Tennessee

Note: Effective September 27, 2023, this dataset will no longer be updated.  This dataset shows health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19).   Number of conditions reported in this table are tabulated from deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more. Conditions contributing to the death were identified using the International Classification of Diseases, Tenth Revision (ICD-10). Deaths involving more than one condition (e.g., deaths involving both diabetes and respiratory arrest) were counted in both totals. To avoid counting the same death multiple times, the numbers for different conditions should not be summated. Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1  

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Data cited at: Homebase https://joinhomebase.com/data/covid-19/. This dataset is based on Homebase data covering 60,000 businesses and 1 million hourly employees active in these metropolitan areas in January 2020.   All the rates compare that day vs. the median for that day of the week for the period Jan 4, 2020 – Jan 31, 2020. In other words, they show the extent to which Covid-19 has impacted Main St. as compared to pre-Covid levels. 

Data cited at: COVID-19 Vaccine Projections https://covid19-projections.com/path-to-herd-immunity/

Overview of Variants in Countries: This dataset contains, the proportion of total number of sequences (not cases), over time, that fall into defined variant groups. Countries are displayed if they have at least 70 sequences in any variant being tracked, over a period of at least 4 weeks. Countries are ordered by total number of sequences in tracked variants.   It is worth interpreting with caution:Not all samples are representative - sometimes some samples are more likely to be sequenced than others (for containing a particular mutation, for example)The last data point - this often has incomplete data and may change as more sequences come inFrequencies that are very 'jagged' - this often indicates low sequencing numbers and so may not be truly representative of the countryIn many places, sampling may not be equal across the region: samples may only cover one area or certain areas. It's important not to assume frequencies shown are necessarily representative.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by race, age, and state.   Number of deaths reported in this dataset are the total number of deaths received and coded as of the date of analysis, and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more.

Data cited at: Rt.live-https://rt.live/ These are up-to-date values for Rt, a key measure of how fast the virus is growing. It’s the average number of people who become infected by an infectious person. If Rt is above 1.0, the virus will spread quickly. When Rt is below 1.0, the virus will stop spreading. The reason historical values change is that source is not producing a single point each day, but rather a single curve. One of the constraints of the model is that this curve be connected and smooth. So, if new data suggests that R~t~ should be higher, it will pull up previous values so that the newest point is connected. Imagine a rope lying on the ground. If you pick up the end of that rope, the rope needs to slope up to your hand. The same thing is roughly happening with the model. If all of a sudden a testing center releases far more tests than were expected, the R~t~ curve increases which drags up previous values of R~t~. Since case data is staggered in its arrival, a bunch of new cases will sometimes rewrite its view of history given the new data. 

Estimates of excess deaths can provide information about the burden of mortality potentially related to COVID-19, beyond the number of deaths that are directly attributed to COVID-19. Excess deaths are typically defined as the difference between observed numbers of deaths and expected numbers. This visualization provides weekly data on excess deaths by jurisdiction of occurrence. Counts of deaths in more recent weeks are compared with historical trends to determine whether the number of deaths is significantly higher than expected. Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this web page were calculated using Farrington surveillance algorithms (1). For each jurisdiction, a model is used to generate a set of expected counts, and the upper bound of the 95% Confidence Intervals (95% CI) of these expected counts is used as a threshold to estimate excess deaths. Observed counts are compared to these upper bound estimates to determine whether a significant increase in deaths has occurred. Provisional counts are weighted to account for potential under reporting in the most recent weeks. However, data for the most recent week(s) are still likely to be incomplete. Only about 60% of deaths are reported within 10 days of the date of death, and there is considerable variation by jurisdiction.

In December 2022, IHME paused its COVID-19 modeling. IHME has developed projections for total and daily deaths, daily infections and testing, hospital resource use, and social distancing due to COVID-19 for a number of countries. Forecasts at the subnational level are included for select countries. The projections for total deaths, daily deaths, and daily infections and testing each include a reference scenario: Current projection, which assumes social distancing mandates are re-imposed for 6 weeks whenever daily deaths reach 8 per million (0.8 per 100k). They also include two additional scenarios: Mandates easing, which reflects continued easing of social distancing mandates, and mandates are not re-imposed; and Universal Masks, which reflects 95% mask usage in public in every location. Hospital resource use forecasts are based on the Current projection scenario. Social distancing forecasts are based on the Mandates easing scenario. These projections are produced with a model that incorporates data on observed COVID-19 deaths, hospitalizations, and cases, information about social distancing and other protective measures, mobility, and other factors. They include uncertainty intervals and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policy makers, and the public with crucial information about how expected need aligns with existing resources, so that cities and countries can best prepare. Dataset contains Observed and Projected data

Disconnected youth, also referred to as opportunity youth, are teenagers and young adults between the ages of 16 and 24 who are neither in school nor working Research reveals that being disconnected as a young person has long-term consequences; it’s associated with lower earnings, less education, worse health, and even less happiness in later adulthood. The duration i.e. how long a young person is disconnected also matters, with longer spells of disconnection associated with worse outcomes. This dataset shows the impact of Covid-19 on disconnected youth rate in the USA.

