Reporting of Aggregate Case and Death Count data was discontinued May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

Weekly COVID-19 Community Levels (CCLs) have been replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrate >99% concordance by county during February 2022–March 2023. For more information on the latest COVID-19 status levels in your area and hospital admission rates, visit United States COVID-19 Hospitalizations, Deaths, and Emergency Visits by Geographic Area.

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.

This archived public use dataset contains weekly community transmission levels data for all available counties and jurisdictions since October 20, 2022. The dataset was appended to contain the most recent week's data as originally posted on COVID Data Tracker. Historical corrections are not made to these data if new case or testing information become available. A separate archived file is made available here (: Weekly COVID-19 County Level of Community Transmission Historical Changes) if historically updated data are desired.

Related data CDC provides the public with two active versions of COVID-19 county-level community transmission level data: this dataset with the levels as originally posted (Weekly Originally Posted dataset), updated weekly with the most recent week’s data since October 20, 2022, and a historical dataset with the county-level transmission data from January 22, 2020 (Weekly Historical Changes dataset).

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 a 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 conducted

Announcement Beginning October 20, 2022, CDC will report and publish aggregate case and death data from jurisdictional and state partners on a weekly basis rather than daily. As a result, community transmission levels data reported on data.cdc.gov will be updated weekly on Thursdays, typically by 8 PM ET, instead of daily. This public use dataset has 7 data elements reflecting community transmission levels for all available counties. This dataset contains reported daily transmission level at the county level and contains the same values used to display transmission maps on the COVID Data Tracker. Each day, the dataset is appended to contain the most recent day's data. Transmission level is set to low, moderate, substantial, or high using the calculation rules below. Currently, CDC provides the public with two versions of COVID-19 county-level community transmission level data: this dataset with the levels as originally posted (Originally Posted dataset), updated daily with the most recent day’s data, and an historical dataset with the county-level transmission data from January 1, 2021 (Historical Changes dataset). 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 a 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 conducted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have a 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). If the two metrics suggest different transmission levels, the higher level is selected. Transmission categories include: Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%; Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%; Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%; High Transmission Threshold: Counties with 100 or more total cases per 100,000

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

The dashboard is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for COVID-19 in California. Test positivity for a given week is calculated by dividing the number of positive COVID-19 results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset (https://dof.ca.gov/forecasting/demographics/projections/) provided by the State of California Department of Finance. Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html). Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 PCR 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. 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).

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

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/MMWRweekoverview.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.

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.

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).

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.

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. COVID-19 cases, hospitalizations, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported d

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

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. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. 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. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

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. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by gender. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes. Starting in Ju

The goal is to predict the rate of heart disease (per 100,000 individuals) across the United States at the county-level from other socioeconomic indicators. The data is compiled from a wide range of sources and made publicly available by the United States Department of Agriculture Economic Research Service (USDA ERS).

There are 33 variables in this dataset. Each row in the dataset represents a United States county, and the dataset we are working with covers two particular years, denoted a, and b We don't provide a unique identifier for an individual county, just a row_id for each row.

The variables in the dataset have names that of the form category_variable, where category is the high level category of the variable (e.g. econ or health). variable is what the specific column contains.

We're trying to predict the variable heart_disease_mortality_per_100k (a positive integer) for each row of the test data set.

Columns

area — information about the county

area_rucc — Rural-Urban Continuum Codes "form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/)

area_urban_influence — Urban Influence Codes "form a classification scheme that distinguishes metropolitan counties by population size of their metro area, and nonmetropolitan counties by size of the largest city or town and proximity to metro and micropolitan areas." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/urban-influence-codes/)

econ — economic indicators

econ_economic_typology — County Typology Codes "classify all U.S. counties according to six mutually exclusive categories of economic dependence and six overlapping categories of policy-relevant themes. The economic dependence types include farming, mining, manufacturing, Federal/State government, recreation, and nonspecialized counties. The policy-relevant types include low education, low employment, persistent poverty, persistent child poverty, population loss, and retirement destination." (USDA Economic Research Service, https://www.ers.usda.gov/data-products/county-typology-codes.aspx)

econ_pct_civilian_labor — Civilian labor force, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_unemployment — Unemployment, annual average, as percent of population (Bureau of Labor Statistics, http://www.bls.gov/lau/)

econ_pct_uninsured_adults — Percent of adults without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/) econ_pct_uninsured_children — Percent of children without health insurance (Bureau of Labor Statistics, http://www.bls.gov/lau/)

health — health indicators

health_pct_adult_obesity — Percent of adults who meet clinical definition of obese (National Center for Chronic Disease Prevention and Health Promotion)

health_pct_adult_smoking — Percent of adults who smoke (Behavioral Risk Factor Surveillance System)

health_pct_diabetes — Percent of population with diabetes (National Center for Chronic Disease Prevention and Health Promotion, Division of Diabetes Translation)

health_pct_low_birthweight — Percent of babies born with low birth weight (National Center for Health Statistics)

health_pct_excessive_drinking — Percent of adult population that engages in excessive consumption of alcohol (Behavioral Risk Factor Surveillance System, )

health_pct_physical_inacticity — Percent of adult population that is physically inactive (National Center for Chronic Disease Prevention and Health Promotion)

