This repository within the ACTIV TRACE initiative houses a comprehensive collection of datasets related to SARS-CoV-2. The processing of SARS-CoV-2 Sequence Read Archive (SRA) files has been optimized to identify genetic variations in viral samples. This information is then presented in the Variant Call Format (VCF). Each VCF file corresponds to the SRA parent-run's accession ID. Additionally, the data is available in the parquet format, making it easier to search and filter using the Amazon Athena Service. The SARS-CoV-2 Variant Calling Pipeline is designed to handle new data every six hours, with updates to the AWS ODP bucket occurring daily.

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 the first reported coronavirus case in Washington State on Jan. 21, 2020, 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.

As of May 2, 2023, there were roughly 687 million global cases of COVID-19. Around 660 million people had recovered from the disease, while there had been almost 6.87 million deaths. The United States, India, and Brazil have been among the countries hardest hit by the pandemic.

The various types of human coronavirus The SARS-CoV-2 virus is the seventh known coronavirus to infect humans. Its emergence makes it the third in recent years to cause widespread infectious disease following the viruses responsible for SARS and MERS. A continual problem is that viruses naturally mutate as they attempt to survive. Notable new variants of SARS-CoV-2 were first identified in the UK, South Africa, and Brazil. Variants are of particular interest because they are associated with increased transmission.

Vaccination campaigns Common human coronaviruses typically cause mild symptoms such as a cough or a cold, but the novel coronavirus SARS-CoV-2 has led to more severe respiratory illnesses and deaths worldwide. Several COVID-19 vaccines have now been approved and are being used around the world.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the death of millions of people worldwide and thousands more infected individuals developed sequelae due to the disease of the new coronavirus of 2019 (COVID-19). The development of several studies has contributed to the knowledge about the evolution of SARS-CoV2 infection and the disease to more severe forms. Despite this information being debated in the scientific literature, many mechanisms still need to be better understood in order to control the spread of the virus and treat clinical cases of COVID-19. In this article, we carried out an extensive literature review in order to bring together, in a single article, the biological, social, genetic, diagnostic, therapeutic, immunization, and even socioeconomic aspects that impact the SAR-CoV-2 pandemic. This information gathered in this article will enable a broad and consistent reading of the main aspects related to the current pandemic.

Main differences between COVID-19, SARS, and MERS.

A Coronavirus infectious disease that is characterized by fever, cough and shortness of breath and that has_material_basis_in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a subtype of Betacoronavirus pandemicum. url:https://www.who.int/emergencies/diseases/novel-coronavirus-2019

This pathway, SARS-CoV-2 infection of human cells (COVID-19), was initially generated via electronic inference from the manually curated and reviewed Reactome SARS-CoV-1 (Human SARS coronavirus) infection pathway. The inference process created SARS-CoV-2 events corresponding to each event in the SARS-CoV-1 pathway and populated those events with SARS-CoV-2 protein-containing physical entities based on orthology to SARS-CoV-1 proteins (https://reactome.org/documentation/inferred-events). All of these computationally created events and entities have been reviewed by Reactome curators and modified as appropriate where recently published experimental data indicate the existences of differences between the molecular details of the SARS-CoV-1 and SARS-CoV-2 infection pathways.

SARS‑CoV‑2 infection begins with the binding of viral S (spike) protein to cell surface angiotensin converting enzyme 2 (ACE2) and endocytosis of the bound virion. Within the endocytic vesicle, host proteases mediate cleavage of S protein into S1 and S2 fragments, leading to S2‑mediated fusion of the viral and host endosome membranes and release of the viral capsid into the host cell cytosol. The capsid is uncoated to free the viral genomic RNA, whose cap‑dependent translation produces polyprotein pp1a and, by means of a 1‑base frameshift, polyprotein pp1ab. Autoproteolytic cleavage of pp1a and pp1ab generates 15 or 16 nonstructural proteins (nsps) with various functions. Importantly, the RNA dependent RNA polymerase (RdRP) activity is encoded in nsp12. Nsp3, 4, and 6 induce rearrangement of the cellular endoplasmic reticulum membrane to form cytosolic double membrane vesicles (DMVs) where the viral replication transcription complex is assembled and anchored. With viral genomic RNA as a template, viral replicase‑transcriptase synthesizes a full length negative sense antigenome, which in turn serves as a template for the synthesis of new genomic RNA. The replicase‑transcriptase can also switch template during discontinuous transcription of the genome at transcription regulated sequences to produce a nested set of negative‑sense subgenomic (sg) RNAs, which are used as templates for the synthesis of positive‑sense sgRNAs that are translated to generate viral proteins. Finally, viral particle assembly occurs in the ER Golgi intermediate compartment (ERGIC). Viral M protein provides the scaffold for virion morphogenesis (Hartenian et al. 2020; Fung & Liu 2019; Masters 2006).

