real Time ◽  
Traffic Congestion ◽  
Human Mobility ◽  
Additive Models ◽  
School Closure ◽  
High Temporal Resolution ◽  
Social Distancing ◽  
The Impact ◽  
At Home

ABSTRACTIntroductionTo mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.MethodsWe used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.ResultsMobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.ConclusionUsing crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.GRAPHICAL ABSTRACT

scholarly journals Excess burden of non-communicable disease years of life lost from heat in rural Burkina Faso: a time series analysis of the years 2000–2010

BMJ Open ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. e018068 ◽  
Author(s):  
Aditi Bunker ◽  
Maquins Odhiambo Sewe ◽  
Ali Sié ◽  
Joacim Rocklöv ◽  
Rainer Sauerborn
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Communicable Disease ◽  
Statistical Significance ◽  
Heat Exposure ◽  
Extreme Temperature ◽  
Mitigation Strategies ◽  
Maximum Temperature ◽  
Years Of Life Lost ◽  
Time Series Regression

ObjectivesInvestigate the association of heat exposure on years of life lost (YLL) from non-communicable diseases (NCD) in Nouna, Burkina Faso, between 2000 and 2010.DesignDaily time series regression analysis using distributed lag non-linear models, assuming a quasi-Poisson distribution of YLL.SettingNouna Health and Demographic Surveillance System, Kossi Province, Rural Burkina Faso.Participants18 367 NCD-YLL corresponding to 790 NCD deaths recorded in the Nouna Health and Demographic Surveillance Site register over 11 years.Main outcome measureExcess mean daily NCD-YLL were generated from the relative risk of maximum daily temperature on NCD-YLL, including effects delayed up to 14 days.ResultsDaily average NCD-YLL were 4.6, 2.4 and 2.1 person-years for all ages, men and women, respectively. Moderate 4-day cumulative rise in maximum temperature from 36.4°C (50th percentile) to 41.4°C (90th percentile) resulted in 4.44 (95% CI 0.24 to 12.28) excess daily NCD-YLL for all ages, rising to 7.39 (95% CI 0.32 to 24.62) at extreme temperature (42.8°C; 99th percentile). The strongest health effects manifested on the day of heat exposure (lag 0), where 0.81 (95% CI 0.13 to 1.59) excess mean NCD-YLL occurred daily at 41.7°C compared with 36.4°C, diminishing in statistical significance after 4 days. At lag 0, daily excess mean NCD-YLL were higher for men, 0.58 (95% CI 0.11 to 1.15) compared with women, 0.15 (95% CI −0.25 to 9.63) at 41.7°C vs 36.4°C.ConclusionPremature death from NCD was elevated significantly with moderate and extreme heat exposure. These findings have important implications for developing adaptation and mitigation strategies to reduce ambient heat exposure and preventive measures for limiting NCD in Africa.

“I stay at work for you, you stay at home for us.” Does this Covid-19 campaign work for the youth in Asia?

2020 ◽  
Vol 8 (2) ◽  
pp. 161-175
Author(s):  
Jappy Fanggidae ◽  
Ridolof Batilmurik ◽  
Pieter Samadara
Keyword(s):  
Experimental Method ◽  
Social Distancing ◽  
Asian Context ◽  
The Relationship ◽  
At Home

This study investigated the relationship between guilt appeal and compliance with social distancing measures. We proposed that the relationship is double mediated by empathy and responsibility for the unfortunate people who have suffered from COVID-19. This research is novel to an extent as an experimental method is used in the Asian context. The results exhibited that guilt positively affected compliance with social distancing measures. The respondents were directly or indirectly compliant due to the emotions of empathy and responsibility. The theoretical and practical contributions of this study were presented.

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