Context

Different states in United States have different lockdown policy. I found this nice summary of the state action from https://www.ncsl.org that might be useful to those who like to investigate how different lockdown policies can help in flattening the curve. I cleaned up the dataset (like fill the null values, etc) without alternating any information and thus all the 'No' in the state can also be interpreted as NaN.

Content

Specifically, this dataset summarizes if each state (and US territories) perform the following actions (column of the dataset), as of April 10, 2020: 1. Emergency Declaration 2. Major Disaster Declaration 3. National Guard State Activation 4. State Employee Travel Restrictions 5. Statewide Limits on Gatherings and Stay at Home Orders 6. Statewide School Closures
7. Statewide Closure of Non-Essential Businesses
8. Statewide Closure of Some or All Non-Essential Businesses
9. Essential Business Designations Issued
10. Statewide Curfew
11. 1135 Waiver Status
12. Extension of Individual Income Tax Deadlines
13. Primary Election
14. Domestic Travel Limitations 15. Statewide Mask Policy
16. Ventilator Sharing

Acknowledgements

Again, this dataset was found from National Conference of State Legislatures website.

P.S.: Hope that this dataset is useful/helpful in understanding the impact of different lockdown policy.

Experimental results of the pilot Office for National Statistics (ONS) online time-use study (collected 28 March to 26 April 2020 across Great Britain) compared with the 2014 to 2015 UK time-use study.

A survey from April 2020 showed that during the coronavirus (COVID-19) lockdown nearly half of Italian people spent more time streaming audio and video content on YouTube. This user behavior was spread more among male respondents than among female ones, as 14 percent of male adults spent much more time on YouTube, while only 12 percent of female adults did the same.

In recent years behavioural science has quickly become embedded in national level governance. As the contributions of behavioural science to the UK's COVID-19 response policies in early 2020 became apparent, a debate emerged in the British media about its involvement. This served as a unique opportunity to capture public discourse and representation of behavioural science in a fast-track, high-stake context. We aimed at identifying elements which foster and detract from trust and credibility in emergent scientific contributions to policy making. With this in mind, in Study 1 we use corpus linguistics and network analysis to map the narrative around the key behavioural science actors and concepts which were discussed in the 647 news articles extracted from the 15 most read British newspapers over the 12-week period surrounding the first hard UK lockdown of 2020. We report and discuss (1) the salience of key concepts and actors as the debate unfolded, (2) quantified changes in the polarity of the sentiment expressed toward them and their policy application contexts, and (3) patterns of co-occurrence via network analyses. To establish public discourse surrounding identified themes, in Study 2 we investigate how salience and sentiment of key themes and relations to policy were discussed in original Twitter chatter (N = 2,187). In Study 3, we complement these findings with a qualitative analysis of the subset of news articles which contained the most extreme sentiments (N = 111), providing an in-depth perspective of sentiments and discourse developed around keywords, as either promoting or undermining their credibility in, and trust toward behaviourally informed policy. We discuss our findings in light of the integration of behavioural science in national policy making under emergency constraints.

Exploring the social impacts on behaviours during the different lockdown periods of the coronavirus (COVID-19) pandemic in the UK. Data are from March 2020 to January 2021.

A survey from April 2020 showed that during the coronavirus (COVID-19) lockdown a significant share of Italian people spent more time streaming audio and video content. However, male respondents reported a more significant behavior change. 17 percent of male adults dedicated much more time to audio and video streaming, while only 13 percent of female respondents did the same.

Vaccinations in London Between 8 December 2020 and 15 September 2021 5,838,305 1st doses and 5,232,885 2nd doses have been administered to London residents.

Differences in vaccine roll out between London and the Rest of England London Rest of England Priority Group Vaccinations given Percentage vaccinated Vaccinations given Percentage vaccinated Group 1 Older Adult Care Home Residents 21,883 95% 275,964 96% Older Adult Care Home Staff 29,405 85% 381,637 88% Group 2 80+ years 251,021 83% 2,368,284 93% Health Care Worker 174,944 99% 1,139,243 100%* Group 3 75 - 79 years 177,665 90% 1,796,408 99% Group 4 70 - 74 years 252,609 90% 2,454,381 97% Clinically Extremely Vulnerable 278,967 88% 1,850,485 95% Group 5 65 - 69 years 285,768 90% 2,381,250 97% Group 6 At Risk or Carer (Under 65) 983,379 78% 6,093,082 88% Younger Adult Care Home Residents 3,822 92% 30,321 93% Group 7 60 - 64 years 373,327 92% 2,748,412 98% Group 8 55 - 59 years 465,276 91% 3,152,412 97% Group 9 50 - 54 years 510,132 90% 3,141,219 95% Data as at 15 September 2021 for age based groups and as at 12 September 2021 for non-age based groups * The number who have received their first dose exceeds the latest official estimate of the population for this group There is considerable uncertainty in the population denominators used to calculate the percentage vaccinated. Comparing implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following datasets can be used to estimate vaccine uptake by age group for London:

ONS 2020 mid-year estimates (MYE). This is the population estimate used for age groups throughout the rest of the analysis.