health_air_pollution_particulate_matter — Fine particulate matter in µg/m³ (CDC WONDER, https://wonder.cdc.gov/wonder/help/pm.html)

health_homicides_per_100k — Deaths by homicide per 100,000 population (National Center for Health Statistics)

health_motor_vehicle_crash_deaths_per_100k — Deaths by motor vehicle crash per 100,000 population (National Center for Health Statistics)

health_pop_per_dentist — Population per dentist (HRSA Area Resource File)

health_pop_per_primary_care_physician — Population per Primary Care Physician (HRSA Area Resource File)

demo — demographics information

demo_pct_female — Percent of population that is female (US Census Population Estimates)

demo_pct_below_18_years_of_age — Percent of population that is below 18 years of age (US Census Population Estimates)

demo_pct_aged_65_years_and_older — Percent of population that is aged 65 years or older (US Census Population Estimates)

dem...

Context

These data contain counts and rates for Centers for Infectious Diseases-related disease cases among California residents by county, disease, sex, and year spanning 2001-2014 (As of September, 2015). Data were extracted on communicable disease cases with an estimated onset or diagnosis date from 2001 through 2014 from California Confidential Morbidity Reports and/or Laboratory Report that were submitted to CDPH by September 2015 and which met the surveillance case definition for that disease. A cleansing and exploration steps have been performed to generate the train and test datasets.

Content

The train dataset contains 75614 rows and the test data has 18904 rows ****Features:**** ****Disease****:Plain text: The name of the disease reported for the patient. ****County****: Plain text "The county in which the case resided when they were diagnosed and/or where they are currently receiving care; in most cases this will be the county that reported the case.
****Year ****:Number: Year is derived from the estimated illness onset date. We defined the estimated illness onset date for each case as the date closest to the time when symptoms first appeared. Because date of illness onset may not be recorded, the estimated date of illness onset can range from the first appearance of symptoms to the date the report was made to CDPH. For diseases with insidious illness onset (for instance, coccidioidomycosis), estimated illness onset was more frequently drawn from the diagnosis date Values include: years spanning 2001-2014, unless otherwise indicated below ****Sex ****:Plain text : Values include: Male, Female, **Count **:Number: The number of occurrences of each disease that meet the surveillance definition and/or inclusion criteria specific to that disease for that County, Year, Sex strata. National surveillance case definitions for these conditions can be found at http://wwwn.cdc.gov/nndss/case-definitions.html. ****Population ****:Number: The estimated population size (rounded to the nearest integer) for each County, Year, Sex strata. California Department of Finance (DOF) Population Projection data (P-3 data table) were used to determine the population proportion of a particular demographic subgroup relative to the total State/County population for a given year. These proportions were then applied to the DOF Estimate totals (E-2 data table) for the given State/County and year total, to obtain the estimates used. These data are available at http://www.dof.ca.gov/research/demographic/reports/view.php. Value: a number (a positive integer)" ****Rate ****:Number:The rate of disease per 100,000 population for the corresponding County, Year, Sex strata using the standard calculation (Count *100,000/Population) Value: a number (a positive real number xxx.xxx)" ****CI.lower****:Number: The lower bound of the 95% confidence interval for the calculated rate. The confidence interval was calculated with the R software package (R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.) using the ""Exact Pearson-Klopper"" method as implement in the ""binom"" package (Sundar Dorai-Raj (2014). binom: Binomial Confidence Intervals For Several Parameterizations. R package version 1.1-1. http://CRAN.R-project.org/package=binom) Value: a number (a positive real number xxx.xxx)" ****CI.uppe**r**:Number:The upper bound of the 95% confidence interval for the calculated rate, calculated as above. Value: a number (a positive real number xxx.xxx)"

Acknowledgements

NSSP Emergency Department (ED) Visit Trajectories by State and Sub-State Regions- COVID-19, Flu, RSV, Combined. This dataset provides the percentage of emergency department patient visits for the specified pathogen of all ED patient visits for the specified geographic part of the country that were observed for the given week from data submitted to the National Syndromic Surveillance Program (NSSP). In addition, the trend over time is characterized as increasing, decreasing or no change, with exceptions for when there are no data available, the data are too sparse, or there are not enough data to compute a trend. These data are to provide awareness of how the weekly trend is changing for the given geographic region.  Note that the reported sub-state trends are from Health Service Areas (HSA) and the data reported from the health care facilities located within the given HSA. Health Service Areas are regions of one or more counties that align to patterns of care seeking. The HSA level data are reported for each county in the HSA. More information on HSAs is available here. For the emergency department time series, trajectory classifications reported on for sub-state (HSA) emergency department time series, trajectory classifications are based on approximations of the first derivative (slope) of trends that are smoothed using generalized additive models (GAMs). To determine time intervals in which the slope is sufficiently changing (i.e., rate of change distinguishable from 0), 95% confidence intervals for the slope approximations are calculated and assessed. Weeks with a 95% confidence interval not containing 0 are classified as increasing if the slope estimate is positive and decreasing if the slope estimate is negative. Weeks with a 95% confidence interval containing 0 are classified as stable. In the scenario that an HSA's time series is determined to be too sparse (i.e., many weeks with percentages of 0%), a model is not fit, and the HSA is classified as “sparse”. For additional information, please see: Companion Guide: NSSP Emergency Department Data on Respiratory Illness Updated once per week on Fridays.