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.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

The federal American Rescue Plan Act (ARPA) provided approximately $8.7 billion to Massachusetts through the Coronavirus State and Local Fiscal Recovery Funds. The Commonwealth received $5.3 billion from the Coronavirus State Fiscal Recovery Fund (CSFRF). Municipalities and functional counties in the Commonwealth received $3.4 billion from the Coronavirus Local Fiscal Recovery Fund (CLFRF). Tribal Governments in the Commonwealth received $25 million from the Coronavirus State and Local Fiscal Recovery Funds.

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.

In 2022, the Israeli Nature and Parks Authority registered over *** thousand visits to the site of Ma'yan Harod in Israel. This was an increase of over just ** percent compared to the previous year. In contrast, in 2020, the number of visits decreased significantly. This decline occurred due to the Israeli government's introduction of tourism and leisure restrictions following the coronavirus (COVID-19) outbreak in March 2020.

This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024.

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

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
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
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Objectives: Patients with comorbidities are at increased risk for poor outcomes in COVID-19, yet data on patients with prior neurological disease remains limited. Our objective was to determine the odds of critical illness and duration of mechanical ventilation in patients with prior cerebrovascular disease and COVID-19.Methods: A observational study of 1,128 consecutive adult patients admitted to an academic center in Boston, Massachusetts, and diagnosed with laboratory-confirmed COVID-19. We tested the association between prior cerebrovascular disease and critical illness, defined as mechanical ventilation (MV) or death by day 28, using logistic regression with inverse probability weighting of the propensity score. Among intubated patients, we estimated the cumulative incidence of successful extubation without death over 45 days using competing risk analysis.Results: Of the 1,128 adults with COVID-19, 350 (36%) were critically ill by day 28. The median age of patients was 59 years (SD: 18 years) and 640 (57%) were men. As of June 2nd, 2020, 127 (11%) patients had died. A total of 177 patients (16%) had a prior cerebrovascular disease. Prior cerebrovascular disease was significantly associated with critical illness (OR = 1.54, 95% CI = 1.14–2.07), lower rate of successful extubation (cause-specific HR = 0.57, 95% CI = 0.33–0.98), and increased duration of intubation (restricted mean time difference = 4.02 days, 95% CI = 0.34–10.92) compared to patients without cerebrovascular disease.Interpretation: Prior cerebrovascular disease adversely affects COVID-19 outcomes in hospitalized patients. Further study is required to determine if this subpopulation requires closer monitoring for disease progression during COVID-19.

Printable poster for businesses participating in the Restrictions Exemption Program to help prevent the spread of COVID-19 in Alberta. Available in various language. For more information visit alberta.ca/REP

