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).

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.

This dataset tracks the updates made on the dataset "New York State Statewide COVID-19 Fatalities by Age Group" as a repository for previous versions of the data and metadata.

As of March 10, 2023, the death rate from COVID-19 in the state of New York was 397 per 100,000 people. New York is one of the states with the highest number of COVID-19 cases.

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.

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.

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.

There have been almost 60 thousand COVID-19 deaths in New York State as of December 16, 2022. A majority of those deaths have been recorded in New York City: Staten Island, Queens, Brooklyn, Bronx, and Manhattan.

Pandemic takes hold in U.S. Across the United States, over one million COVID-19 deaths had been confirmed by the middle of December 2022. New York has been hit particularly hard throughout the pandemic and is among the states with the highest number of deaths from the coronavirus. The neighboring state of New Jersey was also at the heart of the initial outbreak in March 2020, and the two states continue to have some of the highest death rates from the coronavirus in the United States.

Deaths in New York City The number of new daily deaths from COVID-19 in New York City peaked early in the pandemic. Since then there have been waves in which the number of daily deaths rose, but they have not gotten close to the levels seen early in the pandemic. The impact of the coronavirus has been thoroughly analyzed, and the fatality rates by age in New York City support the evidence that the risk of developing more severe COVID-19 symptoms increases with age.

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.

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.

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.

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].

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.

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.

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.

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].

BackgroundThe COVID-19 pandemic has resulted in significant morbidity and mortality worldwide, with cytokine storm leading to exaggerating immune response, multi-organ dysfunction and death. Melatonin has been shown to have anti-inflammatory and immunomodulatory effects and its effect on COVID-19 clinical outcomes is controversial. This study aimed to conduct a meta-analysis to evaluate the impact of melatonin on COVID-19 patients.MethodsPubMed, Embase, and Cochrane Central Register of Controlled Trials were searched without any language or publication year limitations from inception to 15 Nov 2022. Randomized controlled trials (RCTs) using melatonin as therapy in COVID-19 patients were included. The primary outcome was mortality, and the secondary outcomes included were the recovery rate of clinical symptoms, changes in the inflammatory markers like C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and neutrophil to lymphocyte ratio (NLR). A random-effects model was applied for meta-analyses, and further subgroup and sensitivity analyses were also conducted.ResultsA total of nine RCTs with 718 subjects were included. Five studies using melatonin with the primary outcome were analyzed, and the pooled results showed no significant difference in mortality between melatonin and control groups with high heterogeneity across studies identified (risk ratio [RR] 0.72, 95% confidence interval [CI] 0.47–1.11, p = 0.14, I2 = 82%). However, subgroup analyses revealed statistically significant effects in patients aged under 55 years (RR 0.71, 95% CI 0.62–0.82, p < 0.01) and in patients treated for more than 10 days (RR 0.07, 95% CI 0.01–0.53, p = 0.01). The recovery rate of clinical symptoms and changes in CRP, ESR, and NLR were not statistically significant. No serious adverse effects were reported from melatonin use.ConclusionIn conclusion, based on low certainty of evidence, the study concluded that melatonin therapy does not significantly reduce mortality in COVID-19 patients, but there are possible benefits in patients under 55 years or treated for more than 10 days. With a very low certainty of evidence, we found no significant difference in the recovery rate of COVID-19 related symptoms or inflammatory markers in current studies. Further studies with larger sample sizes are warranted to determine the possible efficacy of melatonin on COVID-19 patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022351424.

As of March 7, 2021, there have been 39,029 deaths due to COVID-19 in the state of New York, with the first 3 deaths reported on March 14, 2020. This statistic shows the cumulative number of deaths related to COVID-19 in New York State from March 14 to March 7, 2021, by day.

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.

ObjectivesTo identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).MethodsWe searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.Results83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.ConclusionFew models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.Systematic Review Registration:[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.