The graph illustrates the number of deaths per day in the United States from 1950 to 2025. The x-axis represents the years, abbreviated from '50 to '24, while the y-axis indicates the daily number of deaths. Over this 75-year period, the number of deaths per day ranges from a low of 4,054 in 1950 to a high of 9,570 in 2021. Notable figures include 6,855 deaths in 2010 and 8,333 in 2024. The data shows a general upward trend in daily deaths over the decades, with recent years experiencing some fluctuations. This information is presented in a line graph format, effectively highlighting the long-term trends and yearly variations in daily deaths across the United States.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

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

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

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

Chile CL: Road Fatalities: 30 days data was reported at 433.200 Person in Dec 2024. This records a decrease from the previous number of 434.400 Person for Sep 2024. Chile CL: Road Fatalities: 30 days data is updated quarterly, averaging 503.750 Person from Mar 2014 (Median) to Dec 2024, with 44 observations. The data reached an all-time high of 580.800 Person in Sep 2021 and a record low of 360.000 Person in Sep 2020. Chile CL: Road Fatalities: 30 days data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Chile – Table CL.OECD.ITF: Road Traffic and Road Accident Fatalities: OECD Member: Quarterly. [COVERAGE] Number of road fatalities is defined as the number of road deaths in the 30 days following the accident. [STAT_CONC_DEF] Until 2018, data include people died within 24 hours after the crash. Then, data are corrected using the adjustment factor of 1.3 as suggested by the World Health Organisation (WHO). Since 2019, data include people died within 48 hours after the crash. Then, data are corrected using the adjustment factor of 1.2 as suggested by the World Health Organisation (WHO). Until 2013, data are not available.

ABSTRACT Background : The Covid-19 pandemic has caused very high death tolls across the world in the last two years. Geographic latitude, climate factors, and other human related conditions such as epidemiologic and demographic history are taught to have played a role in the prevalence of Covid-19. Objective : This observational study aimed to investigate possible relations between geographic latitude-associated climate factors and Covid-19 death numbers in 29 countries. The study also aimed to investigate the relationship between geographic latitude and the history of epidemiologic (cancer, Alzheimer's disease) and demographic factors (birth rate, mortality rate, fertility rate, people aged 65 and over), as well as alcohol intake habits. And finally, the study also aimed to evaluate the relationships between epidemiologic and demographic factors, as well as alcohol intake habits with Covid-19 deaths. Methods : We sought the Covid-19 death toll in 29 countries in Europe, Africa, and the Middle East (located in both hemispheres and between the meridian lines "-15°" and "+50°"). We obtained the death numbers for Covid-19 and other geographic (latitude, longitude) and climate factors (average annual temperature, sunshine hours, and UV index) and epidemiologic and demographic parameters as well as data on alcohol intake per capita from official web pages. Based on records of epidemiologic and demographic history, and alcohol intake data, we have calculated a General Immune Capacity (GIC) score for each country. Geographic latitude and climate factors were plotted against each of Covid-19 death numbers, epidemiologic and demographic parameters, and alcohol intake per capita. Data was analysed by simple linear regression or polynomial regression. All statistical data was collected using Microsoft Excell software (2016). Results : Our observational study found higher death numbers in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line and low latitudes. When the Covid-19 death numbers were plotted against the geographic latitude of each country, an inverted bell-shaped curve was obtained (coefficient of determination R2=0.553). In contrast, bell-shaped curves were obtained when latitude was plotted against annual average temperature (coefficient of determination R2= 0.91), average annual sunshine hours (coefficient of determination R2= 0.79) and average annual UV index (coefficient of determination R2= 0.89). In addition, plotting the latitude of each country against the General Immune Capacity score of each country gave an inverted bell-shaped curve (coefficient of determination R2=0.755). Linear regression analysis of the General Immune Capacity score of each country and its Covid-19 deaths showed a very significant negative correlation (coefficient of determination R² = 0,71, p=6.79x10-9). Linear regression analysis of the Covid-19 death number plotted against the average annual temperature temperature and the average annual sunshine hours or the average annual UV index gave very significant negative correlations with the following coefficients of determination: (R2 = 0.69, p = 1.94x10-8), (R2 = 0.536, p = 6.31x10-6) and (R2 = 0.599, p = 8.30x10-7), respectively. Linear regression analysis of the General Immune Capacity score of each country plotted against its average annual temperature temperature and the average annual sunshine hours or the average annual UV index gave very significant negative correlations, with the following coefficients of determination: (R2 = 0.86, p = 3.63x10-13), (R2 = 0.69, p = 2.18x10-8) and (R2 = 0.77, p= 2.47x10-10), respectively. Conclusion : The results of the present study prove that at certain geographic latitudes and their three associated climate parameters are negatively correlated to Covid-19 mortality. On the other hand, our data showed that the General Immune Capacity score, which includes many human related parameters, is inversely correlated to Covid-19 mortality. Likewise, geographic location and health and demographic history were key elements in the prevalence of the Covid-19 pandemic in a given country. On the other hand, the study points to the possible protective role of UV light against Covid-19. The therapeutic potential of UV light against the Covid-19 associated with SARS-Cov-2 is discussed.

