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 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.

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.

Ranking the explanatory power of factors associated with worldwide new Covid-19 cases

2021 ◽  
Author(s):  
Aristides Moustakas
Keyword(s):  
Explanatory Power ◽  
Interdisciplinary Approach ◽  
Time Frame ◽  
Variance Partitioning ◽  
Total Variance ◽  
Disease Spread ◽  
Spatial Spread ◽  
Global Pandemic ◽  
Governmental Intervention ◽  
Short Time

Abstract Disease spread is a complex phenomenon requiring an interdisciplinary approach. Covid-19 exhibited a global spatial spread in a very short time frame resulting in a global pandemic. Data of new Covid-19 cases per million were analysed worldwide at the spatial scale of a country and time replicated from the end of December 2019 to late May 2020. Data driven analysis of epidemiological, economic, public health, and governmental intervention variables was performed in order to select the optimal variables in explaining new Covid-19 cases across all countries in time. Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 cases per million. Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases (51.6%) followed by the governmental stringency index (15.2%). Availability of hospital beds per 100k inhabitants explained 9% extreme poverty explained 8.8%, hand washing facilities 5.3%, the fraction of the population aged 65 or older explained 3.9%, and other disease prevalence (cardiovascular diseases plus diabetes) explained 2.9%. The percentage of smokers within the population explained 2.6% of the total variance, while population density explained 0.6%.

scholarly journals Moderating Effect of Mindfulness on the Influence of Stress on Depression According to the Level of Stress among University Students in South Korea

2020 ◽  
Vol 17 (18) ◽  
pp. 6634
Author(s):  
Kwang-Hi Park ◽  
Hyunlye Kim ◽  
Jaehee Kim
Keyword(s):  
Regression Analysis ◽  
University Students ◽  
South Korea ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Life Stress ◽  
Mental Health Problems ◽  
Explanatory Power ◽  
Moderating Effect ◽  
The Impact

Stress and depression are representative of the mental health problems of university students worldwide. This cross-sectional study explored the moderating effect of mindfulness on the influence of stress on depression according to the degree of life stress. The participants were 738 university students in years 2–4 in five 4-year universities in South Korea. Depression was positively correlated with stress and negatively with mindfulness at a statistically significant level. In multiple regression analysis, stress was found to have an effect by increasing depression, and mindfulness by relieving depression. In the moderated multiple regression analysis, mindfulness had a moderating effect on the impact of stress on depression only in low-stress groups, showing that the interaction of stress with mindfulness was significantly negative (β = −0.11, t = −2.52, p = 0.012) and the inclusion of this interaction significantly increased the explanatory power for depression variation (F change 6.36, p = 0.012) in the full model. In conclusion, we suggest considering stress levels in the development of mindfulness-based intervention strategies to effectively manage the depression of university students.

scholarly journals Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Jooyoung Lee ◽  
Jiho Yeo ◽  
Ilsoo Yun ◽  
Sanghyeok Kang
Keyword(s):  
South Korea ◽  
Safety Management ◽  
Commercial Vehicle ◽  
Unobserved Heterogeneity ◽  
Management Policy ◽  
Single Type ◽  
Commercial Vehicles ◽  
Related Factors ◽  
Factors Affecting ◽  
Different Types

The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes.

scholarly journals National-Local Networks and Immigration Governance

2019 ◽  
Vol 19 (3) ◽  
pp. 345-376
Author(s):  
Jill L. Tao
Keyword(s):  
South Korea ◽  
Immigration Policy ◽  
Local Governments ◽  
Central Government ◽  
Local Level ◽  
Local Economy ◽  
Local Autonomy ◽  
Local Networks ◽  
Local Officials ◽  
The Impact

The ability to regulate the flow of goods, capital and people across borders is one of the defining characteristics of nation-state political power. But there is not always agreement between the central government and local officials as to the desirability of immigration, where local governments may desire greater, or fewer, numbers of immigrants, depending on the local economy and labor needs. In South Korea, a unitary form of government offers an opportunity to examine the policy distance between the national government’s stance on immigration based on the politics of the ruling party, and the attitudes of local officials who work for metropolitan-level governments (those with a population of one million or more). I look at the impact of local economic market needs on local attitudes towards national immigration policy through the lens of intergovernmental relations and Lipsky’s concept of bureaucratic discretion. Comparing two cases drawn from local governments in South Korea with dissimilar economic bases but similar levels of local autonomy, I find that economic needs at the local level are addressed by local approaches to immigration policy. Contrary to expectations, the cases illustrate the relative importance of fiscal autonomy and a new understanding for political autonomy. These cases illustrate the need for caution when applying political and institutional theory within new contexts and offer new variables for future investigations of local autonomy.

