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.

After over two years of public reporting, the State Profile Report will no longer be produced and distributed after February 2023. The final release was on February 23, 2023. We want to thank everyone who contributed to the design, production, and review of this report and we hope that it provided insight into the data trends throughout the COVID-19 pandemic. Data about COVID-19 will continue to be updated at CDC’s COVID Data Tracker. The State Profile Report (SPR) is generated by the Data Strategy and Execution Workgroup in the Joint Coordination Cell, in collaboration with the White House. It is managed by an interagency team with representatives from multiple agencies and offices (including the United States Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention, the HHS Assistant Secretary for Preparedness and Response, and the Indian Health Service). The SPR provides easily interpretable information on key indicators for each state, down to the county level. It is a weekly snapshot in time that: Focuses on recent outcomes in the last seven days and changes relative to the month prior Provides additional contextual information at the county level for each state, and includes national level information Supports rapid visual interpretation of results with color thresholds

Download reports from the Massachusetts Department of Public Health (DPH), March 2020-December 2021.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated.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.

View daily updates and historical trends for Massachusetts Coronavirus Cases (DISCONTINUED). Source: Center for Disease Control and Prevention. Track econ…

View daily updates and historical trends for Massachusetts Coronavirus Cases Currently Hospitalized. Source: US Department of Health & Human Services. Tra…

View dashboards that show data on COVID-19 incidences among staff and patients in state facilities and congregate care sites, and mobile testing results. Published by the Executive Office of Health and Human Services (EOHHS).

The dashboard includes COVID-19 cases, testing, and hospitalizations data. It also contains data on: city/town specific metrics; confirmed and probable cases; testing; age groups, race and ethnicity, and sex of cases; hospitalizations and deaths; hospital capacity.

Context

Massachusetts has been publishing city and town COVID-19 case rate data for about three weeks now. I've been making maps out of it with R (e.g., here), and have written up a bit about how in my blog.

Content

Each week's data is complete for January 1st through the given date (April 14th, 22nd, and 29th, so far). The state publishes a case count and a rate (per 100,000) per city, plus a count for where the city was unknown and the state totals (last two rows). Raw state data columns are marked with the date. Adjusted columns have some estimates in them of case counts (where the state was vague), and rates (where the state omitted them), but otherwise match the state data. The county and population are included for each city.

Note that the population data is from the US Census and is not the same as the populations the state used to calculate their rates.

Acknowledgements

Massachusetts Department of Public Health data: current edition. I have archived the previous versions to do the diffs, but you can probably get your own copies from the WayBack Machine. (If you do so, avoid the bad data from the evening of April 22nd.) Data was extracted using the R package docxtractor.

Census data: This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau. Data was obtained from the estimates API (2018) using the R package tidycensus.

Inspiration

I made some maps from this data, both static and with Leaflet, to try to get a better sense of what was going on in the state and where. The latest one is here. Suggestions for improvement are welcome.

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

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

References

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.

Overview

As coronavirus cases have exploded across the country, states have struggled to obtain sufficient personal protective equipment such as masks, face shields, gloves and ventilators to meet the needs of healthcare workers. FEMA began distributing PPE from the national stockpile as well as PPE obtained from private manufacturers to states in March.

Initially, FEMA distributed materials based primarily on population. By late March, Its methods changed to send more PPE to hotspot locations, and FEMA claimed these decisions were data-driven and need-based. By late spring, the agency was considering requests from states as well.

Although all U.S. states and territories have received some amount of PPE from FEMA, the amounts of PPE states have per capita and per positive COVID-19 case vary widely.

The AP used this data in a story that ran July 7.