This Reactome module also describes molecular mechanisms by which SARS-CoV-2 modulates innate and adaptive immune responses, autophagy, host translation, intracellular signaling and regulatory pathways, and PDZ-mediated cell-cell junctions, mostly annotated from studies of cells infected with SARS-CoV-2.

2020

Reporting of new Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

Aggregate Data Collection Process Since the start of the COVID-19 pandemic, data have been gathered through a robust process with the following steps:

• A CDC data team reviews and validates the information obtained from jurisdictions’ state and local websites via an overnight data review process.
• If more than one official county data source exists, CDC uses a comprehensive data selection process comparing each official county data source, and takes the highest case and death counts respectively, unless otherwise specified by the state.
• CDC compiles these data and posts the finalized information on COVID Data Tracker.
• County level data is aggregated to obtain state and territory specific totals.

This process is collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provide the most up-to-date numbers on cases and deaths by report date. CDC may retrospectively update counts to correct data quality issues.

Methodology Changes Several differences exist between the current, weekly-updated dataset and the archived version:

• Source: The current Weekly-Updated Version is based on county-level aggregate count data, while the Archived Version is based on State-level aggregate count data.
• Confirmed/Probable Cases/Death breakdown:  While the probable cases and deaths are included in the total case and total death counts in both versions (if applicable), they were reported separately from the confirmed cases and deaths by jurisdiction in the Archived Version.  In the current Weekly-Updated Version, the counts by jurisdiction are not reported by confirmed or probable status (See Confirmed and Probable Counts section for more detail).
• Time Series Frequency: The current Weekly-Updated Version contains weekly time series data (i.e., one record per week per jurisdiction), while the Archived Version contains daily time series data (i.e., one record per day per jurisdiction).
• Update Frequency: The current Weekly-Updated Version is updated weekly, while the Archived Version was updated twice daily up to October 20, 2022.

Important note: The counts reflected during a given time period in this dataset may not match the counts reflected for the same time period in the archived dataset noted above. Discrepancies may exist due to differences between county and state COVID-19 case surveillance and reconciliation efforts.

Confirmed and Probable Counts In this dataset, counts by jurisdiction are not displayed by confirmed or probable status. Instead, confirmed and probable cases and deaths are included in the Total Cases and Total Deaths columns, when available. Not all jurisdictions report probable cases and deaths to CDC.* Confirmed and probable case definition criteria are described here:

Council of State and Territorial Epidemiologists (ymaws.com).

Deaths CDC reports death data on other sections of the website: CDC COVID Data Tracker: Home, CDC COVID Data Tracker: Cases, Deaths, and Testing, and NCHS Provisional Death Counts. Information presented on the COVID Data Tracker pages is based on the same source (total case counts) as the present dataset; however, NCHS Death Counts are based on death certificates that use information reported by physicians, medical examiners, or coroners in the cause-of-death section of each certificate. Data from each of these pages are considered provisional (not complete and pending verification) and are therefore subject to change. Counts from previous weeks are continually revised as more records are received and processed.

Number of Jurisdictions Reporting There are currently 60 public health jurisdictions reporting cases of COVID-19. This includes the 50 states, the District of Columbia, New York City, the U.S. territories of American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, Puerto Rico, and the U.S Virgin Islands as well as three independent countries in compacts of free association with the United States, Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau. New York State’s reported case and death counts do not include New York City’s counts as they separately report nationally notifiable conditions to CDC.

CDC COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths, available by state and by county. These and other data on COVID-19 are available from multiple public locations, such as:

https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html

https://www.cdc.gov/covid-data-tracker/index.html

https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

https://www.cdc.gov/coronavirus/2019-ncov/php/open-america/surveillance-data-analytics.html

Additional COVID-19 public use datasets, include line-level (patient-level) data, are available at: https://data.cdc.gov/browse?tags=covid-19.

Archived Data Notes:

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 3, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the Weekly United States COVID-19 Cases and Deaths by State data released on November 10, 2022, instead of the customary 7 days’ worth of data.

November 10, 2022: Per the request of the jurisdiction, cases and deaths among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case and death counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases and deaths. 

November 17, 2022: Two new columns, weekly historic cases and weekly historic deaths, were added to this dataset on November 17, 2022. These columns reflect case and death counts that were reported that week but were historical in nature and not reflective of the current burden within the jurisdiction. These historical cases and deaths are not included in the new weekly case and new weekly death columns; however, they are reflected in the cumulative totals provided for each jurisdiction. These data are used to account for artificial increases in case and death totals due to batched reporting of historical data.

