scholarly journals Evaluating the impact of stay-at-home orders on the time to reach the peak burden of Covid-19 cases and deaths: does timing matter?

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Alexandra Medline ◽  
Lamar Hayes ◽  
Katia Valdez ◽  
Ami Hayashi ◽  
Farnoosh Vahedi ◽  
...  
Keyword(s):  
Social Impact ◽  
Regression Analyses ◽  
Us States ◽  
Peak Number ◽  
Time To Peak ◽  
The Us ◽  
Prolonged Duration ◽  
Case Rate ◽  
The Impact ◽  
At Home

Abstract Background The economic, psychological, and social impact of pandemics and social distancing measures prompt the urgent need to determine the efficacy of non-pharmaceutical interventions (NPIs), especially those considered most stringent such as stay-at-home and self-isolation mandates. This study focuses specifically on the impact of stay-at-home orders, both nationally and internationally, on the control of COVID-19. Methods We conducted an observational analysis from April to May 2020 and included both countries and US states with known stay-at-home orders. Our primary exposure was the time between the date of the first reported case of COVID-19 to an implemented stay-at-home mandate for each region. Our primary outcomes were the time from the first reported case to the highest number of daily cases and daily deaths. We conducted linear regression analyses, controlling for the case rate of the outbreak in each respective region. Results For countries and US states, a longer period of time between the first reported case and stay-at-home mandates was associated with a longer time to reach both the peak daily case and death counts. The largest effect was among regions classified as the latest 10% to implement a mandate, which in the US, predicted an extra 35.3 days (95% CI: 18.2, 52.5) to the peak number of cases, and 38.3 days (95% CI: 23.6, 53.0) to the peak number of deaths. Conclusions Our study supports the association between the timing of stay-at-home orders and the time to peak case and death counts for both countries and US states. Regions in which mandates were implemented late experienced a prolonged duration to reaching both peak daily case and death counts.

scholarly journals Evaluating the Efficacy of Stay-At-Home Orders: Does Timing Matter?

2020 ◽  
Author(s):  
Alexandra Medline ◽  
Lamar Hayes ◽  
Katia Valdez ◽  
Ami Hayashi ◽  
Farnoosh Vahedi ◽  
...  
Keyword(s):  
Social Consequences ◽  
Simple Linear Regression ◽  
Us States ◽  
Peak Number ◽  
Time To Peak ◽  
The Us ◽  
Prolonged Duration ◽  
Case Rate ◽  
The Many ◽  
At Home

ABSTRACTBACKGROUNDThe many economic, psychological, and social consequences of pandemics and social distancing measures create an urgent need to determine the efficacy of non-pharmaceutical interventions (NPIs), and especially those considered most stringent, such as stay-at-home and self-isolation mandates. This study focuses specifically on the efficacy of stay-at-home orders, both nationally and internationally, in the control of COVID-19.METHODSWe conducted an observational analysis from April to May 2020 and included countries and US states with known stay-at-home orders. Our primary exposure was the time between the date of the first reported case of COVID-19 to an implemented stay-at-home mandate for each region. Our primary outcomes were the time from the first reported case to the highest number of daily cases and daily deaths. We conducted simple linear regression analyses, controlling for the case rate of the outbreak.RESULTSFor US states and countries, a larger number of days between the first reported case and stay-at-home mandates was associated with a longer time to reach the peak daily case and death counts. The largest effect was among regions classified as the latest 10% to implement a mandate, which in the US, predicted an extra 35.3 days to the peak number of cases (95 % CI: 18.2, 52.5), and 38.3 days to the peak number of deaths (95 % CI: 23.6, 53.0).CONCLUSIONSOur study supports the potential beneficial effect of earlier stay-at-home mandates, by shortening the time to peak case and death counts for US states and countries. Regions in which mandates were implemented late experienced a prolonged duration to reaching both peak daily case and death counts.

scholarly journals The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

2021 ◽  
Vol 18 (3) ◽  
pp. 996
Author(s):  
Yun Li ◽  
Moming Li ◽  
Megan Rice ◽  
Haoyuan Zhang ◽  
Dexuan Sha ◽  
...  
Keyword(s):  
Growth Rate ◽  
Human Mobility ◽  
Public Information ◽  
Key Factors ◽  
Information Campaigns ◽  
Rapid Spread ◽  
The Us ◽  
Policy Measures ◽  
The Impact ◽  
At Home

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

scholarly journals COVID-19 cases and hospitalizations averted by case investigation and contact tracing in the United States

