As of November 11, 2022, almost 96.8 million confirmed cases of COVID-19 had been reported by the World Health Organization (WHO) for the United States. The pandemic has impacted all 50 states, with vast numbers of cases recorded in California, Texas, and Florida.

The coronavirus in the U.S. The coronavirus hit the United States in mid-March 2020, and cases started to soar at an alarming rate. The country has performed a high number of COVID-19 tests, which is a necessary step to manage the outbreak, but new coronavirus cases in the U.S. have spiked several times since the pandemic began, most notably at the end of 2022. However, restrictions in many states have been eased as new cases have declined.

The origin of the coronavirus In December 2019, officials in Wuhan, China, were the first to report cases of pneumonia with an unknown cause. A new human coronavirus – SARS-CoV-2 – has since been discovered, and COVID-19 is the infectious disease it causes. All available evidence to date suggests that COVID-19 is a zoonotic disease, which means it can spread from animals to humans. The WHO says transmission is likely to have happened through an animal that is handled by humans. Researchers do not support the theory that the virus was developed in a laboratory.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

What impact has the COVID-19 pandemic had on Italians' attitudes, opinions, and behaviors? From this question, the ResPOnsE COVID-19 project (Response of Italian Public Opinion to the COVID-19 Emergency) was developed starting in March 2020, with the aim of building a research infrastructure for the daily monitoring of public opinion during the COVID-19 emergency. The collection of daily information through online interviews (CAWI) to a sample reflecting the distribution of the Italian population by gender and area of residence was divided into four surveys that took place between April 2020 and December 2021, for a total of more than 30,000 interviews. The infrastructure was designed by the spsTREND "Hans Schadee" laboratory in collaboration with the SWG institute, as part of the "Departments of Excellence 2018-2022" project promoted by the Ministry of University and Research and is supported by funding from the Cariplo Foundation. Overall Research Design The research design included four surveys (waves) following a repeated cross-sectional design, consistent with the dynamic nature of the pandemic phenomenon. The four waves of ResPOnsE COVID-19 are distributed as follows. First wave: from April 6 to July 6, 2020 (~15000 cases, RR=46,6%) Second wave: from December 21, 2020 to January 2, 2021 (~3000 cases, RR=47%) Third wave: from March 17 to June 16, 2021 (~9300 cases, RR=76.9%) Fourth wave: from November 10 to December 22, 2021 (~3000 cases, RR=67.1%) Rolling Cross-Section and Panel Design The first, third, and fourth waves collect interviews through a Rolling Cross-Section (RCS) design, that is consecutive daily samples for a relatively long period (in this case 2 to 3 months). In addition, about 60% of subjects were interviewed twice between the first and third or fourth wave, thus allowing longitudinal analysis of intra-individual variations that occurred between 2020 and 2021. An RCS survey can be viewed as a cross-sectional survey of a single sample that is, however, "sliced" into many equivalent small subgroups that are released on consecutive days. On the day of release, individuals belonging to a particular sub-group are invited to participate in the survey. The distinguishing feature of the RCS design, however, is that these individuals can also respond in the days following the delivery of the invitation. Hence comes the term "rolling" meaning that the overall sample "rolls" through the days of the survey, making time (days) a random variable. The daily samples are mutually independent and the estimates derived for each are comparable. In this way, the RCS design is optimal for studying trends in the case of time-varying phenomena. For details, see the articles by Vezzoni et al. (2020) and Biolcati et al. (2021). Questionnaire structure The questionnaire administered in the ResPOnsE COVID-19 survey consists of a main questionnaire, containing a core set of questions repeated in each of the four surveys, and one or more thematic modules that may change with each survey. The main questionnaire consists of eleven thematic sections covering the entire survey period. Most of the questions in the questionnaire were repeated in the four surveys, while some questions were eliminated/changed or new ones were introduced in the transition to a new survey. Covering the entire survey period, the basic module is particularly suitable for diachronic analysis, while the structure of the thematic modules, usually collected over a few weeks, suggests an analysis of them with a cross-sectional approach. Source questionnaires in Italian are available for download. The sample The target population consists of Italian residents aged 18 years and older. In the RCS waves, on average, between 100 and 150 interviews were conducted each day, corresponding to about 1,000 interviews per week for the first survey and about 700 for the third and fourth surveys (the interviews in the second survey were actually concentrated in a single week), for a total of 31,122 interviews. Given time and resource constraints, probabilistic sampling could not be used. Instead, the samples are drawn from an online community of a commercial research institute (SWG SpA). To correct against expected bias, the sample is stratified by ISTAT macro-area of residence and composed of quotas defined by gender and age. Weights have also been created for carryover to the population. Detailed instructions on using the weights can be downloaded together with the data files. The survey also includes a panel component: about 60 percent of subjects were interviewed twice between the first, third, and fourth waves. Over-sampling was also conducted for the Lombardy region, for which 1124 additional cases are available in the third wave Macro level data The cumulative data file also includes official macro-level variables capturing daily information on the health emergency, such as the number of people infected by...