Findings

• Overall, low population, rural states have the most PPE per positive case as of mid-June. This generally held true across types of equipment.
• The states that had the highest number of total PPE items per coronavirus case as of mid-May were, in descending order: Alaska, Montana, Vermont, Hawaii, Wyoming, and North Dakota. The highest was Alaska with 1,579 PPE items per coronavirus case.
• The states that had the highest number of total items per case as of mid-June were largely the same states — Montana, Alaska, Hawaii, Vermont, Wyoming, and West Virginia. The highest was Montana with 1,125 PPE items per coronavirus case.
• Conversely, the states that had the lowest amounts of PPE per positive case in mid-May included hotspot states — Massachusetts, New York, Virginia, California, Nebraska, and Iowa. New Jersey was just a couple spots further down. The lowest was Massachusetts with 36 PPE items per coronavirus case.
• The states that had the lowest amounts of PPE per case as of mid-June were largely the same as well — Massachusetts, New York, Iowa, California, and Nebraska. The lowest was Massachusetts with 32 PPE items per coronavirus case.
• When evaluated on a per-capita basis rather than per positive coronavirus case, the picture is different. The District of Columbia received the most PPE per capita in both May and June, although the vast majority of the PPE it received was distributed as of mid-May. Vermont, Kansas, New Jersey, and North Dakota had the next highest numbers of PPE per capita as of both mid-May and mid-June.
• There is no clear pattern of FEMA distribution by party control of states.

About the data

These numbers include material distributed by FEMA and also those sold by private distributors under direction from FEMA. They include materials both delivered to and en route to states.

States have purchased PPE directly in addition to receiving PPE from FEMA or directed there by the agency, and this data only includes the latter categories.

FEMA also distributed and directed the distribution of gear to U.S. territories in addition to states, which are included in FEMA’s release linked below, but not are not included in this data.

FEMA has publicly distributed its breakdown of PPE delivery by state for May and June. FEMA did not provide comprehensive numbers for each state before May.

These numbers are cumulative, meaning that the numbers for May include items of PPE distributed prior to May 14, dating to when the agency began allocations on March 1. The June numbers include the May numbers and any new PPE distributions since then.

The population column, which was used to calculate the numbers of PPE items per state, came from data from the U.S Census Bureau. Since the Census releases annual population data, population data from 2019 was used for each state.

The numbers of coronavirus cases were pulled from the data released daily by Johns Hopkins University as of the dates that FEMA released its distribution numbers — May 14 and June 10.

Caveats

The data includes amounts of gear that had been delivered to the states or were en route as of the reporting dates.

All PPE item numbers above 1 million were rounded to the nearest hundred thousand by FEMA, but numbers lower than that were not rounded.

In some cases, gear headed to a state was rerouted because it was needed more somewhere else or a state decided it did not need it. In some instances, that resulted in states having higher numbers for certain supplies in May than in June.

COVID-19 deeply impacted communities across Massachusetts, but people of color are bearing a higher burden of cases and deaths relative to their population size.

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

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 12/8/2020. It is being retained on the Open Data Portal for its potential historical interest.

For the more recent version of this data (which is also now historical), please visit: https://data.cambridgema.gov/Public-Safety/COVID-19-Case-Count-by-Date/axxk-jvk8

The data represented in this graph are dynamic and may change over time.

Since March 20, the Cambridge Public Health Department (CPHD) has provided data regarding COVID-19 case counts based on the date that the Massachusetts Public Health Department (MDPH) reported the case to CPHD (known as report date).

Beginning March 31, 2020, CPHD began providing COVID-19 case counts based on the onset time -- the date of a positive diagnosis -- which is preferable for studying disease patterns over time. Both sets of data provide the same number of total cases. CPHD often receives onset time (positive COVID-19 diagnosis) after the report date. Thus, the graph displaying onset time is recommended as a representation of the case counts per day.

Daily case counts reflect the total number of cases to date, including active and recovered individuals.

The predicted number of cumulative death produced by the model over time for three different quarantine scenarios and three time periods together with the corresponding 90% prediction intervals.

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.

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table reports case classification and status data.