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the data released on December 1, 2022.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case and death data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case and death metrics will appear higher than expected in the January 5, 2023, weekly release.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0. As a result, case and death metrics will appear lower than expected in the January 12, 2023, weekly release.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case and death data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties (Livingston and Washtenaw) were higher than expected in the January 19, 2023 weekly release.

January 26, 2023: Due to a backlog of historic COVID-19 cases being reported this week, aggregate case and death counts in Charlotte County and Sarasota County, Florida, will appear higher than expected in the January 26, 2023 weekly release.

January 26, 2023: Due to data processing delays, Mississippi’s aggregate case and death data will be reported as 0 in the weekly release posted on January 26, 2023.

February 2, 2023: As of the data collection deadline, CDC observed an abnormally large increase in aggregate COVID-19 cases and deaths reported for Washington State. In response, totals for new cases and new deaths released on February 2, 2023, have been displayed as zero at the state level until the issue is addressed with state officials. CDC is working with state officials to address the issue.

February 2, 2023: Due to a decrease reported in cumulative case counts by Wyoming, case rates will be reported as 0 in the February 2, 2023, weekly release. CDC is working with state officials to verify the data submitted.

February 16, 2023: Due to data processing delays, Utah’s aggregate case and death data will be reported as 0 in the weekly release posted on February 16, 2023. As a result, case and death metrics will appear lower than expected and should be interpreted with caution.

February 16, 2023: Due to a reporting cadence change, Maine’s

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

The following apply to all three datasets:

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and aut

ABSTRACT Background : The Covid-19 pandemic associated with the SARS-CoV-2 has caused very high death tolls in many countries, while it has had less prevalence in other countries of Africa and Asia. Climate and geographic conditions, as well as other epidemiologic and demographic conditions, were a matter of debate on whether or not they could have an effect on the prevalence of Covid-19. Objective : In the present work, we sought a possible relevance of the geographic location of a given country on its Covid-19 prevalence. On the other hand, we sought a possible relation between the history of epidemiologic and demographic conditions of the populations and the prevalence of Covid-19 across four continents (America, Europe, Africa, and Asia). We also searched for a possible impact of pre-pandemic alcohol consumption in each country on the two year death tolls across the four continents. Methods : We have sought the death toll caused by Covid-19 in 39 countries and obtained the registered deaths from specialized web pages. For every country in the study, we have analysed the correlation of the Covid-19 death numbers with its geographic latitude, and its associated climate conditions, such as the mean annual temperature, the average annual sunshine hours, and the average annual UV index. We also analyzed the correlation of the Covid-19 death numbers with epidemiologic conditions such as cancer score and Alzheimer score, and with demographic parameters such as birth rate, mortality rate, fertility rate, and the percentage of people aged 65 and above. In regard to consumption habits, we searched for a possible relation between alcohol intake levels per capita and the Covid-19 death numbers in each country. Correlation factors and determination factors, as well as analyses by simple linear regression and polynomial regression, were calculated or obtained by Microsoft Exell software (2016). Results : In the present study, higher numbers of deaths related to Covid-19 pandemic were registered in many countries in Europe and America compared to other countries in Africa and Asia. The analysis by polynomial regression generated an inverted bell-shaped curve and a significant correlation between the Covid-19 death numbers and the geographic latitude of each country in our study. Higher death numbers were registered in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line. In a bell shaped curve, the latitude levels were negatively correlated to the average annual levels (last 10 years) of temperatures, sunshine hours, and UV index of each country, with the highest scores of each climate parameter being registered around the equator line, while lower levels of temperature, sunshine hours, and UV index were registered in higher latitude countries. In addition, the linear regression analysis showed that the Covid-19 death numbers registered in the 39 countries of our study were negatively correlated with the three climate factors of our study, with the temperature as the main negatively correlated factor with Covid-19 deaths. On the other hand, cancer and Alzheimer's disease scores, as well as advanced age and alcohol intake, were positively correlated to Covid-19 deaths, and inverted bell-shaped curves were obtained when expressing the above parameters against a country’s latitude. Instead, the (birth rate/mortality rate) ratio and fertility rate were negatively correlated to Covid-19 deaths, and their values gave bell-shaped curves when expressed against a country’s latitude. Conclusion : The results of the present study prove that the climate parameters and history of epidemiologic and demographic conditions as well as nutrition habits are very correlated with Covid-19 prevalence. The results of the present study prove that low levels of temperature, sunshine hours, and UV index, as well as negative epidemiologic and demographic conditions and high scores of alcohol intake may worsen Covid-19 prevalence in many countries of the northern hemisphere, and this phenomenon could explain their high Covid-19 death tolls. Keywords : Covid-19, Coronavirus, SARS-CoV-2, climate, temperature, sunshine hours, UV index, cancer, Alzheimer disease, alcohol.

