Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic

2021 ◽  
pp. 1-20
Author(s):  
Xiao Huang ◽  
Junyu Lu ◽  
Song Gao ◽  
Sicheng Wang ◽  
Zhewei Liu ◽  
...  
Keyword(s):  
United States ◽  
Mobile Phone ◽  
The United States ◽  
Fine Grained ◽  
Location Data ◽  
Staying At Home ◽  
At Home

scholarly journals Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

2020 ◽  
Vol 5 (6) ◽  
pp. 1140-1155 ◽  
Author(s):  
Hoang Pham
Keyword(s):  
United States ◽  
United Kingdom ◽  
Policy Decision ◽  
The United States ◽  
Social Distancing ◽  
Death Toll ◽  
The United Kingdom ◽  
First Case ◽  
Staying At Home ◽  
At Home

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

scholarly journals Japan’s voluntary lockdown

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252468
Author(s):  
Tsutomu Watanabe ◽  
Tomoyoshi Yabu
Keyword(s):  
United States ◽  
The United States ◽  
The State ◽  
State Of Emergency ◽  
Location Data ◽  
New Information ◽  
Order Of Magnitude ◽  
At Home

Japan’s government has taken a number of measures, including declaring a state of emergency, to combat the spread COVID-19. We examine the mechanisms through which the government’s policies have led to changes in people’s behavior. Using smartphone location data, we construct a daily prefecture-level stay-at-home measure to identify the following two effects: (1) the effect that citizens refrained from going out in line with the government’s request, and (2) the effect that government announcements reinforced awareness with regard to the seriousness of the pandemic and people voluntarily refrained from going out. Our main findings are as follows. First, the declaration of the state of emergency reduced the number of people leaving their homes by 8.5% through the first channel, which is of the same order of magnitude as the estimates obtained for lockdowns in the United States. Second, a 1% increase in new infections in a prefecture reduces people’s outings in that prefecture by 0.027%. Third, the government’s requests are responsible for about one quarter of the decrease in outings in Tokyo, while the remaining three quarters are the result of citizens obtaining new information through government announcements and the daily release of the number of infections. The findings suggest that what mattered for containing the spread of COVID-19 was not strong, legally binding measures but the provision of appropriate information that encouraged people to change their behavior.

scholarly journals Quantifying behavior change during the first year of the COVID-19 pandemic in the United States

2022 ◽  
Author(s):  
Dennis L Chao ◽  
Victor Cho ◽  
Amanda S Izzo ◽  
Joshua L Proctor ◽  
Marita Zimmermann
Keyword(s):  
United States ◽  
State Level ◽  
The United States ◽  
Early Spring ◽  
First Year ◽  
Mobility Data ◽  
High Incidence ◽  
Low Incidence ◽  
Staying At Home ◽  
At Home

Background: During the first year of the COVID-19 pandemic, the most effective way to reduce transmission and to protect oneself was to reduce contact with others. However, it is unclear how behavior changed, despite numerous surveys about peoples' attitudes and actions during the pandemic and public health efforts to influence behavior. Methods: We used two sources of data to quantify changes in behavior at the county level during the first year of the pandemic in the United States: aggregated mobile device (smartphone) location data to approximate the fraction of people staying at home each day and digital invitation data to capture the number and size of social gatherings. Results: Between mid-March to early April 2020, the number of events fell and the fraction of devices staying at home peaked, independently of when states issued emergency orders or stay-at-home recommendations. Activity began to recover in May or June, with later rebounds in counties that suffered an early spring wave of reported COVID-19 cases. Counties with high incidence in the summer had more events, higher mobility, and less stringent state-level COVID-related restrictions the month before than counties with low incidence. Counties with high incidence in early fall stayed at home less and had less stringent state-level COVID-related restrictions in October, when cases began to rise in some parts of the US. During the early months of the pandemic, the number of events was inversely correlated with the fraction of devices staying at home, but after the fall of 2020 mobility appeared to stay constant as the number of events fell. Greater changes in behavior were observed in counties where a larger fraction voted for Biden in the 2020 US Presidential election. The number of people invited per event dropped gradually throughout the first year of the pandemic. Conclusions: The mobility and events datasets uncovered different kinds of behavioral responses to the pandemic. Our results indicate that people did in fact change their behavior in ways that likely reduced COVID exposure and transmission, though the degree of change appeared to be affected by political views. Though the mobility data captured the initial massive behavior changes in the first months of the pandemic, the digital invitation data, presented here for the first time, continued to show large changes in behavior later in the first year of the pandemic.

