The COVID-19 dashboard includes data on city/town COVID-19 activity, confirmed and probable cases of COVID-19, confirmed and probable deaths related to COVID-19, and the demographic characteristics of cases and deaths.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

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).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. 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.

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; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

The following dashboards provide data on contagious respiratory viruses, including acute respiratory diseases, COVID-19, influenza (flu), and respiratory syncytial virus (RSV) in Massachusetts. The data presented here can help track trends in respiratory disease and vaccination activity across Massachusetts.

This dataset is no longer being updated as of 6/30/2022. It is being retained on the Open Data Portal for its potential historical interest.

In November 2020, the City of Cambridge began collecting and analyzing COVID-19 data from municipal wastewater, which can serve as an early indicator of increased COVID-19 infections in the city. The Cambridge Public Health Department and Cambridge Department of Public Works are using technology developed by Biobot, a Cambridge based company, and partnering with the Massachusetts Water Resources Authority (MWRA). This Cambridge wastewater surveillance initiative is funded through a $175,000 appropriation from the Cambridge City Council.

This dataset indicates the presence of the COVID-19 virus (measured as viral RNA particles from the novel coronavirus per ml) in municipal wastewater. The Cambridge site data here were collected as a 24-hour composite sample, which is taken weekly. The MWRA site data ere were collected as a 24-hour composite sample, which is taken daily. MWRA and Cambridge data are listed here in a single table.

An interactive graph of this data is available here: https://cityofcambridge.shinyapps.io/COVID19/?tab=wastewater

All areas within the City of Cambridge are captured across four separate catchment areas (or sewersheds) as indicated on the map viewable here: https://cityofcambridge.shinyapps.io/COVID19/_w_484790f7/BioBot_Sites.png. The North and West Cambridge sample also includes nearly all of Belmont and very small areas of Arlington and Somerville (light yellow). The remaining collection sites are entirely -- or almost entirely -- drawn from Cambridge households and workplaces.

Data are corrected for wastewater flow rate, which adjusts for population in general. Data listed are expected to reflect the burden of COVID-19 infections within each of the four sewersheds. A lag of approximately 4-7 days will occur before new transmissions captured in wastewater data would result in a positive PCR test for COVID-19, the most common testing method used. While this wastewater surveillance tool can provide an early indication of major changes in transmission within the community, it remains an emerging technology. In assessing community transmission, wastewater surveillance data should only be considered in conjunction with other clinical measures, such as current infection rates and test positivity.

Each location is selected because it reflects input from a distinct catchment area (or sewershed) as identified on the color-coded map. Viral data collected from small catchment areas like these four Cambridge sites are more variable than data collected from central collection points (e.g., the MWRA facility on Deer Island) where wastewater from dozens of communities are joined and mixed. Data from each catchment area will be impacted by daily activity among individuals living in that area (e.g., working from home vs. traveling to work) and by daytime activities that are not from residences (businesses, schools, etc.) As such, the Regional MWRA data provides a more stable measure of regional viral counts. COVID wastewater data for Boston North and Boston South regions is available at https://www.mwra.com/biobot/biobotdata.htm

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.

As of March 10, 2023, the state with the highest number of COVID-19 cases was California. Almost 104 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time. When the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide has now reached over 669 million.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. People aged 85 years and older have accounted for around 27 percent of all COVID-19 deaths in the United States, although this age group makes up just two percent of the U.S. population

NOTE: This dataset pertains only to the 2020-2021 school year and is no longer being updated. For additional data on COVID-19, visit data.ct.gov/coronavirus. This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education. Data represent daily averages for two-week periods by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary. 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). These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures. For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html 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/CT-School-Learning-Model-Indicators-by-County/rpph-4ysy 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).

Over 12 million people in the United States died from all causes between the beginning of January 2020 and August 21, 2023. Over 1.1 million of those deaths were with confirmed or presumed COVID-19.