New Covid tests per thousand people in Nigeria, January, 2021 The most recent value is 2 new Covid tests per thousand people as of January 2021, an increase compared to the previous value of 1 new Covid tests per thousand people. Historically, the average for Nigeria from April 2020 to January 2021 is 1 new Covid tests per thousand people. The minimum of 0 new Covid tests per thousand people was recorded in April 2020, while the maximum of 2 new Covid tests per thousand people was reached in January 2021. | TheGlobalEconomy.com

Understanding Society, (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

Understanding Society (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex and the survey research organisations Kantar Public and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

The Understanding Society COVID-19 Study, 2020-2021 is a regular survey of households in the UK. The aim of the study is to enable research on the socio-economic and health consequences of the COVID-19 pandemic, in the short and long term. The surveys started in April 2020 and took place monthly until July 2020. From September 2020 they took place every other month until March 2021 and the final wave was fielded in September 2021. They complement the annual interviews of the Understanding Society study. The data can be linked to data on the same individuals from previous waves of the annual interviews (SN 6614) using the personal identifier pidp. However, the most recent pre-pandemic (2019) annual interviews for all respondents who have taken part in the COVID-19 Study are included as part of this data release. Please refer to the User Guide for further information on linking in this way and for geographical information options.

Latest edition information

For the eleventh edition (December 2021), revised April, May, June, July, September, November 2020, January 2021 and March 2021 data files for the adult survey have been deposited. These files have been amended to address issues identified during ongoing quality assurance activities. All documentation has been updated to explain the revisions, and users are advised to consult the documentation for details. In addition new data from the September 2021 web survey have been deposited.

The number of daily new COVID-19 cases started to decline across Europe from the start of April 2020. However, infections continued to increase in the Americas, and the World Health Organization (WHO) identified the region as the new epicenter of the pandemic toward the end of May 2020.

Soaring demand for critical health care supplies Health systems around the world have been overwhelmed because of the coronavirus. Hospitals have reached capacity and health workers have been redirected to care for critical COVID-19 patients. Demand for test kits, respirators, and personal protective equipment (PPE) has led to global shortages of life-saving supplies. The WHO had shipped 131 million units of medical PPE – face masks, goggles, gloves, and gowns – to nearly 150 countries as of August 10, 2020.

Russia claim vaccine prestige Since the start of the pandemic, there has been an urgent need to accelerate the development of COVID-19 treatments. As of August 13, 2020, there are 29 candidate vaccines under clinical evaluation around the world, according to the WHO. One of those vaccines is being developed by the Gamaleya Research Institute of Epidemiology and Microbiology in Moscow. Russian President Vladimir Putin granted the vaccine regulatory approval in mid-August, and it is expected to enter civilian circulation in January 2021.

BackgroundLong-term health conditions can affect labour market outcomes. COVID-19 may have increased labour market inequalities, e.g. due to restricted opportunities for clinically vulnerable people. Evaluating COVID-19’s impact could help target support.AimTo quantify the effect of several long-term conditions on UK labour market outcomes during the COVID-19 pandemic and compare them to pre-pandemic outcomes.MethodsThe Understanding Society COVID-19 survey collected responses from around 20,000 UK residents in nine waves from April 2020-September 2021. Participants employed in January/February 2020 with a variety of long-term conditions were matched with people without the condition but with similar baseline characteristics. Models estimated probability of employment, hours worked and earnings. We compared these results with results from a two-year pre-pandemic period. We also modelled probability of furlough and home-working frequency during COVID-19.ResultsMost conditions (asthma, arthritis, emotional/nervous/psychiatric problems, vascular/pulmonary/liver conditions, epilepsy) were associated with reduced employment probability and/or hours worked during COVID-19, but not pre-pandemic. Furlough was more likely for people with pulmonary conditions. People with arthritis and cancer were slower to return to in-person working. Few effects were seen for earnings.ConclusionCOVID-19 had a disproportionate impact on people with long-term conditions’ labour market outcomes.