This dataset has been temporarily removed as of April 3, 2023. This dataset is still being maintained internally and will be restored after internal processes undergo a transition. Similar data are available from CDPH: https://calcat.covid19.ca.gov/cacovidmodels/.

This dataset displays SARS-CoV-2 lineages identified through whole genome sequencing (WGS) in Marin County by date the sample was collected. There is a minimum 7-day (and up to 21-day) lag in reporting. Not all positive samples in Marin County are sequenced, thus these data may not fully represent the variants circulating in the community. Summarized data can be found in the "Prevalence of Variants of SARS-CoV-2 in Marin County" chart at https://coronavirus.marinhhs.org/surveillance.

More information about variants of SARS-CoV-2 can be found via the CDC at https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html

The ongoing SARS-CoV-2 pandemic has devastated the global economy and claimed more than one million lives, presenting an urgent global health crisis. To identify host factors required for infection by SARS-CoV-2 and seasonal coronaviruses, we designed a focused high-coverage CRISPR-Cas9 library targeting 332 members of a recently published SARS-CoV-2 protein interactome. We leveraged the compact nature of this library to systematically screen SARS-CoV-2 at two physiologically relevant temperatures (33 ºC and 37 ºC) along with three related coronaviruses (HCoV-229E, HCoV-NL63, and HCoV-OC43), allowing us to probe this interactome at a much higher resolution relative to genome scale studies. This approach yielded several new insights, including unexpected virus-specific differences in Rab GTPase requirements and GPI anchor biosynthesis, as well as identification of multiple pan-coronavirus factors involved in cholesterol homeostasis. This coronavirus essentiality catalog could inform ongoing drug development efforts aimed at intercepting and treating COVID-19, and help prepare for future coronavirus outbreaks.

The dataset shows the 7-day median of the RNA copies of the specified virus per day and 100’000 people in the wastewater treatment plant (ARA) Basel as well as the 7-day median of the corresponding case numbers. The data set is usually updated on Tuesdays with the data until the previous Sunday. ProRheno AG (operator of ARA Basel) takes a 24h sample of the raw waste water, which is examined for RNA of the specified viruses by the Cantonal Laboratory Basel-Stadt (KL BS). The measurement methodology has not been changed since the beginning of the monitoring: see publication https://smw.ch/index.php/smw/article/view/3226. The plausibility of the values is continuously checked against internal quality parameters. The study area comprises the catchment area of the ARA Basel, which consists mainly of the canton of Basel-Stadt as well as the municipalities of Allschwil, Binningen, Birsfelden, Bottmingen, Oberwil and Schönenbuch (all Canton Baselland). Until the end of June 2023, the measured values of the KL BS were also presented on the wastewater dashboard of the BAG Covid-19 Switzerland | Coronavirus | Dashboard (https://www.covid19.admin.ch/de/epidemiologic/waste-water?wasteWaterFacility=270101). As of July 2023, the measured values of the EAWAG SARS-CoV2 in wastewater – Eawag (https://www.eawag.ch/de/abteilung/sww/projekte/sars-cov2-im-abwasser/) will be published on this page, which also examines the raw wastewater of ARA Basel. The examination methods used by KL BS and EAWAG are very similar but not identical.Case figures correspond to the number of confirmed and reported cases of infections in the catchment area of ARA Basel.Interpretation of curvesThe monitoring of viruses in wastewater is primarily about identifying trends (in particular, of course, the increase of a circulating virus). It is not possible to derive a certain number of cases or the severity of an infection. A comparison of the curve rash (height of peaks) at different times is hardly possible, because different virus variants lead to different amounts of virus per case. Different virus variants can also affect the symptoms, so that, for example, infections in humans run without a trace, but nevertheless viruses are released into the wastewater.

The aim of the special survey of the GESIS panel on the outbreak of the corona virus SARS-CoV-2 in Germany was to collect timely data on the effects of the corona crisis on people´s daily lives. The study focused on questions of risk perception, risk minimization measures, evaluation of political measures and their compliance, trust in politics and institutions, changed employment situation, childcare obligations, and media consumption. Due to the need for timely data collection, only the GESIS panel sub-sample of online respondents was invited (about three quarters of the sample). Since, due to time constraints, respondents could only participate in the online survey but not by mail, the results cannot be easily transferred to the overall population. Further longitudinal surveys on Covid-19 with the entire sample of the GESIS panel are planned for 2020.