Number of people ages 18 and over on the National Immunisation Management Service (NIMS)


ONS Public Health Data Asset (PHDA) dataset. This is a linked dataset combining the 2011 Census, the General Practice Extraction Service (GPES) data for pandemic planning and research and the Hospital Episode Statistics (HES). This data covers a subset of the population.

Vaccine roll out in London by Ethnic Group Understanding how vaccine uptake varies across different ethnic groups in London is complicated by two issues:

Ethnicity information for recipients is unavailable for a very large number of the vaccinations that have been delivered. As a result, estimates of vaccine uptake by ethnic group are highly sensitive to the assumptions about and treatment of the Unknown group in calculations of rates.

For vaccinations given to people aged 50 and over in London nearly 10% do not have ethnicity information available,

The accuracy of available population denominators by ethnic group is limited. Because ethnicity information is not captured in official estimates of births, deaths, and migration, the available population denominators typically rely on projecting forward patterns captured in the 2011 Census. Subsequent changes to these patterns, particularly with respect to international migration, leads to increasing uncertainty in the accuracy of denominators sources as we move further away from 2011.

Comparing estimated population sizes and implied vaccination rates for multiple sources of denominators provides some indication of uncertainty in the true values. Confidence is higher where the results from multiple sources agree more closely. Because the denominator sources are not fully independent of one another, users should interpret the range of values across sources as indicating the minimum range of uncertainty in the true value. The following population estimates are available by Ethnic group for London:

GLA Ethnic group population projections - 2016 as at 2021


ONS Population Denominators produced for Race Disparity Audit as at 2018


ETHPOP population projections produced by the University of Leeds as at 2020

Antibody prevalence estimates As part of the ONS Coronavirus (COVID-19) Infection Survey ONS publish a modelled estimate of the percent of the adult population testing positive for antibodies to Coronavirus by region. Antibodies can be generated by vaccination or previous infection.

Vaccine effects on cases, hospitalisations and deaths When the vaccine roll out began in December 2020 coronavirus cases, hospital admissions and deaths were rising steeply. The peak of infections came in London in early January 2021, before reducing during the national lockdown and as the vaccine roll out progressed. As the vaccine roll out began in older age groups the effect of vaccinations can be separated from the effect of national lockdown by comparing changes in cases, admissions and deaths

Provisional counts of the number of deaths and annualised age-standardised mortality rates involving the coronavirus (COVID-19) by major occupations, where the infection may have been acquired either before or during the period of lockdown. The deaths have been registered in England and Wales. Figures are provided for males and females.

Abstract

The National Income Dynamics Study - Coronavirus Rapid Mobile Survey 2020 investigates the socioeconomic impacts of the national lockdown associated with the State of Disaster declared in South Africa in March 2020, and the social and economic consequences in South Africa of the global Coronavirus pandemic. NIDS-CRAM forms part of a broader study called the Coronavirus Rapid Mobile Survey (CRAM) which aims to inform policy using rapid reliable research on income, employment and welfare in South Africa, in the context of the global Coronavirus pandemic. The study is run by researchers from the University of Stellenbosch, University of Cape Town (UCT) and University of the Witwatersrand (Wits). The NIDS-CRAM survey data collection and production operations were implemented by the Southern Africa Labour and Development Research Unit (SALDRU) at UCT. The data is collected with Computer Assisted Telephone Interviewing (CATI), with data collection repeated over several months.

Geographic coverage

National coverage, as NIDS was only designed to be nationally representative, it is inadvisable to use the NIDS-CRAM data to calculate provincial or regional totals.

Analysis unit

Households and individuals

Universe

The universe of the study is South Africans 18 years old or older.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame for NIDS-CRAM is the NIDS Wave 5 CSMs and TSMs who were 18 years or older at the time of the NIDS-CRAM Wave 1 fieldwork preparation in April 2020. The sample was drawn using a stratified sampling design. No attempt was made to check whether successfully re-interviewed individuals resided in the same households as they did in Wave 5. In the survey, individuals from larger households were more likely to be sampled than individuals from smaller households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Though NIDS-CRAM is a follow-up with NIDS Wave 5 respondents, the NIDS-CRAM survey uses a much shorter questionnaire, with a focus on the Coronavirus pandemic and the national lockdown. The questionnaire was changed slightly across waves and data users should check the questionnaires for each wave when using the data.