Modeling Latent Information in Voting Data with Dirichlet Process Priors

2015 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Richard Traunmüller ◽  
Andreas Murr ◽  
Jeff Gill
Keyword(s):  
Dirichlet Process ◽  
Unobserved Heterogeneity ◽  
Vote Choice ◽  
Dirichlet Process Mixture ◽  
Omitted Variables ◽  
Factors Affecting ◽  
Structural Integration ◽  
Mixture Prior ◽  
Latent Effects ◽  
Improved Model

We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobserved heterogeneity among immigrant voters. Our approach is based ongeneralized linear mixed Dirichlet models(GLMDMs) where random effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account for unobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latent effects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficult problems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2) smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantive understanding of the factors affecting immigrants' turnout and vote choice. Once we account for unobserved heterogeneity among immigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extant literature suggests. When looking at vote choice, we also found that an immigrant's degree of structural integration does not affect the vote in favor of the CDU/CSU, a party that is traditionally associated with restrictive immigration policy.

scholarly journals A Physically Based Theoretical Model of Spore Deposition for Predicting Spread of Plant Diseases

2016 ◽  
Vol 106 (3) ◽  
pp. 244-253 ◽  
Author(s):  
Scott A. Isard ◽  
Marcelo Chamecki
Keyword(s):  
Theoretical Model ◽  
Resistance Coefficient ◽  
Plant Diseases ◽  
Disease Spread ◽  
Source Distribution ◽  
Rust Disease ◽  
Modeling Framework ◽  
Area Source ◽  
Physically Based ◽  
Spore Deposition

A physically based theory for predicting spore deposition downwind from an area source of inoculum is presented. The modeling framework is based on theories of turbulence dispersion in the atmospheric boundary layer and applies only to spores that escape from plant canopies. A “disease resistance” coefficient is introduced to convert the theoretical spore deposition model into a simple tool for predicting disease spread at the field scale. Results from the model agree well with published measurements of Uromyces phaseoli spore deposition and measurements of wheat leaf rust disease severity. The theoretical model has the advantage over empirical models in that it can be used to assess the influence of source distribution and geometry, spore characteristics, and meteorological conditions on spore deposition and disease spread. The modeling framework is refined to predict the detailed two-dimensional spatial pattern of disease spread from an infection focus. Accounting for the time variations of wind speed and direction in the refined modeling procedure improves predictions, especially near the inoculum source, and enables application of the theoretical modeling framework to field experiment design.

scholarly journals Understanding the Spatial Trajectories of Minority Groups: An Approach that Examines their Demographic, Cultural and Socio-economic Characteristics

2019 ◽  
Vol 44 ◽  
Author(s):  
Philip Sapiro
Keyword(s):  
Policy Development ◽  
Minority Groups ◽  
Local Level ◽  
Minority Group ◽  
Explanatory Power ◽  
Measurement Techniques ◽  
National Level ◽  
Distribution Patterns ◽  
Test Bed ◽  
Political Debate

Population researchers have contributed to the debate on minority group distribution and disadvantage and social cohesion by providing objective analysis. A plethora of new distribution measurement techniques have been presented in recent years, but they have not provided sufficient explanatory power of underlying trajectories to inform ongoing political debate. Indeed, a focus on trying to summarise complex situations with readily understood measures may be misplaced. This paper takes an alternative approach and asks whether a more detailed analysis of individual and environmental characteristics is necessary if researchers are to continue to provide worthwhile input to policy development. Using England and Wales as a test bed, it looks at four small sub-populations (circa 250,000 at the turn of the century) – two based on ethnic grouping: Bangladeshi and Chinese; and two based on an under-researched area of cultural background, religion: Jews and Sikhs. Despite major differences in longevity of presence in the UK, age profile, socio-economic progress, and levels of inter-marriage, there are, at a national level, parallels in the distribution patterns and trajectories for three of the groups. However, heterogeneity between and within the groups mean that at a local level, these similarities are confounded. The paper concludes that complex interactions between natural change and migration, and between suburbanisation and a desire for group congregation, mean that explanations for the trajectory of distribution require examination of data at a detailed level, beyond the scope of index-based methods. Such analyses are necessary if researchers are to effectively contribute to future policy development.

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