As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

With the third-highest number of confirmed COVID-19 cases worldwide, Brazil was the country that required the largest volume of oxygen in Latin America. As of ***************, the Portuguese-speaking nation needed nearly *** million cubic meters of oxygen per day to treat its patients. Meanwhile, Mexico needed close to *** thousand cubic meters of oxygen per day. Most of the countries in the region required less than *** thousand cubic meters of oxygen per day. A critical situation Medical oxygen is pivotal for treating patients affected by the COVID-19 disease. The virus can cause pneumonia, which can lead to acute respiratory distress syndrome (lung failure) and eventually death. Medical oxygen enables patients to receive the oxygen required for normal bodily function. With more than *** million cases worldwide, oxygen demand is at an all-time high. As of ***********, India required the most oxygen at more than * million cylinders per day. It is not just oxygen The shortfall in the amount of medical oxygen in Brazil is coupled with a general lack of resources. In 2019, the South American country had only **** intensive care unit (ICU) beds per 100,000 population. In addition, Brazil registered just over ** ventilators per 100,000 inhabitants that same year. Unfortunately, as one of the most affected countries worldwide, this is not enough to meet the soaring demand.

This graph represents the distribution of the dwellings where French people live the lockdown of March 17 due to coronavirus (COVID-19) in March 2020, by surface area in square meters. At that time 34 percent of respondents were confined in dwellings with a surface area varying between 80 and 109 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: facts and figures about COVID-19 coronavirus.

Number of social distancing violations regressed on linear time, quadratic time, and periodicity.

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for article titled
"COVID-19 mortality: positive correlation with cloudiness, sunlight and no correlation with latitude in Europe"

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

Preprint corresponding to this dataset: https://doi.org/10.1101/2021.01.27.21250658

===========
Dataset file:
1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

Dataset graphical preview:
1.0.0.INFOGRAFIC_CloudFraction_vs_COVID-19_mortality_Europe_March-August_2020.png

DATASET fields:
"Country" :
Country name; 37 European countries included.

"Date":
Date stamp at the collection time.
Data collection was performed in the last day of every month.
Date format: YYYY-MM-DD

"Month_Key" :
Date stamp at the collection time, formatted for easier monthly time series analysis.
Date format: YYYY-MM

"Month_Fct2020"
Date stamp at the collection time,formatted for easier graphing, as a string with names of the months
(in English).

"Deaths_per_1Mpop" :
Monthly mortality from COVID-19 raported in the country,
reported as number of COVID-19 deaths per 1 million population of the country,
in that particular month / country.
NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" :
Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" :
Insolation average (solar irradiance at ground level),
in that particular month / country.
It is expressed in Watt / square meter of the ground surface.
Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory,
CERES / FLASHFlux team, 2020,
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M

"Cloud_Fraction" :
Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover),
as decimal fraction of the sky obscured by clouds,
in that particular month / country.
Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory,
MODIS Atmosphere Science Team, 2020,
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR

"CENTR_latitude" and
"CENTR_longitude" :
Latitude and Longitude of the country centroid, for each country.
Data derived from Google LLC, "Dataset publishing language: country centroids",
https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously);
it is redundantly included for easier monthly sectional analysis of the data.

===========
Versioning: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

MAJOR: changes yearly; 1 = 2020
MINOR: changes if new monthly data is added in that particular year.
PATCH: Changes only if errors or minor edits were performed.

DOI for this version: 10.5281/zenodo.4266758

Dataset file source for this version (internal analysis source file):
db_covid_all-ANALYSIS.2020-09-22_r10.csv

This graph illustrates the average surface area of the dwellings in which French people live during the containment of March 17 due to the coronavirus (COVID-19) in March 2020, by region and in square meters. At that time in the region of Bourgogne-Franche-Comté, French people were confined in dwellings with an average surface area of 108 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: Facts and figures about COVID-19 coronavirus

Number of social distancing violations regressed on the number of people on the street and each of the other variables.