The questionnaire consists of the following sections: - Identification - Background information - Labor and income - Household and social outcomes - Health and COVID-19 - Interviewer evaluation

Data set relating to the publication 'COVID-19: Self-Reported Reductions in Physical Activity and Increases in Sedentary Behaviour During the First National Lockdown in the United Kingdom'.

This file contains workforce absence statistics for education settings from 12 October 2020 to 11 March 2021 December. It excludes half term terms (19th October - 23rd October, and 2nd November 2020) and the national lockdown during the spring term (4 January to 5 March 2021). Data for workforce during the restricted opening of schools can be found in table 1e.Data is in this file has been scaled to account for non-response so it is nationally representative.

Abstract

The National Income Dynamics Study - Coronavirus Rapid Mobile Survey 2020 investigates the socioeconomic impacts of the national lockdown associated with the State of Disaster declared in South Africa in March 2020, and the social and economic consequences in South Africa of the global Coronavirus pandemic. NIDS-CRAM forms part of a broader study called the Coronavirus Rapid Mobile Survey (CRAM) which aims to inform policy using rapid reliable research on income, employment and welfare in South Africa, in the context of the global Coronavirus pandemic. The study is run by researchers from the University of Stellenbosch, University of Cape Town (UCT) and University of the Witwatersrand (Wits). The NIDS-CRAM survey data collection and production operations were implemented by the Southern Africa Labour and Development Research Unit (SALDRU) at UCT. The data is collected with Computer Assisted Telephone Interviewing (CATI), with data collection repeated over several months.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The universe of the study is South Africans 18 years old or older.

Kind of data

Sample survey data

Sampling procedure

The sample frame for NIDS-CRAM is the NIDS Wave 5 CSMs and TSMs who were 18 years or older at the time of the NIDS-CRAM Wave 1 fieldwork preparation in April 2020. The sample was drawn using a stratified sampling design. No attempt was made to check whether successfully re-interviewed individuals resided in the same households as they did in Wave 5. In the survey, individuals from larger households were more likely to be sampled than individuals from smaller households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Though NIDS-CRAM is a follow-up with NIDS Wave 5 respondents, the NIDS-CRAM survey uses a much shorter questionnaire, with a focus on the Coronavirus pandemic and the national lockdown. The questionnaire was changed slightly across waves and data users should check the questionnaires for each wave when using the data.

The time spent on the app or desktop version of YouTube increased among almost half of Italian individuals during the coronavirus (COVID-19) lockdown. The results of a survey conducted April 2020 showed that the increase was significant among 18-34-year olds, as well as among 35-44-year olds (18 percent).

Difference-in-differences estimates for lockdown-, family planning-, and fertility related search terms.

Studies examining factors responsible for COVID-19 incidence are mostly focused at the national or sub-national level. A global-level characterization of contributing factors and temporal trajectories of disease incidence is lacking. Here we conducted a global-scale analysis of COVID-19 infections to identify key factors associated with early disease incidence. Additionally, we compared longitudinal trends of COVID-19 incidence at a per-country level and classified countries based on COVID-19 incidence trajectories and effects of lockdown responses. Univariate analysis identified eleven variables as independently associated with COVID-19 infections at a global level (p<1e-05). Multivariable analysis identified a 4-variable model as optimal for explaining global variations in COVID-19 (p<0.01). COVID-19 case trajectories for most countries were best captured by a log-logistic model, as determined by AIC estimates. Six predominant country clusters were identified when characterizing the effects of lockdown intervals on variations in COVID-19 new cases per country. Globally, economic and meteorological factors are important determinants of early COVID-19 incidence. Analysis of longitudinal trends and lockdown effects on COVID-19 highlights important nuances in country-specific responses to infections. These results provide valuable insights into disease incidence at a per-country level, possibly allowing for more informed decision making by individual governments in future disease outbreaks. Methods Data for COVID-19 confirmed cases was obtained from https://ourworldindata.org/coronavirus-source-data, which is updated daily and based on data on confirmed cases and deaths from Johns Hopkins University. Data on additional demographic, meteorological, health or economic variables were downloaded from a variety of sources online. For each variable, values from the most recent year for which data on the greatest number of countries were available were utilized (varied between 2016-2019). Variables were categorized as Demographic, Meterological, Health or Economic domains. Please see the README document ("README_data_COVID19_112322.txt") and the accompanying published article: Ghosh, S., Roy, S.S. Global-scale modeling of early factors and country-specific trajectories of COVID-19 incidence: a cross-sectional study of the first 6 months of the pandemic. BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12889-022-14336-w

This dataset contains key characteristics about the data described in the Data Descriptor COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format