ABSTRACT Background: Following two years of the Covid-19 pandemic, thousands of deaths were registered around the world, however, death tolls differed from a country to another. A question on whether climate parameters in each country could or not affects coronavirus incidence and Covid-19 death toll is under debate. Objective: In the present work, it is aimed to check the numbers of deaths caused by Covid-19 in 39 countries of four continents (America, Europe, Africa and Asia), and to analyse their possible correlation with climate parameters in a given country, such as the mean of annual temperature, the annual average sunshine hours and the annual average UV index in each country. Methods: We have sought the deaths number caused by Covid-19 in 39 countries and have analysed its correlation degree with the mean annual temperature, the average annual sunshine hours and the average annual UV index. Correlation and determination factors were obtained by Microsoft Exell software (2016). Results: In the present study, higher numbers of deaths related to Covid-19 were registered in many countries of Europe and America compared to other countries in Africa and Asia. On the other hand, after both the first year and the second year of the pandemic, the death numbers registered in the 39 countries of our study were very negatively correlated with the three climate factors of our study, namely, annual average temperature, sunshine hours and UV index. Conclusion:The results of the present study prove that the above climate parameters may have some kind of influence on the coronavirus incidence through a yet unknown mechanism. Our data support the hypothesis that countries which have elevated annual temperatures and elevated sunshine hours may be less vulnerable to the coronavirus SARS-CoV-2 and to its associated Covid-19 disease. Countries with the above characteristics have also elevated levels of average annual UV rays that might play a key role against the spread of the coronavirus.Thus, geographical latitude and longitude of a given country could have been the key points for the outcome of virus incidence and Covid-19 spread around the globe during the past two years. The results prove that elevated levels of temperature, sunshine hours and UV index could play a protective effect against the coronavirus, although their mechanisms of action are still unknown.

In 2023, the death rate in deaths per 1,000 inhabitants in India stood at ****. Between 1960 and 2023, the figure dropped by *****, though the decline followed an uneven course rather than a steady trajectory.

Abstract

Noncommunicable diseases (NCD) are the top cause of deaths. In 2008, more than 36 million people worldwide died of such diseases. Ninety per cent of those lived in low-income and middle-income countries.WHO Maps Noncommunicable Disease Trends in All Countries The STEPS Noncommunicable Disease Risk Factor Survey, part of the STEPwise approach to surveillance (STEPS) Adult Risk Factor Surveillance project by the World Health Organization (WHO), is a survey methodology to help countries begin to develop their own surveillance system to monitor and fight against noncommunicable diseases. The methodology prescribes three steps—questionnaire, physical measurements, and biochemical measurements. The steps consist of core items, core variables, and optional modules. Core topics covered by most surveys are demographics, health status, and health behaviors. These provide data on socioeconomic risk factors and metabolic, nutritional, and lifestyle risk factors. Details may differ from country to country and from year to year.