In 2020, 30 percent of patients in oncology centers in Poland during the coronavirus epidemic (COVID-19) claimed that the number of patients in the hospital caused a crowd that made it impossible to maintain a distance of 2 meters.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Database with annotated practical examples: Structured examples of 1.5m interventions and examples of interventions for resilience values ​​(worldwide). Analyzed for their operating principles, design patterns and strong concepts.

Since the beginning of the pandemic, the transmission modes of SARS-CoV-2—particularly the role of aerosol transmission—has been much debated. Accumulating evidence suggests that SARS-CoV-2 can be transmitted by aerosols, and not only via larger respiratory droplets. In this study, we quantified SARS-CoV-2 in air surrounding 14 test subjects in a controlled setting. All subjects had SARS-CoV-2 infection confirmed by a recent positive PCR test and had mild symptoms when included in the study. RT-PCR and cell culture analyses were performed on air samples collected at distances of one, two, and four meters from test subjects. Oronasopharyngeal samples were taken from consenting test subjects and analyzed by RT-PCR. Additionally, total aerosol particles were quantified during air sampling trials. Air viral concentrations at one-meter distance were significantly correlated with both viral loads in the upper airways, mild coughing, and fever. One sample collected at four-meter distance was RT-PCR positive. No samples were successfully cultured. The results reported here have potential application for SARS-CoV-2 detection and monitoring schemes, and for increasing our understanding of SARS-CoV-2 transmission dynamics.

The following report outlines the workflow used to optimize your Find Hot Spots result:Initial Data Assessment.There were 2933 valid input features.There were 3108 valid input aggregation areas.There were 3108 valid input aggregation areas.There were 66 outlier locations; these will not be used to compute the optimal fixed distance band.Incident AggregationAnalysis was based on the number of points in each polygon cell.Analysis was performed on all aggregation areas.The aggregation process resulted in 3108 weighted areas.Incident Count Properties:Min0.0000Max0.0015Mean0.0001Std. Dev.0.0001Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 150682.0000 Meters.Hot Spot AnalysisThere are 865 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high incident counts cluster.Blue output features represent cold spots where low incident counts cluster.

Because of the outbreak of the coronavirus (COVID-19), many countries around the world were put into lockdown. In February, average levels of PM2.5 pollution in the Chinese city of Wuhan were 35.1 micrograms per cubic meter. This was a reduction of approximately 44 percent when compared to the same period in 2019.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

