scholarly journals A multi-method approach to modeling COVID-19 disease dynamics in the United States

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amir Mokhtari ◽  
Cameron Mineo ◽  
Jeffrey Kriseman ◽  
Pedro Kremer ◽  
Lauren Neal ◽  
...  
Keyword(s):  
Local Level ◽  
Optimization Technique ◽  
The United States ◽  
Disease Spread ◽  
Model Parameters ◽  
Modeling Framework ◽  
Modeling Approach ◽  
Wide Range ◽  
Out Of Sample ◽  
State And Local

AbstractIn this paper, we proposed a multi-method modeling approach to community-level spreading of COVID-19 disease. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread, including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model’s state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to evaluate perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model’s two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the prediction error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.

scholarly journals A Multi-Method Approach to Modeling COVID-19 Disease Dynamics in the United States

2021 ◽  
Author(s):  
Amir Mokhtari ◽  
Cameron Mineo ◽  
Jeffrey Kriseman ◽  
Pedro Kremer ◽  
Lauren Neal ◽  
...  
Keyword(s):  
Local Level ◽  
Optimization Technique ◽  
The United States ◽  
Disease Spread ◽  
Model Parameters ◽  
Modeling Framework ◽  
Modeling Approach ◽  
Wide Range ◽  
Out Of Sample ◽  
State And Local

Abstract In this paper, we proposed a multi-method modeling approach to community-level COVID-19 disease spread. Our methodology was composed of interconnected age-stratified system dynamics models in an agent-based modeling framework that allowed for a granular examination of the scale and severity of disease spread including metrics such as infection cases, deaths, hospitalizations, and ICU usage. Model parameters were calibrated using an optimization technique with an objective function to minimize error associated with the cumulative cases of COVID-19 during a training period between March 15 and October 31, 2020. We outlined several case studies to demonstrate the model’s state- and local-level projection capabilities. We further demonstrated how model outcomes could be used to access perceived levels of COVID-19 risk across different localities using a multi-criteria decision analysis framework. The model’s two, three, and four week out-of-sample projection errors varied on a state-by-state basis, and generally increased as the out-of-sample projection period was extended. Additionally, the error in the state-level projections was generally due to an underestimation of cases and an overestimation of deaths. The proposed modeling approach can be used as a virtual laboratory to investigate a wide range of what-if scenarios and easily adapted to future high-consequence public health threats.

Life Without Parole Sentencing

2019 ◽  
Author(s):  
Christopher Seeds
Keyword(s):  
United States ◽  
Lived Experience ◽  
Death Penalty ◽  
Public Discourse ◽  
Crime Control ◽  
Local Level ◽  
The United States ◽  
Life Without Parole ◽  
The Subject ◽  
State And Local

Life without parole sentencing refers to laws, policies, and practices concerning lifetime prison sentences that also preclude release by parole. While sentences to imprisonment for life without the possibility of parole have existed for more than a century in the United States, over the past four decades the penalty has emerged as a prominent element of U.S. punishment, routinely put to use by penal professionals and featured regularly in public discourse. As use of the death penalty diminishes in the United States, life without parole serves as the ultimate punishment in more and more U.S. jurisdictions. The scope with which states apply life without parole varies, however, and some states have authorized the punishment even for nonviolent offenses. More than a punishment serving purposes of retribution, crime control, and public safety, and beyond the symbolic functions of life without parole sentencing in U.S. culture and politics, life without parole is a lived experience for more than 50,000 prisoners in the United States. Life without parole’s increasing significance in the United States points to the need for further research on the subject—including studies that directly focus on how race and racial prejudice factor in life without parole sentencing, studies that investigate the proximate causes of life without parole sentences at the state and local level, and studies that examine the similarities and differences between life without parole, the death penalty, and de facto forms of imprisonment until death.

scholarly journals Spatiotemporal Associations between Local Safety Level Index and COVID-19 Infection Risks across Capital Regions in South Korea

2022 ◽  
Vol 19 (2) ◽  
pp. 824
Author(s):  
Youngbin Lym ◽  
Hyobin Lym ◽  
Keekwang Kim ◽  
Ki-Jung Kim
Keyword(s):  
South Korea ◽  
Unobserved Heterogeneity ◽  
Local Level ◽  
Explanatory Power ◽  
Temporal Correlation ◽  
Disease Spread ◽  
Modeling Framework ◽  
Safety Level ◽  
Latent Effects ◽  
Local Safety