The epithelial barrier's primary role is to protect against entry of foreign and pathogenic elements. Global and targeted approaches were applied to nasal swabs from healthy and COVID-19-confirmed cases within 24 hours post-positive-confirmation and at 3 weeks post-infection to observe changes in proteome and metabolome. We found that the tryptophan/kynurenine metabolism pathway is a pinch-point regulator of canonical and non-canonical transcription activation, macrophage release of cytokines and significant changes in the immune and metabolic status with increasing severity and disease course. Methods Nasal epithelial swabs were self-collected by participants in this study. Swabs were resuspended in 80% methanol with 6mg of 1.0 mm zirconium beads and used cell shearing to extract proteins and metabolites. The method is described in Wasinger et al., 2020 [1]. Proteins were pelleted and the supernatant containing metabolites stored at -80°C until required. Protein pellet was resuspended in digestion buffer and 50 µg enzymatically treated with trypsin overnight at room temperature. Proteomic mass spectrometry Mass spectrometry was carried out using a QExactive (Thermo Electron, Bremen, Germany) run in DDA mode using 1.5 μg (2.0 μL from 10μL) as previously described [2]. Peptides were eluted using a linear gradient of H2O:CH3CN (98:2, 0.1% formic acid) to H2O:CH3CN (64:36, 0.1% formic acid) at 250 nL min-1 over 60 min. Statistical Analysis Proteins were identified using Mascot Daemon v2.5.1 (Matrix Science, London, UK) searched against the SwissProt and SARV19 database (downloaded February 2021, containing 563,972 sequences; and July 2020, containing 271,909 sequences, respectively). Search parameters were set to carbamidomethyl (C); variable modifications, oxidation (M), phospho (STY); enzyme, semi-Trypsin; and maximum missed cleavages, 1; peptide tolerance, ± 5 ppm; fragment tolerance, 0.05 Da. Scaffold software (version 4.6.1, Proteome Software Inc., Portland, OR, USA) was used to compare the proteome. Peptide identifications were accepted if they could be established at greater than 95% probability using the Scaffold delta-mass correction. Protein identifications were accepted if they could be established at less than 1% false discovery rate (FDR) and contained at least 2 identified peptides. Expression changes across the samples were measured via spectral count, normalised to total ion count. ANOVA was used to report abundance changes controlled by the Benjamini-Hochberg procedure for multiple comparisons, with p-values set to <0.05. The studies reached a power ≥ 90% and were calculated using PASS software based on a mean abundance values and standard deviation between groups. The proteomic dataset of differentially abundant proteins was assessed for enriched pathways using Ingenuity Pathway Analysis (IPA® Qiagen, CA, USA). The core analysis was carried out using the default settings with only direct relationships and only experimentally observed confidence considered based on the IPA knowledge base (genes only). The P-value for the correlation between identified proteins and a given canonical pathway was calculated by Fisher's exact test. Targeted proteins were analysed using Skyline Software, and peptides were accepted based on retention time and sequence with at least 3 transitions required. Peak area under curve of the parent ion was used to assess relative abundance of the marker panel. Log2 transformed data were evaluated using Student T-test, and Receiver Operating Characteristic (ROC) probability curves to measure ability to distinguish between binary classifiersPRM targeted analysis applied transitions listed in Attachment. Quantification of Kynurenine Pathway Mixed standards and 100 µl aliquots of Nasal methanolic extracts were spiked with an internal standard mixture containing labelled KP metabolites; dried, and reconstituted in 100 µl of water, filtered through 4 mm syringe filters with 0.2 μm membrane into reduced volume LC vials; 20 µl aliquots were injected for analysis. MRM LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex USA) solvent delivery/autosampler system. Chromatographic separation was achieved using a Kinetex™ PFP column (150mm x 2 mm, 1.7μm, 100 Å, Phenomenex USA) by reverse phase gradient elution at 25˚C using a gradient of 0.1% formic acid to 10% methanol over 2 min, then ramped to 60% B to 4min, and then ramped to 100%B by 8mins. Quantification of NAD+ome metabolites LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex USA) solvent delivery/autosampler system. Chromatographic separation was achieved using a Kinetex™ PFP column (150mm x 2 mm, 1.7μm, 100 Å, Phenomenex USA) by reverse phase gradient elution at 25˚C. The mobile phase consisted of aqueous 0.1% formic acid (A) and methanol (B). The gradient elution was programmed as follows: start at 10 % B, hold 2 