Data cited at: Prof.Prof. Lauren Gardner; Center for Systems Science and Engineering at John Hopkins University, blog Post -  https://systems.jhu.edu/research/public-health/ncov/   On December 31, 2019, the World Health Organization (WHO) was informed of an outbreak of “pneumonia of unknown cause” detected in Wuhan City, Hubei Province, China – the seventh-largest city in China with 11 million residents. As of February 04, 2020, there are over 24,502 cases confirmed globally, including cases in at least 30 regions in China and 30 countries.  Interests: In-Market Segments Knoema All Users   Knoema modified the original dataset to include calculations per million.   https://knoema.com/WBPEP2018Oct https://knoema.com/USICUBDS2020 https://knoema.com/NBSCN_P_A_A0301 https://knoema.com/IMFIFSS2017Nov https://knoema.com/AUDSS2019 https://knoema.com/UNAIDSS2017 https://knoema.com/UNCTADPOPOCT2019Nov https://knoema.com/WHOWSS2018 https://knoema.com/KPMGDHC2019

Data citation:International Long Term Care Policy Network, Avik Roy, The Foundation for Research on Equal Opportunity retrieved from The Foundation for Research on Equal Opportunity with approval dated 25th August 2020.

Note: The source has discontinued this dataset with note-"With the discontinuation of the database that is the input for the MEI, we will no longer update the index after March 31, 2021. For questions, please contact Tyler Atkinson ( tyler.atkinson@dal.frb.org < tyler.atkinson@dal.frb.org>;)"   The Dallas Fed Mobility and Engagement Index (formerly the “Social Distancing Index”) summarizes the information in seven different variables based on geolocation data collected from a large sample of mobile devices to gain insight into the economic impact of the pandemic. The Mobility and Engagement Index measures the deviation from normal mobility behaviors induced by COVID-19. The updated name recognizes that social distancing, or the limiting of close contact with others outside your household, can be practiced while mobility and engagement improve. Along with revising the index’s name, we also changed the sign of the index to make it more intuitive as a measure of mobility and engagement. The underlying data is provided by SafeGraph. The national series is aggregated from county-level data with device counts as weights. Similar for the states. In the county files, the county name is in the first row, with FIPS code in the variable name. MSA data are for metro statistical areas (MSA), aggregated from county data using the March 2020. MSA names are in the first row, and CBSA codes in the variable name. The index is scaled so that the average of January-February is zero, and the lowest weekly value (week ended April 11) is -100. File names including 'weekly' are averages of the daily data. The data corresponds to the last day of the calendar week.

We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data. Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population. This information will be available for a limited time during the COVID‑19 pandemic.

Data cited at: AllClear.App-https://home.allclear.app/  

Index Period: January 4th - January 31st Indexing Type: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 1 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia and influenza reported to NCHS by place of death and state, United States.

Deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group and state.   Number of deaths reported in this table are the total number of deaths received and coded as of the date of analysis, and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more.

Provisional count of deaths involving coronavirus disease 2019 (COVID-19) by United States county.   Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Counties included in this table have 10 or more COVID-19 deaths at the time of analysis. Number of deaths reported in this table are the total number of deaths received and coded as of the date of analysis and do not represent all deaths that occurred in that period. Data during this period are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes.

This data file contains the following indicators that can be used to illustrate potential differences in the burden of deaths due to COVID-19 according to race and ethnicity: •Count of COVID-19 deaths: Number of deaths due to COVID-19 reported for each race and Hispanic origin group •Distribution of COVID-19 deaths (%): Deaths for each group as a percent of the total number of COVID-19 deaths reported •Unweighted distribution of population (%): Population of each group as a percent of the total population •Weighted distribution of population (%): Population of each group as percent of the total population after accounting for how the race and Hispanic origin population is distributed in relation to the geographic areas impacted by COVID-19

Rates of Laboratory-Confirmed RSV, COVID-19, and Flu Hospitalizations from the RESP-NET Surveillance Systems

 Change in seated diners by month / day, 2024 vs. 2023 This table measures the volume of seated diners on a daily/monthly basis in 2024 vs. 2023. For example, in Berlin on January 8, 2024, seated diners were up 8% compared to 2023. In the monthly view, data for the current month shows the YoY change in seated diners for the month-to-date (up until one day prior to the current date). For example, if the date is January 10, 2024, the data compares January 1 – 9, 2024 to the same range in 2023.

Reporting of new Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. According to the source, this dataset will receive a final update on June 1, 2023, to reconcile historical data.

Coronavirus in the United States

Note: Effective September 27, 2023, this dataset will no longer be updated. Provisional counts of deaths by the week the deaths occurred, by state of occurrence, and by select underlying causes of death. The dataset also includes weekly provisional counts of death for COVID-19, coded to ICD-10 code U07.1 as an underlying or multiple cause of death.