Context

During COVID-19, different countries have put containment measures that include lockdowns and restrictive movement. Oxford University has done a research and created a numerical index which statistically measures the intensity of a restrictive measure.

Content

This is a time-series data of different countries representing measure of stringency on each day for the period of Jan-April 2020.

Acknowledgements

This dataset is a part of Oxford University research.

Inspiration

This data was used in the understanding of effect of countermeasures against the spread of COVID-19.

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 ***********.

The Oxford COVID-19 Government Response Tracker (OxCGRT) aims to track and compare government responses to the coronavirus outbreak worldwide rigorously and consistently. This is done through collecting data on multiple policy indicators and scoring the stringency of such measures,. The policy indicators can be broadly categorized into: containment and closure policies, economic policies, health system policies, vaccination policies, and miscellaneous policies.

Context

COVID-19 The Government Response Stringency Index

The Government Response Stringency Index is a composite measure based on nine response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response).

Content

OxCGRT collects publicly available information on indicators of government response. These indicators take policies such as school closures, travel bans, etc. and record them on an ordinal scale; the remainder are financial indicators such as fiscal or monetary measures.

OxCGRT measures the variation in governments’ responses using its 'COVID-19 Government Response Stringency Index (Stringency Index)'. This composite measure is a simple additive score of nine indicators measured on an ordinal scale, rescaled to vary from 0 to 100. Please note that this measure is for comparative purposes only, and should not necessarily be interpreted as a rating of the appropriateness or effectiveness of a country's response.

Acknowledgements

Data published by Thomas Hale, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government. https://www.bsg.ox.ac.uk/research/research-projects/oxford-covid-19-government-response-tracker

Photo by Étienne Godiard on Unsplash

Inspiration

BCG - COVID-19 AI Challenge Improve BCG Data and Provide Insights to "BCG - COVID-19" Clinical Trials

The OxCGRT systematically collects information on several different common policy responses national governments have taken in response to the COVID-19 pandemic, scores the stringency of such measures, and aggregates these scores into a common Stringency Index. Eleven indicators of government response are provided; seven indicators are policies such as school closures and travel bans, and four are financial indicators such as fiscal or monetary measures. Data are collected from public sources by a team of dozens of Oxford University students and staff from every part of the world.

This paper presents a novel dataset of non-pharmaceutical interventions adopted by Italian authorities to tackle the COVID-19 pandemic at the national and local levels. The dataset follows the structure of the Oxford Coronavirus Government Response Tracker (OxCGRT; Hale et al. in Nat Human Behav 5:529–538, https://doi.org/10.1038/s41562-021-01079-8, 2021)). We include several novelties with respect to the original source. First, we tailor the classification of provisions to the measures adopted in Italy. Second, we collect detailed information on local restrictions in the country, including lockdowns and school closures. Third, we apply a bottom-up approach to construct population-weighted average stringency indexes (Italian Stringency Indexes, ItSIs) at the provincial, regional, and country-wide levels. While expanding the geographical coverage of the stringency indicators, we preserve the comparability of the ItSIs with the original stringency index published in the OxCGRT. As an application, we show that the correlations of our ItSI with community mobility indicators and various measures of economic activity are higher than those obtained with the OxCGRT indicator.

Difference of mobility changes in the countries with high or low stringency index.

This dataset evaluated 17 indicators of government response to COVID-19 to produce an aggregate Stringency Index score calculated by a working group at Oxford University. The indicators assess policies related to containment and closure, the economy, health systems, and vaccination policies. Data was collected between 2020 and 2022 via public sources by faculty, students, and other affiliates of Oxford University. Versions of the dataset available after July 27, 2022 differentiate policies which apply to vaccinated versus unvaccinated populations.

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

• Percentages over the country's population:

  • confirmed_PopPct
  • deaths_PopPct
  • recoveries_PopPct

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:

Context

Information on Lockdown dates is a very important aspect in determining the shape of the trend of CVD19 confirmed cases. The enforcement of lockdown is done very differently by each country. A Stringency index was calculated by University of Oxford, that tries to bring the efforts on lockdown to a comparable ground. Moreover, the timeline of Stringency makes it very useful to compare it with rate of increase of Confirmed CVD19 cases.

Content

Has a daily status on changes to Stringency index and flags for incidents like shutdown of schools, public transportation etc.

Acknowledgements

Blavatnik School of Government, University of Oxford

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.

Descriptive statistics by jurisdiction, exclusion/elimination strategy, and GBD region for the 2020 to 2021 period (note ‘high-income’ region consists of several high-income countries across different parts of the world, ‘level 4’ is the maximum Oxford Stringency Index border restriction).

Introduction

This page comprises the additional datasets used for the COVID-19 Global Forecasting Challenge (currently in week 3). Only datasets that have not been hosted on Kaggle will be uploaded here: * Oxford COVID-19 Government Response Tracker * Assessment Capacities Project COVID-19 Government Measures

UPDATE: Please see my notebook on the COVID-19 Global Forecasting Challenge (Week 3) competition here for merging the data.

Oxford COVID-19 Government Response Tracker

The Oxford COVID-19 Government Response Tracker (OxCGRT) provides a systematic cross-national, cross-temporal measure to understand how government responses have evolved over the full period of the disease’s spread. The project tracks governments’ policies and interventions across a standardized series of indicators and creates a composite index to measure the stringency of these responses. Data is collected and updated in real time by a team of dozens of students and staff at Oxford University. Read the white paper here. Access the OxCGRT website here.

Indicators

The OxCGRT tracks 11 indicators of government response:

1. School closing (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend closing
     • 2 = Require closing
2. Workplace closing (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend closing
     • 2 = Require closing
3. Cancel public events (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend cancelling
     • 2 = Require cancelling
4. Close public transport (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend closing
     • 2 = Require closing
5. Public info campaigns(with geographic scope)
   • Ordinal scale:
     • 0 = No COVID-10 public information campaign
     • 1 = COVID-10 public information campaign
6. Restrictions on internal movement (with geographic scope)
   • Ordinal scale:
     • 0 = No measures
     • 1 = Recommend movement restriction
     • 2 = Restricted movement
7. International travel controls
   • Ordinal scale:
     • 0 = No measures
     • 1 = Screening
     • 2 = Quarantine on high-risk regions
     • 3 = Ban on high-risk regions
8. Fiscal measures
   • Value of fiscal stimuli in USD
9. Monetary measures
   • Value of interest rate in %
10. Emergency investment in health care
    • Value of new short-term spending on health in USD
11. Investment in vaccines
    • Value of investment in USD

Indicators with geographic scope are coded in the following way: - 0 = Targeted - 1 = General

The Assessment Capacities Project (ACAPS) COVID-19 Government Measures Dataset

This dataset comprises government measures and descriptions of these measures by country and date. The measures include:

1. Social distancing
2. Movement restrictions
3. Public health measures
4. Social and economic measures
5. Lockdowns

Descriptors of these measures include: - Date of implementation - Specific measure - Penalties for non-compliance - Source (e.g. government, media)

Correlations between border restrictions and outcomes by jurisdiction type (only for the 159 (83%) of jurisdictions that enacted the highest level of border restrictions, i.e., Oxford Stringency Index ‘level 4’).

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.

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.