BackgroundThe COVID-19 pandemic impacts different health aspects. Concomitant with the adoption of non-pharmaceutical interventions (NPIs) to reduce the spread of SARS-CoV-2, global surveillance studies reported a reduction in occurrence of respiratory pathogens like influenza A and B virus (IAV & IBV) and respiratory syncytial virus (RSV). We hypothesized to observe this collateral benefit on viral respiratory infection epidemiology in young children.MethodsRespiratory samples of children aged below 6 years, presenting at the outpatient clinic, emergency department, or pediatric infectious diseases department of the University Hospitals Leuven, between April 2017 and April 2021 were retrospectively analyzed. The occurrence (positivity rate), and seasonal patterns of viral respiratory infections were described. Chi-squared or Fisher's exact test (and Bonferroni correction) were used to explore differences in occurrence between 2020-2021 and previous 12-month (April to April) periods.ResultsWe included 3020 samples (453 respiratory panels, 2567 single SARS-CoV-2 PCR tests). IAV and IBV were not detected from March and January 2020, respectively. For IAV, positivity rate in 2020–2021 (0%, n = 0) was significantly different from 2018-2019 (12.4%, n = 17) (p < 0.001) and 2019-2020 (15.4%, n = 19) (p < 0.001). IBV positivity rate in 2020-2021 (0%, n = 0) was not significantly different from previous periods. RSV occurrence was significantly lower in 2020–2021 (3.2%, n = 3), compared to 2017-2018 (15.0%, n = 15) (p = 0.006), 2018–2019 (16.1%, n = 22) (p = 0.002) and 2019-2020 (22.8%, n = 28) (p < 0.001). The RSV (winter) peak was absent and presented later (March-April 2021). Positivity rate of parainfluenza virus 3 (PIV-3) was significantly higher in 2020-2021 (11.8%, n = 11) than 2017-2018 (1%, n = 1) (p = 0.002). PIV-3 was absent from April 2020 to January 2021, whereas no clear seasonal pattern was distinguished the other years. For the other viruses tested, no significant differences in occurrence were observed between 2020-2021 and previous periods. From March 2020 onwards, 20 cases (0.7%) of SARS-CoV-2 were identified.ConclusionThese findings reinforce the hypothesis of NPIs impacting the epidemiology of influenza viruses and RSV in young children. Compared to previous periods, no IAV and IBV cases were observed in the 2020-2021 study period, and the RSV peak occurred later. Since the pandemic is still ongoing, continuation of epidemiological surveillance, even on a larger scale, is indicated.

During the peak of the coronavirus (COVID-19) crisis (March-April 2020) when many countries worldwide introduced lockdown measures, e-commerce share in total retail sales saw proportions that were not seen before. In the United Kingdom, where an already mature e-commerce market exists, e-commerce share saw as high as **** percent, before stabilizing in the subsequent periods. In the most current period (as of January 31, 2021), United Kingdom, United States and Canada were the leading countries where e-commerce had a higher share as a proportion of total retail, at **, **, and ** percent, respectively.

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance.

Data were collected to determine when members of the public in states and territories were subject to state and territorial executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require them to wear masks in public. “Members of the public” are defined as individuals operating in a personal capacity. “In public” is defined to mean either (1) anywhere outside the home or (2) both in retail businesses and in restaurants/food establishments. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level.

These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require individuals to wear masks in public found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program, and Max Gakh, Assistant Professor, School of Public Health, University of Nevada, Las Vegas from April 10, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the dates provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not include data on counties that have opted out of their state mask mandate pursuant to state law. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Covid-19 pandemic in Switzerland. Map types: Charts, Choropleths. Spatial extent: Switzerland. Times: February 2020, March 2020, 1.3.2020, April 2020, 1.4.2020, May 2020, 1.5.2020, June 2020, 1.6.2020, July 2020, 1.7.2020, August 2020, 1.8.2020, September 2020, 1.9.2020, October 2020, 1.10.2020, November 2020, 1.11.2020, December 2020, 1.12.2020, January 2021, 1.1.2021, February 2021, 1.2.2021, March 2021, 1.3.2021, April 2021, 1.4.2021, May 2021, 1.5.2021, June 2021, 1.6.2021, July 2021, 1.7.2021, August 2021, 1.8.2021, September 2021, 1.9.2021, October 2021, 1.10.2021, November 2021, 1.11.2021, December 2021, 1.12.2021, January 2022, 1.1.2022, February 2022, 1.2.2022, 1.3.2022, March 2022, April 2022, 1.4.2022, 1.5.2022. Spatial unit: Cantons. Distinction: monthly, cumulative

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state estimates exclude New York City. Puerto Rico is included in HHS Region 2 estimates. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file. Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death. Death counts should not be compared across states. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly. The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York, New York City, Puerto Rico; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington. Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf). Rates are based on deaths occurring in the specified week/month and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly/monthly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly/monthly) rate prevailed for a full year. Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