minutes, ramp to 60%B in 4min, then to 100%B in 8min. In 0.4min set to 10 % B and equilibrate for 5.6 min. Total run time is 20 min. Mass spectrometric detection was performed using multiple reaction monitoring (MRM) with heated electrospray ionization (HESI) source in positive mode. MSD parameters were optimised using Anthranilic acid direct infusion, and the tune file created was used in the created method. The conditions were: ion spray voltage, 4,000 V; vaporizer temperature 300˚C, capillary temperature 300˚C, collision argon gas 1 Torr, sheath and auxiliary gas valves (nitrogen) set at 20 and 10 arbitrary units respectively. The MRM transitions for all analytes were optimised using a syringe infusion pump and are shown in Attachment 1. Data acquisition and processing were performed with Xcalibur™ (version 2.2, 2011 Thermo Fischer Scientific, Waltham MA). NAD+ome LCMS/MS assay of nasal epithelial (NE) swab extracts Methods followed Bustamante et al. [3]. LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer (Thermo, USA) connected to Vanquish (Thermo-Dionex, USA) solvent delivery system/autosampler using an adaptation of a previously published method by Bustamante et al. [5]. Isotopically enriched (2H) internal standards were purchased from Toronto Research Chemicals and primary standards from Sigma-Aldrich. HESI-MS parameters: Ion spray voltage 4,000 V; vaporizer temperature 300˚C, capillary temperature 300˚C, collision gas 1.0 Torr. These parameters were optimised using NMN solution in positive ion mode. Calibrators of known concentrations (0, 0.02, 0.04, 0.06, 0.08, 0.1, 0.2, 0.3, 0.4 μM) of NADOME metabolites were prepared by mixing aliquots of standards with a fixed volume of internal standard mixture. Similarly, NE extracts were mixed with internal std. cocktail, dried and reconstituted in 50 µl of 100 mM ammonium acetate in water. Samples were filtered into LC vials and 20μL injected for analysis. Data acquisition and processing were performed with Xcalibur™ (version 2.2, 2011 Thermo Fischer Scientific, Waltham MA). Mobile phases consisted of 5mM ammonium acetate in water pH 9.5 (A); 100 % Acetonitrile (B) according to Table S5 using a Phenomenex Luna 3 µm NH2 100 Å 150 x 2 mm column. Racemic amino acid analysis Methods were adapted from Ayon et al. [4]. Briefly, 40 µl of colon biopsies extracts were mixed with 2H4-alanine as internal standard. Samples were dried and derivatised with 20 µl of 10mM Marfey’s reagent in acetone and 5 µl of triethylamine and incubated at 37˚C for 3 hours, the reaction was quenched with 10 µl 0.5 M HCl. Samples were diluted with 120 µl of 30 % ACN in 0.1% aqueous formic acid. Phenomenex SPE Strata-X cartridges (30 mg) were preconditioned with methanol, followed by 0.1 % formic acid in water, and samples were loaded and washed with 0.1 % formic acid in water, and then eluted with 70 % acetonitrile in 0.1 % aqueous formic acid. Eluants were dried and reconstituted in 0.1 % aqueous formic acid before analysis. LC-MS/MS analysis was conducted using a TSQ Vantage mass spectrometer as described in Attachment 1. GCMS/MS assay of nasal epithelial (NE) swabs of picolinic and quinolinic acid GC-MS analysis was carried out using Agilent Technologies GCMS system comprising 5973inert MSD coupled to 6890 GC oven and 7683 series autosampler. Chromatographic column Agilent J&W DB5-MS UI 30mx 0.25mm x 0.25μm. Methods followed those described by Smythe et al. [5]. Single Ion Monitoring (SIM) GC-MS assay of picolinic and quinolinic acid in nasal swab extracts. Picolinic and quinolinic acid in NE extracts were assayed by GC–MS in electron-capture negative ionization mode; a very sensitive method with on-column limit of detection for QUIN and PIC < 1 femtomol on column (Smythe et al. 2003). Briefly, standards and NS extracts (100-200μl) were spiked with 2H4 -Pic and 2H3-Quin in 13x100mm glass cell culture tubes, and dried in a Speedvac before derivatisation with 60μL TFAA and 60μL of HFP. Capped tubes were placed in a heating block at at 60°C for 30 min to produce the hexafluoro-isopropyl esters of the respective acids. Samples were then dissolved in 80μl of toluene, washed with 1ml of 5% sodium bicarbonate and 1ml of water to remove by-products. The upper toluene layer was passed through anhydrous sodium sulphate mini columns (approx. 500 mg) into autosampler vials, and 2μl were injected into the GC/MS system. Sample concentrations of Pic and Quin were calculated from the standard curves generated.
Monitored SIM ions for 2H4 -Pic, Pic, 2H3-Quin and Quin are m/z 277, m/z 273, m/z 467 and m/z 470 respectively.
Injector temperature 250˚C, transfer line temperature 280˚C; run time 15.2 minutes using T program below: GC-MS analysis was carried out using Agilent Technologies GCMS system comprising 5973inert MSD coupled to 6890 GC oven and 7683 series autosampler. Chromatographic column