The overall aim of the Republic of the Marshall Islands (RMI) NCD STEPs survey was to determine the prevalence of and better understand major and associated risk factors for NCD, providing baseline information that would help develop a National Strategy for the Prevention and Control of NCDs.

The specific objectives of the RMI NCD Steps survey were: - To investigate and document the prevalence of key NCD risk factors amongst the target population. - To determine the prevalence of and better understand the major modifiable risk factors for common NCDs. These included physical inactivity, poor diet, obesity, high cholesterol, tobacco and alcohol abuse, and knowledge and attitude about diabetes and hypertension. - To study NCD and its risk factors across different stratas of age, gender, and locality.

Geographic coverage

50.9% of the respondents were from Majuro, 20.7% from Ebeye, 21.5% from the Outer Islands and 6.8% from the 177 Atoll.

Analysis unit

Household Individual

Universe

The 2002 RMI-STEPS survey was designed as a population-based cross-sectional survey of 15 to 64 year olds.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 3045 participants surveyed (15-64yrs) were selected through random locality-stratified multi-stage cluster sampling but with much of logistic consideration as the geography and communication were a big challenge. Detailed sampling information is available in section 4.2 of the survey report, provided under the related materials tab.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data for all three STEPS were hand-entered using EpiInfo 6.04d database. All questionnaires were kept in the Nutrition & Diabetes Prevention Office with access to data entry people, team leaders and project manager only. Each data entry personnel was able to enter from 15 to 30 questionnaires per hour. All questionnaires were entered twice. Upon completion, questionnaires were placed in boxes, sealed and stored in a secured storeroom. With the completion of this report, all questionnaires will be destroyed.