The "test mode" rows show confirmed and probable case counts for all Cambridge residents who have tested positive for COVID-19 or have been clinically diagnosed with the disease to date. The numbers represented in these rows reflect individual people (cases), not tests performed. If someone is clinically diagnosed and later gets an antibody test, for example, they will be removed from the “clinical diagnosis” category and added to the “antibody positive” category. Case classification is based on guidance from the Massachusetts Department of Public Health and is as follows:

Confirmed Case: A person with a positive viral (PCR) test for COVID-19. This test is also known as a molecular test.

Probable Case: A person with a positive antigen test. This test is also known as a rapid test.

A person who is a known contact of a confirmed case and has received a clinical diagnosis based on their symptoms. People in this category have not received a viral or antibody test. Whenever possible, lab results from a viral (PCR) test are used to confirm a clinical diagnosis, and if that is not feasible, antibody testing can be used.

Suspect Case: A person with a positive antibody test. This test is also known as a serology test.

The "case status" rows show current outcomes for all Cambridge residents who are classified as confirmed, probable, or suspect COVID-19 cases. Outcomes include:

Recovered Case: The Cambridge Public Health Department determines if a Cambridge COVID-19 case has recovered based on the Center for Disease Control and Prevention’s criteria for ending home isolation: https://www.cdc.gov/coronavirus/2019-ncov/hcp/disposition-in-home-patients.html. Staff from the Cambridge Public Health Department (CPHD) or the state’s Community Tracing Collaborative (CTC) follow up with all reported COVID-19 cases multiple times throughout their illness. It is through these conversations that CPHD or CTC staff determine when a Cambridge resident infected with COVID-19 has met the CDC criteria for ending isolation, which connotes recovery. While many people with mild COVID-19 illness will meet the CDC criteria for ending isolation (i.e., recovery) in under two weeks, people who survive severe illness might not meet the criteria for six weeks or more.

Active Case: This category reflects Cambridge COVID-19 cases who are currently infected. Note: There may be a delay in the time between a person being released from isolation (recovered) and when their recovery is reported.

Death: This category reflects total deaths among Cambridge COVID 19 cases.

Unknown Outcome: This category reflects Cambridge COVID-19 cases who public health staff have been unable to reach by phone or letter, or who have stopped responding to follow up from public health staff.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

IntroductionCorrectional facilities have environmental, resource, and organizational factors that facilitate SARS-CoV-2 transmission and challenge clinical testing of staff and residents. In Massachusetts, multiple state prisons implemented wastewater surveillance for strategic testing of individuals and isolation of COVID-19 cases early in the course of infection, as recommended by the Centers for Disease Control and Prevention (CDC). Our objective was to quantify the correlation of COVID-19 cases with facility-level wastewater surveillance compared to standard case surveillance in towns in closest geographic proximity to participating correctional facilities.Materials and methodsAvailable data included number of reported COVID-19 cases in residents from each of eight participating facilities (labeled A-H for anonymity), wastewater viral concentrations at each facility, and COVID-19 cases reported to routine surveillance in towns geographically nearest each facility. We selected data from December 2020-February 2022. Spearman's rank correlation was calculated at each facility to assess agreement between town cases and facility resident cases, and between wastewater concentrations and facility resident cases. We considered a correlation of ≤0.3 as weak and ≥0.6 as strong.ResultsFacilities housed a mean of 502 individuals (range 54–1,184) with mean staffing of 341 (range 53–547). In 7/8 facilities, the town/resident cases correlation coefficients (ρ) were statistically significant (range 0.22–0.65); in all facilities, the wastewater/facility resident cases correlations were statistically significant (range 0.57–0.82). Consistently, ρ values were higher for facility-specific wastewater/resident cases than for town/resident cases: A (0.65, 0.80), B (0.59, 0.81), C (0.55, 0.70), D (0.61, 0.82), E (0.46, 0.62), F (0.51, 0.70), and H (0.22, 0.57).ConclusionWe conclude that wastewater surveillance for SARS-CoV-2 can provide an additional signal to objectively supplement existing COVID-19 clinical surveillance for the early detection of cases and infection control efforts at correctional facilities.