Topics: Risk perception: Probability of events related to corona infection in the next two months (self, infection of a person from close social surrondings, hospital treatment, quarantine measures regardless of whether infected or not, infecting other people)

Risk minimization: risk minimization measures taken in the last seven days (avoided certain (busy) places, kept minimum distance to other people, adapted school or work situation, quarantine due to symptoms or without symptoms, washed hands more often, used disinfectant, stocks increased, reduced social interactions, worn face mask, other, none of these measures).

Evaluation of the effectiveness of various policy measures to combat the further spread of corona virus (closure of day-care centres, kindergartens and schools, closure of sports facilities, closure of bars, cafés and restaurants, closure of all shops except supermarkets and pharmacies, ban on visiting hospitals, nursing homes and old people´s homes, curfew for persons aged 70 and over or people with health problems or for anyone not working in the health sector or other critical professions (except for basic purchases and urgent medical care).

Curfew compliance or refusal: Willingness to obey a curfew vs. refusal; reasons for the compliance with curfew (social duty, fear of punishment, protection against infection, fear of infecting others (loved ones, infecting others in general, a risk group); reasons for refusal of curfew (restrictions too drastic or not justified, other obligations, does not stop the spread, not affected by the outbreak, boring at home, will not be punished).

Evaluation of the effectiveness of various government measures (medical care, restrictions on social life such as closure of public facilities and businesses, reduction of economic damage, communication with the population).

Trust in politics and institutions with regard to dealing with the coronavirus (physician, local health authority, local and municipal administration, Robert Koch Institute (RKI), Federal Government, German Chancellor, Ministry of Health, World Health Organization (WHO), scientists).

Changed employment situation: employment status at the beginning of March; change in occupational situation since the spread of coronavirus: dependent employees: number of hours reduced, number of hours increased, more home office, leave of absence with/ without continued wage payment , fired, no change; self-employed: working hours reduced, working hours increased, more home office, revenue decreased, revenue increased, company temporarily closed by the authorities, company temporarily voluntarily closed, financial hardship, company permanently closed or insolvent, no change.

Childcare: children under 12 in the household; organisation of childcare during the closure of day-care centres, kindergartens and schools (staying at home, partner stays at home, older siblings take care, grandparents are watching, etc.)

Media consumption on Corona: information sources used for Corona (e.g. nationwide public or private television or radio, local public or private television or radio, national newspapers or local newspapers, Facebook, other social media, personal conversations with friends and family, other, do not inform myself on the subject); frequency of Facebook usage; information about Corona obtained from regional Facebook page or regional Facebook group.

Demography: sex; age (categorized); education (categorized); intention to vote and choice of party (Sunday question); Left-right self-assessment; marital status; size of household.

Additionally coded: Respondent ID;...

The COVID Tracking Project was a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States. Our dataset was in use by national and local news organizations across the United States and by research projects and agencies worldwide.

Every day, we collected data on COVID-19 testing and patient outcomes from all 50 states, 5 territories, and the District of Columbia by visiting official public health websites for those jurisdictions and entering reported values in a spreadsheet. The files in this dataset represent the entirety of our COVID-19 testing and outcomes data collection from March 7, 2020 to March 7, 2021. This dataset includes official values reported by each state on each day of antigen, antibody, and PCR test result totals; the total number of probable and confirmed cases of COVID-19; the number of people currently hospitalized, in intensive care, and on a ventilator; the total number of confirmed and probable COVID-19 deaths; and more.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

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2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
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COVID-19++ is a citation-aware COVID-19 dataset for the analysis of research dynamics. In addition to primary COVID-19 related articles and preprints from 2020, it includes citations and the metadata of first-order cited work. All publications are annotated with MeSH terms, either from the ground truth, or via ConceptMapper, if no ground truth was available.

The data is organized in CSV files

- Paper metadata (paper_id, publdate, title, data_source): paper.csv

- Annotation data, mapping paper_id to MeSH terms: annotation.csv

- Authorship data, mapping paper_id to author, optionally with ORCID: authorship.csv
- Paired DOIs of citing and cited papers: references.csv

The column data source within the paper metadata has the value KE (for metadata from ZB MED KE), PP (for preprints) or CR (for cited resources from CrossRef)

This work was supported by BMBF within the programme ``Quantitative Wissenschaftsforschung'' under grant numbers 01PU17013A, 01PU17013B, 01PU17013C.

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.