The project ´Quality of Life and Well-being of Very Old People in NRW (NRW80+)´, which is funded by the Ministry of Innovation, Science and Research of North Rhine-Westphalia and carried out by the CERES research association at the University of Cologne, is intended to provide representative statements on the living conditions of very old people in North Rhine-Westphalia. The aim is to obtain comprehensive information about the environment in which very old people live or would like to live, what their social role is and how satisfied they are with their living situation. Housing situation: type of housing; full inpatient care in the case of residential accommodation; number of rooms; duration of living in this apartment/house/home; tenure (owner, main tenant, subtenant, rent-free); always in this apartment/house or lived in this flat/house; barrier-reduced living: thresholds over 2 cm; doors at least 80 cm wide; stairs with handrail or stair lift; doors of bath and WC open to the outside; suitability of the living environment on foot or in a wheelchair (walkability); residential attachment; trust in people in the neighbourhood (social cohesion). 2. Family situation: marital status; currently stable partnership; children present; number of children; number of living children; number of grandchildren and great grandchildren; household size; household composition: sex of up to three persons and their relationship to the respondent; pets. 3. Financial situation: sources of income; net household income; costs: amount of the monthly rent for warmth; amount of the monthly rent for cold or rent without additional costs; amount of the monthly additional costs; housing loans or mortgages to be paid off and their amount; monthly costs for the stay in the home; debts from loans; amount of debts; assets: amount of the total assets. 4. Dealing with old age: autonomy; experience of ageing (e.g. greater appreciation of relationships and other people, more attention to one´s own health, decrease in mental capacity, etc.); appreciation by others (being needed, being appreciated for services, being treated as a burden, being appreciated more than before). 5. Health: cognitive tests on mental health (repeat ten selected words in two passes, convert numbers, mention as many things as possible that you can buy in the supermarket in one minute, repeat numbers in reverse order, remember the ten words at the beginning of the cognitive test); self-assessment of health; assessment of pain level in the last four weeks; height in cm; weight in kg; weight loss in the last twelve months; multimorbidity: medical treatment due to selected diseases; existence of care level or degree of care; designation of care level or degree of care; additional care level 0 (limited everyday competence); care use: use of an outpatient care service; use of day care; private care; hours of private care per week; respondent cares privately for another person and hours per week; functional health with regard to various activities of daily life (eating, dressing and undressing, personal hygiene, walking, looking up from bed and lying down, being bedridden, bathing or showering, reaching the toilet in time, frequency of problems with bladder and bowel control, using the telephone, organising routes outside the walking range (trips by taxi or bus), buying food and clothing yourself, preparing your own meals, doing housework, taking medication, regulating financial matters); use of assistive devices (hearing aid, wheelchair, home emergency call system, private car); health literacy (knowledge and compliance). 6. Everyday life and lifestyle: importance and frequency of: time spent together with other people, physical activity, rest and time for oneself, in-depth study of a topic and creative activity; preferred music style; preferences regarding clothing and food; leisure activities in the last 12 months (e.g. sports, participation in a coffee circle or regulars´ table, visiting a café, restaurant or pub, travelling, voluntary work, etc.); frequency and place of the respective activities; religious community, club membership; political participation: party affiliation; participation in the last federal election. 7. Technology setting and technology use: technology use in the last 12 months (computer or laptop, internet, smartphone, regular mobile phone, tablet computer, fitness wristband) and frequency of use; technology setting: interest, difficulties in using modern digital devices, ease of everyday life with modern digital devices); purpose of internet use in the last three months (emails, looking for information on health topics, participating in social networks, buying or selling goods or services). 8. Social inclusion: called social network; for the four most important persons the following was asked: sex, their relationship to the respondent, frequency of contact and attachment to these persons; number of other persons in the social network (size of the social network); frequency of loneliness in the last week; social support: larger gifts given or larger gifts received; frequency of social support given or received by the respondent (e.g. helped other people with their tasks, received help with tasks and tasks, received consolation, received consolation); Generativity (importance of passing on one´s own experiences to younger people, passing on social values to younger people, being a role model for younger people); Integration into society: Anomie (coping with today´s social way of life, one´s own values fit less and less with the values of today´s society, lack of orientation due to rapidly changing society). 9. Hand grip force: agreement with hand grip test; right- or left-handed; writing hand; test value 1st measurement right and left; test value 2nd measurement right and left; deviations exist. 10. Value system: Individual value system (doing things in one´s own way (self-determination), being wealthy (power), avoiding dangers and safe environment (security), spending good time (hedonism), doing good for society (benevolence), getting achievements recognized (achievement), taking risks (stimulation), avoiding teasing others (conformity), caring for nature and the environment (universalism), respecting traditions (tradition); spirituality: Importance of a connection with God or a higher power, with people and with nature; frequency of connection with God or a higher power, with people and with nature; importance of institutionalizing one´s own beliefs, e.g. in church; ; frequency of the feeling of community in institutionalized forms; orientation to the guidelines of religious institutions; importance of being part of a large entity; frequency of the feeling of being part of a larger entity; importance of practicing religious practices such as Praying or meditating, frequency of religious practices; reconciled relationship with God; God as support; desire to leave everything behind to go to God; God is threatening and punishing; importance of faith or spirituality in one´s own life; attitude towards dying and death: acceptance of one´s own mortality; death as an incriminating thought; fear of dying; frequency of thoughts about death; will written; dispositions (living will, precautionary power of attorney, care-giving will, general power of attorney). 11. Interpersonal personality: tendency to quarrel, losing control, feeling irritated and harassed); external and internal controlling life (life in one´s own hands, success through effort, life is determined by others, plans thwarted by fate). 12. Well-being and life satisfaction: frequency of selected feelings in the last year (PANAS: enthusiastic, attentive, joyfully excited/expectant, stimulated, determined); depressiveness during the last 14 days (depressed, difficult to pick up, enjoy life, even if some things are difficult, brooding a lot); Valuation of Life-Scale (and a. optimistic, consider current life as useful, life determined by religious or moral principles, etc.); Meaning in Life-Scale (satisfaction with what has been achieved in the past, with the past at peace); general life satisfaction. 13. Critical life events: perceived burden of life events in general; generally most stressful event; current burden of events related to World War II; most stressful event related to World War II; current burden of events outside World War II; most stressful event outside World War II; most stressful event outside World War II; most stressful event outside World War II Interpersonal conflicts and emotional consequences (INDICATE): Frequency of conflicts with known persons (other person has become louder/ abusive towards the respondent (intimidation), has spoken about weaknesses or impairments of the respondent (shame), blamed for an event, paternalism: Ignoring the respondent´s opinion, has caused the respondent to renounce his or her wish or right, neglect: no support given, no time given, financial exploitation: property or possessions of the respondent used for own purposes, has been kept by the respondent, physical violence: firm or rough handling, physically rough or inconsiderate handling, custodial measures restriction of freedom of movement, medication given without consent, sexualised violence: offensive behaviour, sexual harassment). 14. Biography: caregiver in childhood up to the age of 16; social status of parents: employment and occupational status of father and mother when the interviewee was 15 years old; number of siblings; occupational biography of the interviewee: end of full-time employment; occupational status; special designation of occupational status; occupational biography of spouse: end of full-time employment; occupational status; special designation of occupational status; request to politicians to improve one´s own quality of life (open). Demography: sex; age; origin: country, place of residence 1949-1990; education: country of last school attendance; highest