This study aims provide understanding of the local-level spatiotemporal evolution of COVID-19 spread across capital regions of South Korea during the second and third waves of the pandemic (August 2020~June 2021). To explain transmission, we rely upon the local safety level indices along with latent influences from the spatial alignment of municipalities and their serial (temporal) correlation. Utilizing a flexible hierarchical Bayesian model as an analytic operational framework, we exploit the modified BYM (BYM2) model with the Penalized Complexity (PC) priors to account for latent effects (unobserved heterogeneity). The outcome reveals that a municipality with higher population density is likely to have an elevated infection risk, whereas one with good preparedness for infectious disease tends to have a reduction in risk. Furthermore, we identify that including spatial and temporal correlations into the modeling framework significantly improves the performance and explanatory power, justifying our adoption of latent effects. Based on these findings, we present the dynamic evolution of COVID-19 across the Seoul Capital Area (SCA), which helps us verify unique patterns of disease spread as well as regions of elevated risk for further policy intervention and for supporting informed decision making for responding to infectious diseases.

scholarly journals Promoting Clean Energy Innovation at the State and Local Level

2020 ◽  
Vol 49 (2) ◽  
pp. 360-373
Author(s):  
David Popp
Keyword(s):  
Local Governments ◽  
Local Level ◽  
State Level ◽  
Clean Energy ◽  
The United States ◽  
The State ◽  
Local Economy ◽  
Working Paper ◽  
Energy Innovation ◽  
State And Local

AbstractInnovation is an important part of energy policy, and encouraging clean energy innovation is often an explicit goal of policy makers. For local governments, promoting clean energy innovation is seen not only as a pathway to a cleaner economy but also as a tool for promoting the local economy. But is such optimism warranted? There is a substantial literature examining the relationships between innovation and environmental policy, but few studies focus explicitly on innovation at the state and local level. In this paper, I provide key lessons from research on clean energy innovation, focusing on lessons relevant for state and local governments. I then summarize the results of a recent working paper by Fu et al. (2018) that studied wind energy innovation across individual states in the United States. While state-level policies can promote clean energy innovation, it is overall market size that matters most. Thus, innovation need not occur in those states most actively promoting clean energy. I conclude with lessons for state and local governments drawn from both this work and the broader literature on energy innovation.

Modeling Operational Robustness and Resiliency with High-Level Petri Nets

2011 ◽  
Vol 1 (2) ◽  
pp. 17-38 ◽  
Author(s):  
Madjid Tavana ◽  
Timothy E. Busch ◽  
Eleanor L. Davis
Keyword(s):  
Petri Nets ◽  
Workflow Management ◽  
The United States ◽  
Self Healing ◽  
Military Operations ◽  
Tight Coupling ◽  
Modeling Framework ◽  
Workflow Systems ◽  
Wide Range ◽  
High Level

Military operations are highly complex workflow systems that require careful planning and execution. The interactive complexity and tight coupling between people and technological systems has been increasing in military operations, which leads to both improved efficiency and a greater vulnerability to mission accomplishment due to attack or system failure. Although the ability to resist and recover from failure is important to many systems and processes, the robustness and resiliency of workflow management systems has received little attention in literature. The authors propose a novel workflow modeling framework using high-level Petri nets (PNs). The proposed framework is capable of both modeling structure and providing a wide range of qualitative and quantitative analysis. The concepts of self-protecting and self-healing systems are captured by the robustness and resiliency measures proposed in this study. The proposed measures are plotted in a Cartesian coordinate system; a classification scheme with four quadrants (i.e., possession, preservation, restoration, and devastation) is proposed to show the state of the system in terms of robustness and resiliency. The authors introduce an overall sustainability index for the system based on the theory of displaced ideals. The application of the methodology in the evaluation of an air tasking order generation system at the United States Air Force is demonstrated.

scholarly journals State Preparedness for Crisis Standards of Care in the United States: Implications for Emergency Management

2020 ◽  
pp. 1-3
Author(s):  
Annie E. Ingram ◽  
Attila J. Hertelendy ◽  
Michael S. Molloy ◽  
Gregory R. Ciottone
Keyword(s):  
United States ◽  
Health Care ◽  
Hurricane Katrina ◽  
Local Level ◽  
The United States ◽  
Health Care Facilities ◽  
Legal Responsibility ◽  
Standards Of Care ◽  
Clear Understanding ◽  
State And Local

