The Oxford COVID-19 Government Response Tracker (OxCGRT) systematically collects information on several different common policy responses that governments have taken to respond to the pandemic on 18 indicators such as school closures and travel restrictions. It now has data from more than 180 countries. The data is also used to inform a Risk of Openness Index which aims to help countries understand if it is safe to ‘open up’ or whether they should ‘close down’ in their fight to tackle the coronavirus.

After the global COVID-19 outbreak, the University of Oxford set up a Government Response Tracker, which analyzes the stringency to which governments around the world have responded to the sanitary crisis. According to the index, the French Government was easy on policies at the beginning of the year 2020, reaching a high value starting in March, just as the lockdown measures were implemented. The index value decreased in June before reaching a second high in *************. Vaccination programs were implemented from *************, and the French government introduced separate restrictions for vaccinated and non-vaccinated persons from *********. These separate restrictions stayed in place for just over ********, until ***********.

Governments are taking a wide range of measures in response to the COVID-19 outbreak. The Oxford COVID-19 Government Response Tracker (OxCGRT) aims track and compare government responses to the coronavirus outbreak worldwide rigorously and consistently.

The OxCGRT systematically collects information on several different common policy responses governments have taken, scores the stringency of such measures, and aggregates these scores into a common Stringency Index. For more, please visit > https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker

Context

Within the current response of a pandemic caused by the SARS-CoV-2 coronavirus, which in turn causes the disease, called COVID-19. It is necessary to join forces to minimize the effects of this disease.

Therefore, the intention of this dataset is to save data scientists time:

• Gather the data at the country level, encoding the country with its ISO code to allow easy access to other data
• Perform pre-processing of data, calculations of increments and other indicators that can facilitate modeling.
• Add the response of the governments over time so that it can be taken into account in the modeling.
• Daily update.

This dataset is not intended to be static, so suggestions for expanding it are welcome. If someone considers it important to add information, please let me know.

Content

The data contained in this dataset comes mainly from the following sources:

Source: Center for Systems Science and Engineering (CSSE) at Johns Hopkins University https://github.com/CSSEGISandData/COVID-19 Provided by Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE): https://systems.jhu.edu/

Source: OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker Hale, Thomas and Samuel Webster (2020). Oxford COVID-19 Government Response Tracker. Data use policy: Creative Commons Attribution CC BY standard.

The original data is updated daily.

The features it includes are:

• Country Name

• Country Code ISO 3166 Alpha 3

• Date

• Incidence data:

  • confirmed
  • deaths
  • recoveries

• Daily increments:

  • confirmed_inc
  • deaths_inc
  • recoveries_inc

• Empirical Contagion Rate - ECR

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3508582%2F3e90ecbcdf76dfbbee54a21800f5e0d6%2FECR.jpg?generation=1586861653126435&alt=media" alt="">

• GOVERNMENT RESPONSE TRACKER - GRTStringencyIndex

  OXFORD COVID-19 GOVERNMENT RESPONSE TRACKER - Stringency Index

• Indices from Start Contagion

  • Days since the first case of contagion is overcome
  • Days since 100 cases are exceeded

The method of obtaining the data and its transformations can be seen in the notebook:

Notebook COVID-19 Data by country with Government Response

Photo by Markus Spiske on Unsplash

This is data, used in the bachelor thesis "Anwendung des Oxford Stringency Index auf die Spanische Grippe in den Städten Winterthur und St. Gallen". The excel is written mainly in German. On the second tab relevant historical sources are listed. The file contains tabs with the following data:

Tracking Australian Subnational policy outcomes during the COVID-19 pandemic. Drawing on the Oxford COVID-19 Government Response Tracker (OxCGRT) coding system, we provide a systematic and objective account of the strength of Covid-19 response policies that have been instigated by Australia’s federal government and state and territory governments. Currently we provide coding for 16 indicators. These indicators allow the creation of four different indices: the stringency index, the containment and health index, the government response index and economic support index. The dataset is updated continuously in real time.

ED visits characteristics by Oxford Stringency Index category.

IntroductionThe COVID-19 outbreak disrupted regular health care, including the Emergency Department (ED), and resulted in insufficient ICU capacity. Lockdown measures were taken to prevent disease spread and hospital overcrowding. Little is known about the relationship of stringency of lockdown measures on ED utilization.ObjectiveThis study aimed to compare the frequency and characteristics of ED visits during the COVID-19 outbreak in 2020 to 2019, and their relation to stringency of lockdown measures.Material and methodsA retrospective multicentre study among five Dutch hospitals was performed. The primary outcome was the absolute number of ED visits (year 2018 and 2019 compared to 2020). Secondary outcomes were age, sex, triage category, way of transportation, referral, disposition, and treating medical specialty. The relation between stringency of lockdown measures, measured with the Oxford Stringency Index (OSI) and number and characteristics of ED visits was analysed.ResultsThe total number of ED visits in the five hospitals in 2019 was 165,894, whereas the total number of visits in 2020 was 135,762, which was a decrease of 18.2% (range per hospital: 10.5%-30.7%). The reduction in ED visits was greater during periods of high stringency lockdown measures, as indicated by OSI.ConclusionThe number of ED visits in the Netherlands has significantly dropped during the first year of the COVID-19 pandemic, with a clear association between decreasing ED visits and increasing lockdown measures. The OSI could be used as an indicator in the management of ED visits during a future pandemic.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic and related countermeasures hinder health care access and affect mental wellbeing of non-COVID-19 patients. There is lack of evidence on distress and mental health of patients hospitalized due to other reasons than COVID-19—a vulnerable population group in two ways: First, given their risk for physical diseases, they are at increased risk for severe courses and death related to COVID-19. Second, they may struggle particularly with COVID-19 restrictions due to their dependence on social support. Therefore, we investigated the association of intensity of COVID-19 restrictions with levels of COVID-19-related distress, mental health (depression, anxiety, somatic symptom disorder, and mental quality of life), and perceived social support among Swiss general hospital non-COVID-19 inpatients.MethodsWe analyzed distress of 873 hospital inpatients not admitted for COVID-19, recruited from internal medicine, gynecology, rheumatology, rehabilitation, acute geriatrics, and geriatric rehabilitation wards of three hospitals. We assessed distress due to the COVID-19 pandemic, and four indicators of mental health: depressive and anxiety symptom severity, psychological distress associated with somatic symptoms, and the mental component of health-related quality of life; additionally, we assessed social support. The data collection period was divided into modest (June 9 to October 18, 2020) and strong (October 19, 2020, to April 17, 2021) COVID-19 restrictions, based on the Oxford Stringency Index for Switzerland.ResultsAn additional 13% (95%-Confidence Interval 4–21%) and 9% (1–16%) of hospital inpatients reported distress related to leisure time and loneliness, respectively, during strong COVID-19 restrictions compared to times of modest restrictions. There was no evidence for changes in mental health or social support.ConclusionsFocusing on the vulnerable population of general hospital inpatients not admitted for COVID-19, our results suggest that tightening of COVID-19 restrictions in October 2020 was associated with increased COVID-19-related distress regarding leisure time and loneliness, with no evidence for a related decrease in mental health. If this association was causal, safe measures to increase social interaction (e.g., virtual encounters and outdoor activities) are highly warranted.Trial registrationwww.ClinicalTrials.gov, identifier: NCT04269005.

Analysis of the relationships between COVID-19 restrictions, homeworking and spending, comparison of these variables: percentage of homeworkers, Google Workplace Mobility Index, Oxford Stringency Index and CHAPS spending.

Annual number of ED visits, total and by hospital.