Context

After observing many naive conversations about COVID-19, claiming that the pandemic can be blamed on just a few factors, I decided to create a data set, to map a number of different data points to every U.S. state (including D.C. and Puerto Rico).

Content

This data set contains basic COVID-19 information about each state, such as total population, total COVID-19 cases, cases per capita, COVID-19 deaths and death rate, Mask mandate start, and end dates, mask mandate duration (in days), and vaccination rates.

However, when evaluating a pandemic (specifically a respiratory virus) it would be wise to also explore the population density of each state, which is also included. For those interested, I also included political party affiliation for each state ("D" for Democrat, "R" for Republican, and "I" for Puerto Rico). Vaccination rates are split into 1-dose and 2-dose rates.

Also included is data ranking the Well-Being Index and Social Determinantes of Health Index for each state (2019). There are also several other columns that "rank" states, such as ranking total cases per state (ascending), total cases per capita per state (ascending), population density rank (ascending), and 2-dose vaccine rate rank (ascending). There are also columns that compare deviation between columns: case count rank vs population density rank (negative numbers indicate that a state has more COVID-19 cases, despite being lower in population density, while positive numbers indicate the opposite), as well as per-capita case count vs density.

Acknowledgements

Several Statista Sources: * COVID-19 Cases in the US * Population Density of US States * COVID-19 Cases in the US per-capita * COVID-19 Vaccination Rates by State

Other sources I'd like to acknowledge: * Ballotpedia * DC Policy Center * Sharecare Well-Being Index * USA Facts * World Population Overview

Inspiration

I would like to see if any new insights could be made about this pandemic, where states failed, or if these case numbers are 100% expected for each state.

Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. This dataset will receive a final update on June 1, 2023, to reconcile historical data through May 10, 2023, and will remain publicly available.

This archived public use dataset contains historical case and percent positivity data updated weekly for all available counties and jurisdictions. Each week, the dataset was refreshed to capture any historical updates. Please note, percent positivity data may be incomplete for the most recent time period.

Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with historical case and percent positivity data for each county from January 22, 2020 (Weekly Historical Changes dataset) and a dataset with the levels as originally posted (Weekly Originally Posted dataset) since October 20, 2022. Please navigate to the Weekly Originally Posted dataset for the Community Transmission Levels published weekly on Thursdays.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2 Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.

This dataset is created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Archived data CDC has archived two prior versions of these datasets. Both versions contain the same 7 data elements reflecting community transmission levels for all available counties and jurisdictions; however, the datasets updated daily. The archived datasets can be found here:

Archived Originally Posted dataset

Archived Historical Changes dataset

Archived Data Notes:

October 27, 2022: Due to a processing issue this dataset will not be posted this week. CDC is currently working to address the issue and will publish the data when able.

November 10, 2022: As of 11/10/2022, this dataset will continue to incorporate historical updates made to case and percent positivity data; however, community transmission level will only be published in the corresponding Weekly COVID-19 County Level of Community Transmission as Originally Posted dataset (Weekly Originally Posted dataset).

Note:

October 20, 2022: Due to a data reporting error, the case rate for Philadelphia County, Pennsylvania is lower than expected in the COVID-19 Community Transmission Level data released on October 20, 2022. This could lead to the COVID-19 Community Transmission Level for Philadelphia County being underestimated; therefore, it should be interpreted with caution.

November 3, 2022: Due to a reporting cadence issue, case rates for Missouri counties are calculated based on 11 days’ worth of case count data in the COVID-19 Community Transmission Level data released on November 3, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for Missouri counties being overestimated; therefore, they should be interpreted with caution.

November 10, 2022: Due to a reporting cadence change, case rates for Alabama counties are calculated based on 13 days’ worth of case count data in the COVID-19 Community Transmission Level data released on November 10, 2022, instead of the customary 7 days’ worth of data. This could lead to the COVID-19 Community Transmission Levels metrics for Alabama counties being overestimated; therefore, they should be interpreted with caution.

November 10, 2022: Per the request of the jurisdiction, cases among non-residents have been removed from all Hawaii county totals throughout the entire time series. Cumulative case counts reported by CDC will no longer match Hawaii’s COVID-19 Dashboard, which still includes non-resident cases. 

November 10, 2022: In the COVID-19 Community Transmission Level data released on November 10, 2022, multiple municipalities in Puerto Rico are reporting higher than expected increases in case counts. CDC is working with territory officials to verify the data submitted. 

December 1, 2022: Due to cadence changes over the Thanksgiving holiday, case rates for all Ohio counties are reported as 0 in the COVID-19 Community Transmission Level data released on December 1, 2022. Therefore, the COVID-19 Community Transmission Levels may be underestimated and should be interpreted with caution. 

December 22, 2022: Due to an internal revision process, case rates for some Tennessee counties may appear higher than expected in the December 22, 2022, weekly release. Therefore, the COVID-19 Community Transmission Levels metrics for some Tennessee counties may be overestimated and should be interpreted with caution.

December 22, 2022: Due to reporting of a backlog of historic COVID-19 cases, case rates for some Louisiana counties will appear higher than expected in the December 22, 2022, weekly release. Therefore, the COVID-19 Community Transmission Levels metrics for some Louisiana counties may be overestimated and should be interpreted with caution.

December 29, 2022: Due to technical difficulties, county data from Alabama could not be incorporated via standard practices. As a result, case and death metrics will be reported as 0 in the December 29, 2022, weekly release. Therefore, the COVID-19 Community Transmission Levels metrics for Alabama counties will be underestimated and should be interpreted with caution.