2021 ◽  
Author(s):  
Gabriel Rainisch ◽  
Seonghye Jeon ◽  
Danielle Pappas ◽  
Kimberly Spencer ◽  
Leah S Fischer ◽  
...  
Keyword(s):  
The United States ◽  
Contact Tracing ◽  
Federally Funded ◽  
Us States ◽  
Incidence Data ◽  
Case Investigation ◽  
The Us ◽  
Potential Benefits ◽  
Reported Data ◽  
The Impact

Importance: Evidence of the impact of COVID-19 Case Investigation and Contact Tracing (CICT) programs is lacking. Policymakers need this evidence to assess its value. Objective: Estimate COVID-19 cases and hospitalizations averted nationwide by US states' CICT programs. Design: We combined data from US CICT programs (e.g., proportion of cases interviewed, contacts notified or monitored, and days to case and contact notification) with incidence data to model CICT impacts over 60 days period (November 25, 2020 to January 23, 2021) during the height of the pandemic. We estimated a range of impacts by varying assumed compliance with isolation and quarantine recommendations. Setting: US States and Territories Participants: Fifty-nine state and territorial health departments that received federal funding supporting COVID-19 pandemic response activities were eligible for inclusion. Of these, 22 states and 1 territory reported all measures necessary for the analysis. These 23 jurisdictions covered 42.5% of the US population (140 million persons), spanned all 4 census regions, and reported data that reflected all 59 federally funded CICT programs. Intervention: Public health case investigation and contact tracing Main Outcomes and Measures: Cases and hospitalizations averted; percent of cases averted among cases not prevented by vaccination and other non-pharmaceutical interventions (other NPIs). Results: We estimated 1.11 million cases and 27,231 hospitalizations were averted by CICT programs under a scenario where 80% of interviewed cases and monitored contacts, and 30% of notified contacts fully complied with isolation and quarantine guidance, eliminating their contributions to future transmission. As many as 1.36 million cases and 33,527 hospitalizations could have been prevented if all interviewed cases and monitored contacts had entered into and fully complied with isolation and quarantine guidelines upon being interviewed or notified. Across all scenarios and jurisdictions, CICT averted a median of 21.2% (range: 1.3% - 65.8%) of the cases not prevented by vaccination and other NPIs. Conclusions and Relevance: CICT programs likely had a substantial role in curtailing the pandemic in most jurisdictions during the winter 2020-2021 peak. Differences in impact across jurisdictions indicate an opportunity to further improve CICT effectiveness. These estimates demonstrate the potential benefits from sustaining and improving these programs.

scholarly journals COVID-19 prevalence and predictors in United States adults during peak stay-at-home orders

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245586
Author(s):  
Robert Morlock ◽  
Amy Morlock ◽  
Martha Downen ◽  
Sonali N. Shah
Keyword(s):  
Symptom Severity ◽  
Early Recognition ◽  
Nationwide Survey ◽  
Total Sample ◽  
International Travel ◽  
Community Based ◽  
Cross Sectional ◽  
The Us ◽  
The Impact ◽  
At Home

Background Early recognition of COVID-19 cases is essential for effective public health measures aimed at isolation of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS–COV-2). The objective of this study was to describe characteristics, self-reported symptoms, and predictors of testing positive for SARS-CoV-2 infection in a community-based sample. Methods and findings This was a cross-sectional nationwide survey of adults in the US conducted between April 24 through May 13, 2020. The survey targeted a representative sample of approximately 5,000 respondents. The rate of COVID-19 cases and testing, most frequently reported symptoms, symptom severity, treatment received, impact of COVID-19 on mental and physical health, and factors predictive of testing positive were assessed. Most of the 5,203 participants (85.6%) reported no COVID-19-like symptoms. Of the 747 (14.5%) participants reporting COVID-19-like symptoms, 367 (49.1%) obtained a diagnostic test. Eighty-nine participants (24.3%) reported a positive COVID-19 test result, representing 1.7% of the total sample. For those testing positive, the most common symptoms were dry cough, fever, and shortness of breath/difficulty breathing. Those who tested positive were more likely to report greater symptom severity versus those who tested negative. Severe dry cough, new loss of taste or smell, trouble waking up, living with someone experiencing symptoms, recent international travel, respiratory issues, and reporting ethnicity of Black or African American were predictive of testing positive. Conclusions This study assessed the impact of COVID-19 using community-level self-reported data across the US during the peak of most stay at home’ orders. Self-reported symptoms and risk factors identified in this study are consistent with the clinical profile emerging for COVID-19. In the absence of widespread testing, this study demonstrates the utility of a representative US community-based sample to provide direct-reported symptoms and outcomes to quickly identify high-risk individuals who are likely to test positive and should consider taking greater precautions.