Vaccine rollout in the United States Finding a safe and effective COVID-19 vaccine was an urgent health priority since the very start of the pandemic. In the United States, the first two vaccines were authorized and recommended for use in December 2020. One has been developed by Massachusetts-based biotech company Moderna, and the number of Moderna COVID-19 vaccines administered in the U.S. was over 250 million. Moderna has also said that its vaccine is effective against the coronavirus variants first identified in the UK and South Africa.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, the school learning model indicator metrics will be calculated using a 14-day average rather than a 7-day average. The new school learning model indicators dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/CT-School-Learning-Model-Indicators-by-County-14-d/e4bh-ax24

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).

This dataset includes the leading and secondary metrics identified by the Connecticut Department of Health (DPH) and the Department of Education (CSDE) to support local district decision-making on the level of in-person, hybrid (blended), and remote learning model for Pre K-12 education.

Data represent daily averages for each week by date of specimen collection (cases and positivity), date of hospital admission, or date of ED visit. Hospitalization data come from the Connecticut Hospital Association and are based on hospital location, not county of patient residence. COVID-19-like illness includes fever and cough or shortness of breath or difficulty breathing or the presence of coronavirus diagnosis code and excludes patients with influenza-like illness. All data are preliminary.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

These metrics were adapted from recommendations by the Harvard Global Institute and supplemented by existing DPH measures.

For national data on COVID-19, see COVID View, the national weekly surveillance summary of U.S. COVID-19 activity, at https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

This dataset is no longer being updated as of m/d/yyyy. It is being retained on the Open Data Portal for its potential historical interest.

This table shows selected demographic information for Cambridge residents living in skilled nursing or assisted living facilities who are classified as confirmed, probable, or suspect cases (see “Case Count by Classification” section for definitions). Demographic information includes gender, age range, and race/ethnicity.

About the COVID-19 Rapid Testing Program: On April 9, the Broad Institute, in partnership with the City of Cambridge and Pro EMS, launched a surveillance testing pilot program in Cambridge skilled nursing and assisted living facilities. The goal of the program is to gain an accurate picture of the true infection rate in these facilities by testing all residents and workers regardless of whether they have symptoms or feel ill. Positive cases among facility residents reflect three rounds of testing in April and May of all residents at the seven skilled nursing and assisted living facilities in Cambridge, as well as other testing ordered by medical providers.

Of note:

The case count includes those who have recovered, are currently sick with COVID-19, and who have died from complications of the disease. Any category with a case count less than five is omitted to protect individual privacy. The Cambridge case count reflects current data received from the Massachusetts Department of Public Health.

It is important to note that race and ethnicity data are collected and reported by multiple entities and may or may not reflect self-reporting by the individual case. The Cambridge Public Health Department (CPHD) is actively reaching out to cases to collect this information. Due to these efforts, race and ethnicity information have been confirmed for over 80% of Cambridge cases, as of June 2020. Race/Ethnicity Category Definitions: “White” indicates “White, not of Hispanic origin.” “Black” indicates “Black, not of Hispanic origin.” “Hispanic” refers to a person having Hispanic origin. A person having Hispanic origin may be of any race. “Asian” indicates “Asian, not of Hispanic origin.” "Unknown" indicates that the originating reporter or reporting system did not capture race and ethnicity information or the individual refused to provide the information. "Other" indicates multiple races, another race that is not listed above, and cases who have reported nationality in lieu of a race category recognized by the US Census. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts. "Other" also includes a small number of people who identify as Native American or Native Hawaiian/Pacific islander. Because the count for Native Americans or Native Hawaiian/Pacific Islanders is currently < 5 people, these categories have been combined with “Other” to protect individual privacy.

The table is updated daily at 4 p.m.

**Living in a facility is defined as a Cambridge resident who lives in a skilled nursing or assisted living facility.

^Positive cases among facility residents reflect three rounds of testing in April and May of all residents at the seven skilled nursing and assisted living facilities in Cambridge, as well as other testing ordered by medical providers.

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