Industry and government response to transportation R&D budget levels

2001 ◽  
Author(s):  
Edward Peak
Keyword(s):  
Government Response ◽  
Budget Levels

scholarly journals The Impact of Health Policies and Sociodemographic Factors on Doubling Time of the COVID-19 Pandemic in Mexico

2021 ◽  
Vol 18 (5) ◽  
pp. 2354
Author(s):  
Lina Díaz-Castro ◽  
Héctor Cabello-Rangel ◽  
Kurt Hoffman
Keyword(s):  
Population Density ◽  
Doubling Time ◽  
Sociodemographic Factors ◽  
Health Policies ◽  
Government Response ◽  
University Of Oxford ◽  
Negative Effect ◽  
Population Sizes ◽  
The University ◽  
The Impact

Background. The doubling time is the best indicator of the course of the current COVID-19 pandemic. The aim of the present investigation was to determine the impact of policies and several sociodemographic factors on the COVID-19 doubling time in Mexico. Methods. A retrospective longitudinal study was carried out across March–August, 2020. Policies issued by each of the 32 Mexican states during each week of this period were classified according to the University of Oxford Coronavirus Government Response Tracker (OxCGRT), and the doubling time of COVID-19 cases was calculated. Additionally, variables such as population size and density, poverty and mobility were included. A panel data model was applied to measure the effect of these variables on doubling time. Results. States with larger population sizes issued a larger number of policies. Delay in the issuance of policies was associated with accelerated propagation. The policy index (coefficient 0.60, p < 0.01) and the income per capita (coefficient 3.36, p < 0.01) had a positive effect on doubling time; by contrast, the population density (coefficient −0.012, p < 0.05), the mobility in parks (coefficient −1.10, p < 0.01) and the residential mobility (coefficient −4.14, p < 0.01) had a negative effect. Conclusions. Health policies had an effect on slowing the pandemic’s propagation, but population density and mobility played a fundamental role. Therefore, it is necessary to implement policies that consider these variables.

scholarly journals Public governance, agility and pandemics: a case study of the UK response to COVID-19

2021 ◽  
pp. 002085232098340
Author(s):  
Paul Joyce
Keyword(s):  
Leadership Role ◽  
Public Confidence ◽  
Strategic Decisions ◽  
Public Governance ◽  
Local Initiative ◽  
Government Response ◽  
Local Resources ◽  
The Government ◽  
The One ◽  
The Uk

The UK government’s leaders initially believed that it was among the best-prepared governments for a pandemic. By June 2020, the outcome of the collision between the government’s initial confidence, on the one hand, and the aggressiveness and virulence of COVID-19, on the other, was evident. The UK had one of the worst COVID-19 mortality rates in the world. This article explores the UK government’s response to COVID-19 from a public administration and governance perspective. Using factual information and statistical data, it considers the government’s preparedness and strategic decisions, the delivery of the government response, and public confidence in the government. Points for practitioners Possible lessons for testing through application include: Use the precautionary principle to set planning assumptions in government strategies to create the possibility of government agility during a pandemic. Use central government’s leadership role to facilitate and enable local initiative and operational responses, as well as to take advantage of local resources and assets. Choose smart government responses that address tensions between the goal of saving lives and other government goals, and beware choices that are unsatisfactory compromises.

scholarly journals The relationship between time to a high COVID-19 response level and timing of peak daily incidence: an analysis of governments’ Stringency Index from 148 countries

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yan Ma ◽  
Shiva Raj Mishra ◽  
Xi-Kun Han ◽  
Dong-Shan Zhu
Keyword(s):  
Standard Error ◽  
Daily Basis ◽  
Linear Regression Models ◽  
Response Level ◽  
Peak Number ◽  
Epidemic Curve ◽  
Government Response ◽  
Early Arrival ◽  
High Level ◽  
The Relationship

Abstract Background The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments’ policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory. Methods Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government’s response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases. Results Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage—from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments’ responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R2 = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R2 = 0.42) days earlier to the peak number of daily new case. Conclusions Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level. Graphic abstract

scholarly journals Understanding the Evolution of Government Attention in Response to COVID-19 in China: A Topic Modeling Approach

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 898
Author(s):  
Quan Cheng ◽  
Jianhua Kang ◽  
Minwang Lin
Keyword(s):  
Latent Dirichlet Allocation ◽  
Effective Control ◽  
Public Health Emergencies ◽  
Government Response ◽  
Evolution Path ◽  
Essential Parameter ◽  
Governance Strategies ◽  
Internal Relationships ◽  
Shed Light ◽  
Modelling Approach

The effective control over the outbreak of COVID-19 in China showcases a prompt government response, in which, however, the allocation of attention, as an essential parameter, remains obscure. This study is designed to clarify the evolution of the Chinese government’s attention in tackling the pandemic. To this end, 674 policy documents issued by the State Council of China are collected to establish a text corpus, which is then used to extract policy topics by applying the latent dirichlet allocation (LDA) model, a topic modelling approach. It is found that the response policies take different tracks in a four-stage controlling process, and five policy topics are identified as major government attention areas in all stages. Moreover, a topic evolution path is highlighted to show internal relationships between different policy topics. These findings shed light on the Chinese government’s dynamic response to the pandemic and indicate the strength of applying adaptive governance strategies in coping with public health emergencies.

scholarly journals Depression and loneliness of older adults in Europe and Israel after the first wave of covid-19

2021 ◽  
Author(s):  
Josefine Atzendorf ◽  
Stefan Gruber
Keyword(s):  
Older Adults ◽  
Well Being ◽  
Individual Characteristics ◽  
Control Measures ◽  
Epidemic Control ◽  
Government Response ◽  
Country Level ◽  
Interaction Terms ◽  
Share Data ◽  
Using Data

AbstractEpidemic control measures that aim to introduce social distancing help to decelerate the spread of the COVID-19 pandemic. However, their consequences in terms of mental well-being might be negative, especially for older adults. While existing studies mainly focus on the time during the first lockdown, we look at the weeks afterward in order to measure the medium-term consequences of the first wave of the pandemic. Using data from the SHARE Corona Survey, we include retired respondents aged 60 and above from 25 European countries plus Israel. Combining SHARE data with macro-data from the Oxford COVID-19 Government Response Tracker allows us to include macro-indicators at the country level, namely the number of deaths per 100,000 and the number of days with stringent epidemic control measures, in addition to individual characteristics. The findings show that both macro-indicators are influential for increased feelings of sadness/depression, but that individual factors are crucial for explaining increased feelings of loneliness in the time after the first lockdown. Models with interaction terms reveal that the included macro-indicators have negative well-being consequences, particularly for the oldest survey participants. Additionally, the results reveal that especially those living alone had a higher risk for increased loneliness in the time after the first COVID-19 wave.

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