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.

This dataset contains daily data trackers for the COVID-19 pandemic, aggregated by month and starting 18.3.20. The first release of COVID-19 data on this platform was on 1.6.20. Updates have been provided on a quarterly basis throughout 2023/24. No updates are currently scheduled for 2024/25 as case rates remain low. The data is accurate as at 8.00 a.m. on 8.4.24. Some narrative for the data covering the latest period is provided here below: Diagnosed cases / episodes • As at 3.4.24 CYC residents have had a total 75,556 covid episodes since the start of the pandemic, a rate of 37,465 per 100,000 of population (using 2021 Mid-Year Population estimates). The cumulative rate in York is similar to the national (37,305) and regional (37,059) averages. • The latest rate of new Covid cases per 100,000 of population for the period 28.3.24 to 3.4.24 in York was 1.49 (3 cases). The national and regional averages at this date were 1.67 and 2.19 respectively (using data published on Gov.uk on 5.4.24).

As of December 16, 2022, there had been almost 6.37 million COVID-19 cases in New York State, with 2.97 million cases found in New York City. New York has been one of the U.S. states most impacted by the pandemic, recording the highest number of deaths in the country.

A closer look at the outbreak in New York Towards the middle of December 2022, the number of deaths due to the coronavirus in New York State had reached almost 60 thousand, and almost half of those deaths were in New York City. However, the number of new daily deaths in New York City peaked early in the pandemic and although there have been times when the number of new daily deaths surged, they have not gotten close to reaching the levels seen at the beginning of the pandemic. New York City is made up of five counties, which are more commonly known by their borough names – Staten Island is the borough with the highest rate of COVID-19 cases.

Note: Data elements were retired from HERDS on 10/6/23 and this dataset was archived.

This dataset includes the cumulative number and percent of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date and age group. This dataset does not include fatalities related to COVID-19 disease that did not occur at a hospital, nursing home, or adult care facility. The primary goal of publishing this dataset is to provide users with information about healthcare facility fatalities among patients with lab-confirmed COVID-19 disease.

The information in this dataset is also updated daily on the NYS COVID-19 Tracker at https://www.ny.gov/covid-19tracker.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals, nursing homes, and adult care facilities are required to complete this survey daily. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in March 2020, while Nursing Homes and Adult Care Facilities began reporting in April 2020. It is important to note that fatalities related to COVID-19 disease that occurred prior to the first publication dates are also included.

The fatality numbers in this dataset are calculated by assigning age groups to each patient based on the patient age, then summing the patient fatalities within each age group, as of each reporting date. The statewide total fatality numbers are calculated by summing the number of fatalities across all age groups, by reporting date. The fatality percentages are calculated by dividing the number of fatalities in each age group by the statewide total number of fatalities, by reporting date. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

On April 7, 2020, there were 598 new deaths due to COVID-19 in New York City, higher than any other day since the pandemic hit the city. The state of New York has been one of the hardest hit U.S. states by the COVID-19 pandemic. This statistic shows the number of new COVID-19 deaths in New York City from March 3, 2020 to December 19, 2022, by date.

Daily count of NYC residents who tested positive for SARS-CoV-2, who were hospitalized with COVID-19, and deaths among COVID-19 patients.

Note that this dataset currently pulls from https://raw.githubusercontent.com/nychealth/coronavirus-data/master/case-hosp-death.csv on a daily basis.

The Project involved getting photos of closed due to COVID signs in shops and businesses in York UK.

One column of "text" includes all the transcripts of the signs with phone numbers removed.

The death rate in New York City for adults aged 75 years and older was around 4,135 per 100,000 people as of December 22, 2022. The risk of developing more severe illness from COVID-19 increases with age, and the virus also poses a particular threat to people with underlying health conditions.

What is the death toll in NYC? The first coronavirus-related death in New York City was recorded on March 11, 2020. Since then, the total number of confirmed deaths has reached 37,452 while there have been 2.6 million positive tests for the disease. The number of daily new deaths in New York City has fallen sharply since nearly 600 residents lost their lives on April 7, 2020. A significant number of fatalities across New York State have been linked to long-term care facilities that provide support to vulnerable elderly adults and individuals with physical disabilities.

The impact on the counties of New York State Nearly every county in the state of New York has recorded at least one death due to the coronavirus. Outside of New York City, the counties of Nassau, Suffolk, and Westchester have confirmed over 11,500 deaths between them. When analyzing the ratio of deaths to county population, Rockland had one of the highest COVID-19 death rates in New York State in 2021. The county, which has approximately 325,700 residents, had a death rate of around 29 per 10,000 people in April 2021.