Daily situation for Tennessee counties as reported by the Tennessee Department of Health. The data are posted on the department's coronavirus disease web page: https://www.tn.gov/health/cedep/ncov.html. Date on testing results and deaths was posted beginning March 31, 2020.CountyNS (County GNIS code)NAMELSAD (Legal/statistical area) -County of residence of COVID-19 casesCounty identifier (GEOID) - County FIPS codeCombined statistical area code (CBSAFP) - Metropolitan/Micropolitan Area codeCore-based area name (CBSA_TITLE) - Metropolitan/Micropolitan Area nameCore-based statistical area type (MSA_TYPE) - Core-based statistical area typeCore-based area county type (MSA_COUNTY_TYPE) - Type of county in core-based statistical areasHealth Department Region (HEALTH_DEPT_REG)Health Department Type (HEALTH_DEPT_TYPE)TN ECD Urban Rural Classification (ECD_URBAN_RURAL_CLASS)Positive Tests (TEST_POS) - Total number of people ever to test positive for COVID-19Negative Tests (TEST_NEG) - Total number of people with a negative COVID-19 test resultTotal Tests (TEST_TOT) - Total number of COVID-19 tests with reported resultNew Tests (TEST_NEW) - Number of new tests results posted since the previous dayTotal Cases (CASES_TOT) - Total number of people ever to have a confirmed or probably case of COVID-19 by countyNew Cases (CASES_NEW) - The number of new cases reported to have a confirmed case of COVID-19 since the report on the previous dayTotal Hospitalizations (HOSPITALIZED_TOT) - Number of patients that were ever hospitalized during their illness, it does not indicate the number of patients currently hospitalizeNew Hospitalizations (HOSPITALIZED_NEW) - Number of patients that were ever hospitalized in the previous 24-hour period. Does not indicate the number of patients currently hospitalizedTotal Recovered (RECOV_TOT) - Total Number of inactive/recovered COVID cases. Includes people 14 days beyond illness onset date, specimen collection date, investigation report date, or investigation start date.New Recovered (RECOV_NEW) - Change in the number of new inactive/recovered cases since the previous day.Total Deaths (DEATHS_TOT) - Number of COVID-19 related deaths that were ever reported by countyNew Deaths (DEATHS_NEW) - Number of COVID-19 related deaths that were reported since the previous dayActive Cases (ACTIVE_TOT) - Calculated as the total number of confirmed COVID-19 cases, less the number of recovered and deaths reportedNew Active Cases (ACTIVE_NEW) - Change in the number of active COVID-19 cases since the previous dayPopulation Estimate 2019 (POPESTIMATE2019) - 2019 vintage estimated population for counties by the U.S. Census BureauNOWcast Current (NOWCast_CURRENT) - UTK COVID-19 NOWCast estimate of the number of new daily casesEffective Rate Transmission (EffectiveR) - Effective reproduction or R is an estimate of the average number of new infections caused by a single infected individualEffect Rate Transmission Label (EffectiveR_LABEL)