New Covid tests per month in Nigeria, January, 2021 The most recent value is 328948 new Covid tests as of January 2021, an increase compared to the previous value of 189906 new Covid tests. Historically, the average for Nigeria from April 2020 to January 2021 is 126461 new Covid tests. The minimum of 19920 new Covid tests was recorded in April 2020, while the maximum of 328948 new Covid tests was reached in January 2021. | TheGlobalEconomy.com

The COVID-19 pandemic has had unprecedented effects on our daily lives. This study aimed to assess the quality of life (QoL) (WHOQOL-Bref physical, social, and environmental domains) at two time points during the COVID-19 pandemic with lockdown restrictions according to gender, age, and urbanization level. Qualtrics® recruited representative Austrian population samples in April 2020 (t1; N = 1,005) and December 2020/January 2021 (t2; N = 1,505). ANOVAs and the Bonferroni-corrected post-hoc tests were conducted to investigate differences between April and December 2020 and to compare with pre-pandemic data. Although the quality of life (physical, social, and environmental domains) changed from pre-pandemic (mean scores 80, 77, and 81, respectively) to April 2020 (mean scores 72, 65, and 75, all p-values < 0.001), there were no significant changes between April and December (mean scores 75, 65, and 75). Living location (urban vs. rural), gender, and age showed an effect on the quality of life. All domains of quality of life have decreased since the onset of the pandemic, and this decline has been maintained over the course of the first year of the pandemic. Creative measures should be implemented to assist people in improving one or more areas of quality of life, within the lockdown restrictions to improve the overall wellbeing of the population.

Descriptive statistics on nursing home to CBSA COVID-19 mortality rate ratio, June 2020—January 2021.

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance.

Data were collected to determine when members of the public in states and territories were subject to state and territorial executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require them to wear masks in public. “Members of the public” are defined as individuals operating in a personal capacity. “In public” is defined to mean either (1) anywhere outside the home or (2) both in retail businesses and in restaurants/food establishments. Data consists exclusively of state and territorial orders.

These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require individuals to wear masks in public found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program, and Max Gakh, Assistant Professor, School of Public Health, University of Nevada, Las Vegas from April 8, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the dates provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not include data on counties that have opted out of their state mask mandate pursuant to state law. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Objective Daily COVID-19 data reported by the World Health Organization (WHO) may provide the basis for political ad hoc decisions including travel restrictions. Data reported by countries, however, is heterogeneous and metrics to evaluate its quality are scarce. In this work, we analyzed COVID-19 case counts provided by WHO and developed tools to evaluate country-specific reporting behaviors. Methods In this retrospective cross-sectional study, COVID-19 data reported daily to WHO from 3rd January 2020 until 14th June 2021 were analyzed. We proposed the concepts of binary reporting rate and relative reporting behavior and performed descriptive analyses for all countries with these metrics. We developed a score to evaluate the consistency of incidence and binary reporting rates. Further, we performed spectral clustering of the binary reporting rate and relative reporting behavior to identify salient patterns in these metrics. Results Our final analysis included 222 countries and regions. Reporting scores varied between -0.17, indicating discrepancies between incidence and binary reporting rate, and 1.0 suggesting high consistency of these two metrics. Median reporting score for all countries was 0.71 (IQR 0.55 to 0.87). Descriptive analyses of the binary reporting rate and relative reporting behavior showed constant reporting with a slight “weekend effect” for most countries, while spectral clustering demonstrated that some countries had even more complex reporting patterns. Conclusion The majority of countries reported COVID-19 cases when they did have cases to report. The identification of a slight “weekend effect” suggests that COVID-19 case counts reported in the middle of the week may represent the best data basis for political ad hoc decisions. A few countries, however, showed unusual or highly irregular reporting that might require more careful interpretation. Our score system and cluster analyses might be applied by epidemiologists advising policymakers to consider country-specific reporting behaviors in political ad hoc decisions. Methods Data collection COVID-19 data was downloaded from WHO. Using a public repository, we have added the countries' full names to the WHO data set using the two-letter abbreviations for each country to merge both data sets. The provided COVID-19 data covers January 2020 until June 2021. We uploaded the final data set used for the analyses of this paper. Data processing We processed data using a Jupyter Notebook with a Python kernel and publically available external libraries. This upload contains the required Jupyter Notebook (reporting_behavior.ipynb) with all analyses and some additional work, a README, and the conda environment yml (env.yml).

SIECRT is a new dataset focused on public health measures adopted by 26 small affiliated island economies in their fight against the Covid-19 pandemic, between January 1, 2020, and April 31, 2021, with some exceptions extending until October 30, 2021. This dataset stands out from previous ones by focusing on affiliated island territories, often overlooked in other databases. It includes eight public health measures, such as restrictions on gatherings, educational establishments, and international travel. Each measure is coded daily.