Note: This dataset was archived on 10/6/23. Statewide hospitalization data is available in the New York State Statewide COVID-19 Hospitalizations and Beds dataset.

This dataset includes the number of patients hospitalized, and number of patients in the intensive care unit (ICU) among patients with lab-confirmed COVID-19 disease by hospital region and reporting date. The primary goal of publishing this dataset is to provide users with timely information about hospitalizations among patients with lab-confirmed COVID-19 disease.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals are required to complete this survey daily and data reflects the number of patients hospitalized and number of patients in the ICU reported by hospitals through the survey each day. These data include NYS resident and non-NYS resident hospitalizations. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in mid-March 2020.

To calculate regional totals, the number of patients hospitalized and number of patients in the ICU are each summed by hospital region and reporting date.

The information in this dataset is updated daily on NY Forward; New York State’s resource for COVID-19 testing, early warning monitoring, and regional daily hospitalization dashboards. More information can be found at forward.ny.gov.

Note: As of November 10, 2023, this dataset has been archived. For the current version of this data, please visit: https://health.data.ny.gov/d/gikn-znjh

This dataset reports daily on the number of people vaccinated by New York providers with at least one dose and with a complete COVID-19 vaccination series overall since December 14, 2020. New York providers include hospitals, mass vaccination sites operated by the State or local governments, pharmacies, and other providers registered with the State to serve as points of distribution.

This dataset is created by the New York State Department of Health from data reported to the New York State Immunization Information System (NYSIIS) and the New York City Citywide Immunization Registry (NYC CIR). County-level vaccination data is based on data reported to NYSIIS and NYC CIR by the providers administering vaccines. Residency is self-reported by the individual being vaccinated. This data does not include vaccine administered through Federal entities or performed outside of New York State to New York residents. NYSIIS and CIR data is used for county-level statistics. New York State Department of Health requires all New York State vaccination providers to report all COVID-19 vaccination administration data to NYSIIS and NYC CIR within 24 hours of administration.

Context

This dataset is a collection of Public Notices issued by York Region of ON, Canada.

Content

The Data found within this dataset lists the COVID19 Restriction Violations by local shops and restaurants since the announcement of COVID19 restrictions in November 2020. Details such as name of the business as well as the corresponding address is provided.

Acknowledgements

Special thanks goes to the York Region of ON, Canada for publishing up to date information on COVID19 violations https://www.york.ca/wps/portal/yorkhome/health/yr/covid-19/enforcingcovid19regulations/02enforcingcovid19regulations/!ut/p/z1/vZJBU4MwFIR_i4cemTySAOkx0lpAS2u1LXDpAKU0WqBirNZfb9rRGS9FHUUOYZJs9s1-syhCAYrKeCfyWIqqjDdqH0bmwuUD13EuwRtRZgOHEfewxaDf1dH8KIATHwcUfed9gyBqtp-hCEXbVCxRuCJL01yxVDMMg2rUJImWpCbVVkZKk2VCEjDigzot5VauUbivF2lVyqyUHdhX9b3aPEohn44H66rI1JrFG7nuQFrtxFLTux3IylVVp6LMj0d6t87yp82R1mMHADdcI-8rFgo2rof2MFeJYrnWhDJDwcdsFDSZB82zlbW4e3iIuIp_yPwiUfDf-ecH-J8JDG4YBXfmWXymj4C65F2AMTUd3QYPnBED98IaGz3m6HCJ3wUNfQhVn6yTkCcYzXcie0bTsqoL1e-bH9bH-ZhgMZs7fABjuJ1acN23KDOvhuOrif7LCV8EaNmetGpvQbv2uF37v4HjuWDr_FB_0ifAsWuzc-Ix32-Xvd8ue79d9n67vZ_9Fs62mE4LRoxNzmTXvTPyYtE797XQ2702_ob87OwNXKayIA!!/dz/d5/L2dBISEvZ0FBIS9nQSEh/#.X9rrni2Q2fd

Inspiration

This data can be used to identify where COVID19 violations are taking place and where it is safe (or otherwise) to visit.

This dataset includes the different reopening statuses and health and safety guidelines that were assigned to individual industries during the State of New York’s COVID-19 declared state of emergency, which began on March 7, 2020, and ended on June 24, 2021.