Administrative Law and Multi-Level Administration: An EU and US Comparison

2009 ◽  
Vol 11 ◽  
pp. 211-246
Author(s):  
Catherine Donnelly
Keyword(s):  
The United States ◽  
Administrative Law ◽  
The European Union ◽  
Member States ◽  
Us States ◽  
The Us ◽  
Multi Level ◽  
Eu And Us ◽  
The Impact ◽  
The Eu

AbstractThe aim of this chapter is to assess what, if anything, administrative law can demonstrate about multi-level administration in the European Union and the United States. The particular focus of the examination is not on the content of administrative law in each legal order, but rather on the impact of EU and US federal administrative law on the Member States and US States respectively. It will be seen that, while US federal administrative law has primarily only influential effect on US States, EU administrative law is often binding on Member States. This observation challenges presumptions often made, particularly in political science, as to the degrees of inter-penetration in administration in the EU and the US. It will be argued that the cause of divergence is largely derived from differing judicial attitudes as to the fundamental tenets of the co-operation between the different levels of administration, and indeed, more general understandings of federalism in the two jurisdictions. In this way, this study also provides a useful prism through which to consider integration in the EU and US more broadly.

Administrative Law and Multi-Level Administration: An EU and US Comparison

2009 ◽  
Vol 11 ◽  
pp. 211-246
Author(s):  
Catherine Donnelly
Keyword(s):  
The United States ◽  
Administrative Law ◽  
The European Union ◽  
Member States ◽  
Us States ◽  
The Us ◽  
Multi Level ◽  
Eu And Us ◽  
The Impact ◽  
The Eu

Abstract The aim of this chapter is to assess what, if anything, administrative law can demonstrate about multi-level administration in the European Union and the United States. The particular focus of the examination is not on the content of administrative law in each legal order, but rather on the impact of EU and US federal administrative law on the Member States and US States respectively. It will be seen that, while US federal administrative law has primarily only influential effect on US States, EU administrative law is often binding on Member States. This observation challenges presumptions often made, particularly in political science, as to the degrees of inter-penetration in administration in the EU and the US. It will be argued that the cause of divergence is largely derived from differing judicial attitudes as to the fundamental tenets of the co-operation between the different levels of administration, and indeed, more general understandings of federalism in the two jurisdictions. In this way, this study also provides a useful prism through which to consider integration in the EU and US more broadly.

scholarly journals Stay Home Save Lives: A Machine Learning Approach to Causal Inference to Evaluate Impact of Social Distancing in the US

2020 ◽  
Author(s):  
Syed Muhammad Ishraque Osman ◽  
Nazmus Sakib
Keyword(s):  
Machine Learning ◽  
Fixed Effects ◽  
Control Method ◽  
Control Group ◽  
Synthetic Control ◽  
Us States ◽  
Social Distancing ◽  
Machine Learning Approach ◽  
The Us ◽  
The Impact

Abstract Although there are few studies done to provide estimations of the impact of COVID-19 pandemic, however, there is a need for an actual policy evaluation of the already implemented social distancing measures. In the US context in specific, this is especially instrumental because nearly a dozen US states are considering the reopening of the economy following anti social distancing protests. Using a machine learning based Generalized Synthetic Control Method, considering the US states that adopted early social distancing approaches as the treatment group and the states that adopted social distancing much later as the control group and controlling for state and time fixed effects (to cancel out the selection bias and endogeneity), this paper finds that social distancing is associated with lower COVID-19 infection growth rate (by 192%) when compared to the no policy intervention counterfactual.

scholarly journals Stay Home Save Lives: A Machine Learning Approach to Causal Inference to Evaluate Impact of Social Distancing in the US

2021 ◽  
Author(s):  
Syed Muhammad Ishraque Osman ◽  
Nazmus Sakib
Keyword(s):  
Machine Learning ◽  
Policy Evaluation ◽  
Fixed Effects ◽  
Control Method ◽  
Control Group ◽  
Us States ◽  
Social Distancing ◽  
The Social ◽  
The Us ◽  
The Impact

Abstract Introduction: This study presents a machine learning based evaluation of the social distancing measures implemented in the US states. Objectives: Although there are a few studies that provide estimations of the impact of COVID-19 pandemic, there is a need for an actual policy evaluation of the already implemented social distancing measures. This paper presents an evaluation of the social distancing measures implemented by the US states. Methods: This research uses a machine learning based Generalized Synthetic Control Method. In doing so, it considers the US states that adopted early social distancing approaches as the treatment group and the states that adopted social distancing much later as the control group and it has controlled for state and time fixed effects, to cancel out the possible selection bias and endogeneity. Results: The results show that the first round of social distancing in the US is associated with lower COVID-19 infection growth rate (by -167%) when compared to the no policy intervention counterfactual. Conclusions: The findings from this policy evaluation establishes a robust scientific basis of the efficacy of social distancing measures on slowing down the contagion of a pandemic.

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