Abstract State governments and hospital facilities are often unprepared to handle a complex medical crisis, despite a moral and ethical obligation to be prepared for disaster. The 2019 novel coronavirus disease (COVID-19) has drawn attention to the lack of state guidance on how hospitals should provide care in a crisis. When the resources available are insufficient to treat the current patient load, crisis standards of care (CSC) are implemented to provide care to the population in an ethical manner, while maintaining an ability to handle the surge. This Editorial aims to raise awareness concerning a lack of preparedness that calls for immediate correction at the state and local level. Analysis of state guidelines for implementation of CSC demonstrates a lack of preparedness, as only five states in the US have appropriately completed necessary plans, despite a clear understanding of the danger. States have a legal responsibility to regulate the medical care within their borders. Failure of hospital facilities to properly prepare for disasters is not a new issue; Hurricane Katrina (2005) demonstrated a lack of planning and coordination. Improving disaster health care readiness in the United States requires states to create new policy and legislative directives for the health care facilities within their respective jurisdictions. Hospitals should have clear directives to prepare for disasters as part of a “duty to care” and to ensure that the necessary planning and supplies are available to their employees.

Washington View

2017 ◽  
Vol 98 (6) ◽  
pp. 72-73
Author(s):  
Maria Ferguson
Keyword(s):  
United States ◽  
United Kingdom ◽  
Local Level ◽  
The United States ◽  
The State ◽  
The United Kingdom ◽  
State And Local ◽  
Policy Proposals ◽  
The U.S

Seismic shifts in both the United States and the United Kingdom during the 2016 elections have introduced changes in the education space as well. Worries about jobs, immigration, and shifting demographics underlie policy proposals in both countries. Where the U.S. is trying to drive change to the state and local level, however, Britain is moving toward centralization.

scholarly journals High-Dimensional Methods and Inference on Structural and Treatment Effects

2014 ◽  
Vol 28 (2) ◽  
pp. 29-50 ◽  
Author(s):  
Alexandre Belloni ◽  
Victor Chernozhukov ◽  
Christian Hansen
Keyword(s):  
Data Mining ◽  
Predictive Ability ◽  
Individual Characteristics ◽  
High Dimensional ◽  
Model Parameters ◽  
Large Set ◽  
Empirical Economics ◽  
Level Data ◽  
Wide Range ◽  
Out Of Sample

Data with a large number of variables relative to the sample size—“high-dimensional data”—are readily available and increasingly common in empirical economics. Highdimensional data arise through a combination of two phenomena. First, the data may be inherently high dimensional in that many different characteristics per observation are available. For example, the US Census collects information on hundreds of individual characteristics and scanner datasets record transaction-level data for households across a wide range of products. Second, even when the number of available variables is relatively small, researchers rarely know the exact functional form with which the small number of variables enter the model of interest. Researchers are thus faced with a large set of potential variables formed by different ways of interacting and transforming the underlying variables. This paper provides an overview of how innovations in “data mining” can be adapted and modified to provide high-quality inference about model parameters. Note that we use the term “data mining” in a modern sense which denotes a principled search for “true” predictive power that guards against false discovery and overfitting, does not erroneously equate in-sample fit to out-of-sample predictive ability, and accurately accounts for using the same data to examine many different hypotheses or models.

scholarly journals Surveillance of Physical Activity and Sedentary Behavior Among Youth and Adults in the United States: History and Opportunities

2021 ◽  
Vol 18 (S1) ◽  
pp. S6-S24 ◽  
Author(s):  
John D. Omura ◽  
Geoffrey P. Whitfield ◽  
Tiffany J. Chen ◽  
Eric T. Hyde ◽  
Emily N. Ussery ◽  
...  
Keyword(s):  
Physical Activity ◽  
United States ◽  
Sedentary Behavior ◽  
Local Level ◽  
The United States ◽  
Surveillance Systems ◽  
National Surveillance ◽  
The Past ◽  
Activity Measures ◽  
State And Local

Background: Surveillance is a core function of public health, and approaches to national surveillance of physical activity and sedentary behavior have evolved over the past 2 decades. The purpose of this paper is to provide an overview of surveillance of physical activity and sedentary behavior in the United States over the past 2 decades, along with related challenges and emerging opportunities. Methods: The authors reviewed key national surveillance systems for the assessment of physical activity and sedentary behavior among youth and adults in the United States between 2000 and 2019. Results: Over the past 20 years, 8 surveillance systems have assessed physical activity, and 5 of those have assessed sedentary behavior. Three of the 8 originated in nonpublic health agencies. Most systems have assessed physical activity and sedentary behavior via surveys. However, survey questions varied over time within and also across systems, resulting in a wide array of available data. Conclusion: The evolving nature of physical activity surveillance in the United States has resulted in both broad challenges (eg, balancing content with survey space; providing data at the national, state, and local level; adapting traditional physical activity measures and survey designs; and addressing variation across surveillance systems) and related opportunities.

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