BackgroundMany COVID-19 patients reveal a marked decrease in their lymphocyte counts, a condition that translates clinically into immunodepression and is common among these patients. Outcomes for infected patients vary depending on their lymphocytopenia status, especially their T-cell counts. Patients are more likely to recover when lymphocytopenia is resolved. When lymphocytopenia persists, severe complications can develop and often lead to death. Similarly, IL-10 concentration is elevated in severe COVID-19 cases and may be associated with the depression observed in T-cell counts. Accordingly, this systematic review and meta-analysis aims to analyze T-cell subsets and IL-10 levels among COVID-19 patients. Understanding the underlying mechanisms of the immunodepression observed in COVID-19, and its consequences, may enable early identification of disease severity and reduction of overall morbidity and mortality.MethodsA systematic search was conducted covering PubMed MEDLINE, Scopus, Web of Science, and EBSCO databases for journal articles published from December 1, 2019 to March 14, 2021. In addition, we reviewed bibliographies of relevant reviews and the medRxiv preprint server for eligible studies. Our search covered published studies reporting laboratory parameters for T-cell subsets (CD4/CD8) and IL-10 among confirmed COVID-19 patients. Six authors carried out the process of data screening, extraction, and quality assessment independently. The DerSimonian-Laird random-effect model was performed for this meta-analysis, and the standardized mean difference (SMD) and 95% confidence interval (CI) were calculated for each parameter.ResultsA total of 52 studies from 11 countries across 3 continents were included in this study. Compared with mild and survivor COVID-19 cases, severe and non-survivor cases had lower counts of CD4/CD8 T-cells and higher levels of IL-10.ConclusionOur findings reveal that the level of CD4/CD8 T-cells and IL-10 are reliable predictors of severity and mortality in COVID-19 patients. The study protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO); registration number CRD42020218918.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218918, identifier: CRD42020218918.

PurposeThe unique constraints to everyday life brought about by the COVID-19 pandemic have been suggested to negatively impact those with pre-existing mental health issues such as eating disorders. While individuals with eating disorders or disordered eating behaviors likely represent a vulnerable group to the COVID-19 pandemic, the impact of the pandemic is yet to be fully established.MethodsWe systematically examined the impact of the COVID-19 pandemic on eating disorders and disordered eating behaviors. We searched electronic databases MEDLINE, PsycINFO, CINAHL, and EMBASE for literature published until October 2021. Eligible studies were required to report on individuals with or without a diagnosed eating disorder or disordered eating behaviors who were exposed to the COVID-19 pandemic.FindingsSeventy-two studies met eligibility criteria with the majority reporting an increase in eating disorder or disordered eating behaviors associated with the COVID-19 pandemic. Specifically, it appears children and adolescents and individuals with a diagnosed eating disorder may present vulnerable groups to the impacts of the COVID-19 pandemic.DiscussionThis mixed systematic review provides a timely insight into COVID-19 eating disorder literature and will assist in understanding possible future long-term impacts of the pandemic on eating disorder behaviors. It appears that the role of stress in the development and maintenance of eating disorders may have been intensified to cope with the uncertainty of the COVID-19 pandemic. Future research is needed among understudied and minority groups and to examine the long-term implications of the COVID-19 pandemic on eating disorders and disordered eating behaviors.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=284749, PROSPERO [CRD42021284749].

Objective: Cardiac injury is detected in numerous patients with coronavirus disease 2019 (COVID-19) and has been demonstrated to be closely related to poor outcomes. However, an optimal cardiac biomarker for predicting COVID-19 prognosis has not been identified.Methods: The PubMed, Web of Science, and Embase databases were searched for published articles between December 1, 2019 and September 8, 2021. Eligible studies that examined the anomalies of different cardiac biomarkers in patients with COVID-19 were included. The prevalence and odds ratios (ORs) were extracted. Summary estimates and the corresponding 95% confidence intervals (95% CIs) were obtained through meta-analyses.Results: A total of 63 studies, with 64,319 patients with COVID-19, were enrolled in this meta-analysis. The prevalence of elevated cardiac troponin I (cTnI) and myoglobin (Mb) in the general population with COVID-19 was 22.9 (19–27%) and 13.5% (10.6–16.4%), respectively. However, the presence of elevated Mb was more common than elevated cTnI in patients with severe COVID-19 [37.7 (23.3–52.1%) vs.30.7% (24.7–37.1%)]. Moreover, compared with cTnI, the elevation of Mb also demonstrated tendency of higher correlation with case-severity rate (Mb, r = 13.9 vs. cTnI, r = 3.93) and case-fatality rate (Mb, r = 15.42 vs. cTnI, r = 3.04). Notably, elevated Mb level was also associated with higher odds of severe illness [Mb, OR = 13.75 (10.2–18.54) vs. cTnI, OR = 7.06 (3.94–12.65)] and mortality [Mb, OR = 13.49 (9.3–19.58) vs. cTnI, OR = 7.75 (4.4–13.66)] than cTnI.Conclusions: Patients with COVID-19 and elevated Mb levels are at significantly higher risk of severe disease and mortality. Elevation of Mb may serve as a marker for predicting COVID-19-related adverse outcomes.Prospero Registration Number:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020175133, CRD42020175133.