scholarly journals Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

2020 ◽  
Author(s):  
Hoang Pham
Keyword(s):  
United States ◽  
United Kingdom ◽  
The United States ◽  
List Type ◽  
Social Distancing ◽  
Death Toll ◽  
The United Kingdom ◽  
First Case ◽  
Staying At Home ◽  
At Home

AbstractCOVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University.With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way?In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study.The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020.The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020.The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

Armed Violence in the Ideology of Contemporary Islamic Fundamentalism - Study models

2019 ◽  
Vol 2 (4) ◽  
pp. 237
Author(s):  
Laith Mzahim Khudair Kazem
Keyword(s):  
United States ◽  
United States Of America ◽  
The United States ◽  
The Other ◽  
Islamic Fundamentalism ◽  
Islamic Movements ◽  
Important Means ◽  
Armed Violence ◽  
Goals And Objectives ◽  
At Home

The armed violence of many radical Islamic movements is one of the most important means to achieve the goals and objectives of these movements. These movements have legitimized and legitimized these violent practices and constructed justification ideologies in order to justify their use for them both at home against governments or against the other Religiously, intellectually and even culturally, or abroad against countries that call them the term "unbelievers", especially the United States of America.

REVISITING CONTEMPORARY HOMESCHOOLING: THE CASE OF THE UNITED STATES

2020 ◽  
Author(s):  
Sara Zamir
Keyword(s):  
United States ◽  
The United States ◽  
American States ◽  
At Home

The term “homeschooling” denotes the process of educating, instructing, and tutoring children by parents at home instead of having this done by professional teachers in formal settings. Although regulation and court rulings vary from one state to another, homeschooling is legal in all fifty American states. Contrary to the growing tendency of parents in the United States to move toward homeschooling in 1999-2012, the rate of homeschooling and the population of those educated in this manner appear to have leveled off in 2012–2016. This paper aims to explain both phenomena and asks whether a trend is at hand.

scholarly journals Family caregiving and place of death: Insights from cross-national analysis of the Harmonized End of Life Data

2020 ◽  
Author(s):  
Jennifer Ailshire ◽  
Margarita Osuna ◽  
Jenny Wilkens ◽  
Jinkook Lee
Keyword(s):  
United States ◽  
Older Adults ◽  
Nursing Home ◽  
End Of Life ◽  
Family Caregiving ◽  
The United States ◽  
Place Of Death ◽  
Life Data ◽  
Cross National ◽  
At Home

Abstract Objectives Family is largely overlooked in research on factors associated with place of death among older adults. We determine if family caregiving at the end of life is associated with place of death in the United States and Europe. Methods We use the Harmonized End of Life data sets developed by the Gateway to Global Aging Data for the Survey of Health, Ageing and Retirement in Europe (SHARE) and the Health and Retirement Study (HRS). We conducted multinomial logistic regression on 7,113 decedents from 18 European countries and 3,031 decedents from the United States to determine if family caregiving, defined based on assistance with activities of daily living, was associated with death at home versus at a hospital or nursing home. Results Family caregiving was associated with reduced odds of dying in a hospital and nursing home, relative to dying at home in both the United States and Europe. Care from a spouse/partner or child/grandchild was both more common and more strongly associated with place of death than care from other relatives. Associations between family caregiving and place of death were generally consistent across European welfare regimes. Discussion This cross-national examination of family caregiving indicates that family-based support is universally important in determining where older adults die. In both the United States and in Europe, most care provided during a long-term illness or disability is provided by family caregivers, and it is clear families exert tremendous influence on place of death.

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