January 5, 2023: Due to a reporting cadence issue, case rates for all Alabama counties will be calculated based on 14 days’ worth of case count data in the COVID-19 Community Transmission Level information released on January 5, 2023, instead of the customary 7 days’ worth of case count data. Therefore, the weekly case rates will be overestimated, which could affect counties’ COVID-19 Community Transmission Level classification and should be interpreted with caution.

January 5, 2023: Due to North Carolina’s holiday reporting cadence, aggregate case data will contain 14 days’ worth of data instead of the customary 7 days. As a result, case metrics will appear higher than expected in the January 5, 2023, weekly release. COVID-19 Community Transmission metrics may be overestimated and should be interpreted with caution.

January 12, 2023: Due to data processing delays, Mississippi’s aggregate case data will be reported as 0. As a result, case metrics will appear lower than expected in the January 12, 2023, weekly release. COVID-19 Community Transmission metrics may be underestimated and should be interpreted with caution. 

January 13, 2023: Aggregate case data released for Los Angeles County, California for the week of December 22nd, 2022, and December 29th, 2022, have been corrected for a data processing error.

January 19, 2023: Due to a reporting cadence issue, Mississippi’s aggregate case data will be calculated based on 14 days’ worth of data instead of the customary 7 days in the January 19, 2023, weekly release. Therefore, COVID-19 Community Transmission metrics may be overestimated and should be interpreted with caution.

January 26, 2023: Due to a reporting backlog of historic COVID-19 cases, case rates for two Michigan counties

BackgroundChikungunya virus (CHIKV) is transmitted by Aedes species mosquitoes and is the cause of an acute febrile illness characterized by potentially debilitating arthralgia. After emerging in the Caribbean in late 2013, the first locally-acquired case reported to public health authorities in Puerto Rico occurred in May 2014. During June–August 2014, household-based cluster investigations were conducted to identify factors associated with infection, development of disease, and case reporting.Methodology/Principal FindingsResidents of households within a 50-meter radius of the residence of laboratory-positive chikungunya cases that had been reported to Puerto Rico Department of Health (PRDH) were offered participation in the investigation. Participants provided a serum specimen and answered a questionnaire that collected information on demographic factors, household characteristics, recent illnesses, healthcare seeking behaviors, and clinical diagnoses. Current CHIKV infection was identified by rRT-PCR, and recent CHIKV infection was defined by detection of either anti-CHIKV IgM or IgG antibody. Among 250 participants, 74 (30%) had evidence of CHIKV infection, including 12 (5%) with current and 62 (25%) with recent CHIKV infection. All specimens from patients with CHIKV infection that were collected within four days, two weeks, and three weeks of illness onset were positive by RT-PCR, IgM ELISA, and IgG ELISA, respectively. Reporting an acute illness in the prior three months was strongly associated with CHIKV infection (adjusted odds ratio [aOR] = 21.6, 95% confidence interval [CI]: 9.24–50.3). Use of air conditioning (aOR = 0.50, 95% CI = 0.3–0.9) and citronella candles (aOR = 0.4, 95% CI = 0.1–0.9) were associated with protection from CHIKV infection. Multivariable analysis indicated that arthralgia (aOR = 51.8, 95% CI = 3.8–700.8) and skin rash (aOR = 14.2, 95% CI = 2.4–84.7) were strongly associated with CHIKV infection. Hierarchical cluster analysis of signs and symptoms reported by CHIKV-infected participants demonstrated that fever, arthralgia, myalgia, headache, and chills tended to occur simultaneously. Rate of symptomatic CHIKV infection (defined by arthralgia with fever or skin rash) was 62.5%. Excluding index case-patients, 22 (63%) participants with symptomatic CHIKV infection sought medical care, of which 5 (23%) were diagnosed with chikungunya and 2 (9%) were reported to PRDH.Conclusions/SignificanceThis investigation revealed high rates of CHIKV infection among household members and neighbors of chikungunya patients, and that behavioral interventions such as use of air conditioning were associated with prevention of CHIKV infection. Nearly two-thirds of patients with symptomatic CHIKV infection sought medical care, of which less than one-quarter were reportedly diagnosed with chikungunya and one-in-ten were reported to public health authorities. These findings emphasize the need for point-of-care rapid diagnostic tests to optimize identification and reporting of chikungunya patients.

BackgroundDengue is a leading cause of morbidity throughout the tropics; however, accurate population-based estimates of mortality rates are not available.Methods/Principal FindingsWe established the Enhanced Fatal Acute Febrile Illness Surveillance System (EFASS) to estimate dengue mortality rates in Puerto Rico. Healthcare professionals submitted serum and tissue specimens from patients who died from a dengue-like acute febrile illness, and death certificates were reviewed to identify additional cases. Specimens were tested for markers of dengue virus (DENV) infection by molecular, immunologic, and immunohistochemical methods, and were also tested for West Nile virus, Leptospira spp., and other pathogens based on histopathologic findings. Medical records were reviewed and clinical data abstracted. A total of 311 deaths were identified, of which 58 (19%) were DENV laboratory-positive. Dengue mortality rates were 1.05 per 100,000 population in 2010, 0.16 in 2011 and 0.36 in 2012. Dengue mortality was highest among adults 19–64 years and seniors ≥65 years (1.17 and 1.66 deaths per 100,000, respectively). Other pathogens identified included 34 Leptospira spp. cases and one case of Burkholderia pseudomallei and Neisseria meningitidis.Conclusions/SignificanceEFASS showed that dengue mortality rates among adults were higher than reported for influenza, and identified a leptospirosis outbreak and index cases of melioidosis and meningitis.

Reported and estimated rate of medically attended dengue per 1,000 general population in Puerto Rico (2005 to 2010).