BackgroundSystematic data on the efficacy and safety of COVID-19 vaccine in patients on renal replacement therapy (RRT) remains limited. We conducted a meta-analysis on the efficacy and safety of COVID-19 vaccine in patients on RRT.MethodsEligible studies were identified by systematic literature search in four electronic databases. Twenty-seven studies (4,264 patients) were included for meta-analysis. 99% patients received mRNA vaccine.ResultsPatients on RRT showed inferior seropositivity after two-dosed COVID-19 vaccine, 44% lower than the general population. Kidney transplant recipients (KTRs) had significantly lower seropositivity than patients on haemodialysis (HD) or peritoneal dialysis (PD) (26.1 vs. 84.3% and 92.4% respectively, p < 0.001 for both). Compared with healthy controls, KTRs, HD and PD patients were 80% (95% CI: 62–99%), 18% (95% CI: 9–27%) and 11% (95% CI: 1–21%) less likely to develop antibodies after vaccination (p < 0.001, <0.001 and 0.39 respectively). In KTRs, every 1% increase in using mycophenolate was associated with 0.92% reduction in seropositivity (95% CI: −1.68, −0.17, p = 0.021) at population level. The overall adverse event rate attributed to vaccination was 2.1%. Most events were mild.ConclusionPatients on RRT, particularly KTRs, had significantly reduced antibody response after two-dosed COVID-19 vaccination. Vaccination is generally well tolerated.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42021261879.

This dataset reports the number of people vaccinated by New York providers who have received a dose of the annual COVID-19 vaccine and the number who have received the annual Influenza vaccine, beginning with the 2024-2025 season.

Note: This dataset replaces two prior COVID-19 vaccination datasets. Please refer to the notes section below for links to the archived data.

Post-COVID syndrome can be defined as symptoms of COVID-19 that persist for longer than 12 weeks, with several studies reporting persistent symptoms relating to the sensory organs (eyes, ears, and nose). The aim of this systematic review was to examine the prevalence of persistent anosmia, hyposmia, ageusia, and hypogeusia, as well as eye/vision and ear/hearing related long-COVID symptoms. Authors searched the electronic databases from inception to November 2021. Search terms included words related to long-COVID, smell, taste, eyes/vision, and ears/hearing, with all observational study designs being included. A random effects meta-analysis was undertaken, calculating the prevalence proportions of anosmia, hyposmia, ageusia, and hypogeusia, respectively. From the initial pool, 21 studies met the inclusion criteria (total n 4,707; median n per study 125; median age = 49.8; median percentage female = 59.2%) and 14 were included in the meta-analysis The prevalence of anosmia was 12.2% (95% CI 7.7–16.6%), hyposmia 29.9% (95% CI 19.9–40%), ageusia 11.7% (95% CI 6.1–17.3%), and hypogeusia 31.2% (95% 16.4–46.1%). Several eye/vision and ear/hearing symptoms were also reported. Considering that changes in the sensory organs are associated with decreases in quality of life, future research should examine the etiology behind the persistent symptoms.Systematic review registration[www.crd.york.ac.uk/prospero], identifier [CRD42021292804].

This systematic review aimed to provide an overview of the clinical profile and outcome of COVID-19 infection in patients with hemoglobinopathy. The rate of COVID-19 mortality and its predictors were also identified. A systematic search was conducted in accordance with PRISMA guidelines in five electronic databases (PubMed, Scopus, Web of Science, Embase, WHO COVID-19 database) for articles published between 1st December 2019 to 31st October 2020. All articles with laboratory-confirmed COVID-19 cases with underlying hemoglobinopathy were included. Methodological quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Thirty-one articles with data on 246 patients with hemoglobinopathy were included in this review. In general, clinical manifestations of COVID-19 infection among patients with hemoglobinopathy were similar to the general population. Vaso-occlusive crisis occurred in 55.6% of sickle cell disease patients with COVID-19 infection. Mortality from COVID-19 infection among patients with hemoglobinopathy was 6.9%. After adjusting for age, gender, types of hemoglobinopathy and oxygen supplementation, respiratory (adj OR = 89.63, 95% CI 2.514–3195.537, p = 0.014) and cardiovascular (adj OR = 35.20, 95% CI 1.291–959.526, p = 0.035) comorbidities were significant predictors of mortality. Patients with hemoglobinopathy had a higher mortality rate from COVID-19 infection compared to the general population. Those with coexisting cardiovascular or respiratory comorbidities require closer monitoring during the course of illness. More data are needed to allow a better understanding on the clinical impact of COVID-19 infections among patients with hemoglobinopathy.Clinical Trial Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020218200.