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 November 2020. Vaccination programs were implemented from December 2020, and the French government introduced separate restrictions for vaccinated and non-vaccinated persons from June 2021. These separate restrictions stayed in place for just over one year, until August 2022.

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

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.

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

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 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.

Nach Berechnungen der Oxford University hat China bis zum 31. Dezember 2022 (oder den aktuellsten verfügbaren Werten) einige der weltweit striktesten Maßnahmen zur Eindämmung des Coronavirus erlassen. Mit 74,07 von 100 Indexpunkten rangierte Pakistan auf dem ersten Platz. Der Oxford Covid-19 Government Response Tracker (OxCGRT) sammelt systematische Informationen über politische Maßnahmen, die Regierungen zur Bekämpfung von COVID-19 ergriffen haben. Die verschiedenen politischen Maßnahmen werden seit dem 1. Januar 2020 verfolgt, decken mehr als 180 Länder ab und werden in 23 Indikatoren kodiert, wie z. B. Schulschließungen, Reisebeschränkungen, Impfpolitik. Diese Maßnahmen werden auf einer Skala erfasst, die das Ausmaß der Regierungsmaßnahmen widerspiegelt, und die Ergebnisse werden zu einer Reihe von Politikindizes zusammengefasst. Die Indizes werden dann zu einer einzigen Zahl zwischen 0 und 100 zusammenfassen (stringency Index). Dies ist ein Maß dafür, wie viele der relevanten Indikatoren eine Regierung umgesetzt hat und in welchem Umfang. Der Index sagt nichts darüber aus, ob die Politik einer Regierung wirksam umgesetzt wurde.

Weltweit beläuft sich die kumulative Zahl bestätigter SARS CoV-2-Infektionen derzeit auf mehr als 678 Millionen. Die Zahl der Todesopfer im Zusammenhang mit dem Virus beläuft sich aktuell auf mehr als 6,7 Millionen.

Wo nahm der Corona-Ausbruch seinen Anfang?

Am 31. Dezember 2019 wurde das WHO-Länderbüro China über Fälle von Lungenentzündung unbekannter Ätiologie informiert, die in der Millionenmetropole Wuhan in der Provinz Hubei festgestellt wurden. Ein neuartiges Coronavirus (SARS-CoV-2) wurde am 7. Januar von den chinesischen Behörden als das verursachende Virus identifiziert. Ursprünglicher Infektionsort war der Wuhaner Großhandelsmarkt für Fische und Meeresfrüchte, von wo sich das Virus binnen weniger Wochen erst in den Nachbarländern und dann über die ganze Welt ausbreitete. Am 11. März 2020 schließlich erklärte die WHO den Corona-Ausbruch zur globalen Pandemie. Drei Jahre später, zu Beginn des Jahres 2023 steht die Pandemie vielerorts an der Schwelle, sich zu einer Endemie zu entwickeln. Zwar stuft die WHO die Corona-Pandemie nach wie vor als globalen Notfall ein, die meisten Länder haben jedoch den überwiegenden Teil oder alle Corona-Schutzmaßnahmen aufgehoben.

Was sind Coronaviren?

Coronaviren (CoV) sind eine unter Säugetieren und Vögeln weit verbreitete Virusfamilie. Beim Menschen verursachen sie vorwiegend milde Erkältungskrankheiten, können aber mitunter schwere Lungenentzündungen hervorrufen und gar zum Tod führen. Coronaviren sind genetisch hochvariabel, und einzelne Virusspezies können durch Überwindung der Artenbarriere auch mehrere Wirtspezies infizieren. Durch solche Artübertritte sind beim Menschen unter anderem Infektionen mit dem SARS-assoziierten Coronavirus (SARS-CoV) sowie mit dem 2012 neu aufgetretenen Middle East respiratory syndrome coronavirus (MERS-CoV) entstanden. Auch die Corona-Pandemie 2019-2023 wurde durch ein neuartiges Coronavirus, dem SARS-CoV-2-Erreger, ausgelöst. Die durch diesen hervorgerufene Erkrankung erhielt den Namen COVID-19.

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.