Introducing national lockdowns is an effective strategy to contain the Covid-19 pandemic. In Austria, the first Covid-19-related lockdown was introduced on 15 March 2020 with most restrictions being lifted one month later. Seven months later, in November 2020, the second hard lockdown was implemented. The presented dataset contains data from the two waves of an online survey which aimed at comparing the perceptions and experiences of the general population related to the first two Covid-19 lockdowns in Austria. The first wave of data collection was conducted between 27 May and 16 June 2020, with all questions referring to the one-month lockdown period in Austria between 15 March and 15 April 2020. The second wave of data collection was conducted between 2 December and 9 December 2020 with questions referring to the second national lockdown in Austria between 17 November and 6 December 2020. In total 560 respondents were included in the first wave of the survey. Of these 560 participants, 228 provided their e-mail addresses and agreed to be contacted in the future. From the 228 persons who were re-contacted during the second wave of the survey, 141 responded among which 134 provided valid answers and were included in the dataset. Download and use of the data is conditional upon citation of the documents in any resulting work/publication as follows: Simon, J, Łaszewska, A, Helter, T (2021) Perceptions of Covid-19 lockdowns and related public health measures in Austria: Dataset, Version 10-03-2021, Department of Health Economics, Center for Public Health, Medical University of Vienna, Vienna. doi: 10.5281/zenodo.4598821 and Simon, J., Helter, T.M., White, R.G. et al. Impacts of the Covid-19 lockdown and relevant vulnerabilities on capability well-being, mental health and social support: an Austrian survey study. BMC Public Health 21, 314 (2021). https://doi.org/10.1186/s12889-021-10351-5 License: Creative Commons Attribution-NonCommercial 4.0 International Variables included in the dataset: 1. Demographic characteristics 2. Covid-19-related questions - Tested positive for Covid-19 or experienced Covid-19 symptoms - Indirect Covid-19 experience defined as having a friend and/or family member infected or knowing someone who died of Covid-19 - Quarantine or self-isolation in the past months - Concern about infection with SARS-CoV-2 - Concern about family member infected with SARS-CoV-2 3. Lockdown-related questions - Personal experiences of the Covid-19 lockdowns: threat to livelihood/income, more difficult than usual for to focus on work or normal, daily activities, being less busy than usual, feeling more isolated than usual, the lockdown restrictions are necessary to limit spread of the virus, understanding better what is really important in life, greater sense of appreciation for the healthcare workers, communicating with relatives more often, feeling that people have become more friendly towards other people, feeling more connected to the members of the local community - Perceptions of the necessity of public health measures during the first lockdown: commuting to and from work only when absolutely necessary, walks only with people living in the same household, closure of all non-essential business premises, only necessary purchases, no physical contact with family members outside the same household, mouth and nose protection in public spaces - Perceptions of the necessity of public health measures during the second lockdown: restrictions on leaving private living space, school closing and distance learning, closure of all non-essential shops and businesses, mouth and nose protection in public spaces, ban on events or restrictions in the event area, distance of one meter in public space for people from different households, physical contact only with closest relatives or individual caregivers, switch to homeoffice wherever possible, visits in nursing homes and hospitals once a week, commuting to and from work only when absolutely necessary - Complying with the public health measures during the first lockdown: walks only with people from the same household, only necessary purchases e.g. groceries, medication, no physical contact with family members outside the same household, mouth and nose protection in public spaces - Complying with the public health measures during the second lockdown: restrictions on leaving private living space, mouth and nose protection in public spaces, distance of one meter in public spaces for people from different households, physical contact only with closest relatives or individual caregivers, switch to homeoffice wherever possible - Impact of the lockdowns on different life domains: marriage, parenting, friendships, work, education, leisure activities, spirituality, community life, physical self-care {"references": ["Simon, J., Helter, T.M., White, R.G. et al. Impacts of the Covid-19 lockdown and relevant vulnerabilities on capability well-being, mental health and social suppo...

The COVID-19 pandemic lockdown worldwide provided a unique research opportunity for ecologists to investigate the human-wildlife relationship under abrupt changes in human mobility, also known as Anthropause. Here we chose 15 common non-migratory bird species with different levels of synanthrope and we aimed to compare how human mobility changes could influence the occupancy of fully synanthropic species such as House Sparrow (Passer domesticus) versus casual to tangential synanthropic species such as White-breasted Nuthatch (Sitta carolinensis). We extracted data from the eBird citizen science project during three study periods in the spring and summer of 2020 when human mobility changed unevenly across different counties in North Carolina. We used the COVID-19 Community Mobility reports from Google to examine how community mobility changes towards workplaces, an indicator of overall human movements at the county level, could influence bird occupancy., The data source we used for bird data was eBird, a global citizen science project run by the Cornell Lab of Ornithology. We used the COVID-19 Community Mobility Reports by Google to represent the pause of human activities at the county level in North Carolina. These data are publicly available and were last updated on 10/15/2022. We used forest land cover data from NC One Map that has a high resolution (1-meter pixel) raster data from 2016 imagery to represent canopy cover at each eBird checklist location. We also used the raster data of the 2019 National Land Cover Database to represent the degree of development/impervious surface at each eBird checklist location. All three measurements were used for the highest resolution that was available to use. We downloaded the eBird Basic Dataset (EBD) that contains the 15 study species from February to June 2020. We also downloaded the sampling event data that contains the checklist efforts information. First, we used the R package Auk (versio..., , # Processed data for the analysis of human mobility changes on bird occupancy in NC

https://doi.org/10.5061/dryad.gb5mkkwxr

There are 3 types of data here including Google Community Mobility data, and processed data (data after extracting spatial covariates and merging with all covariates for the Occupancy Modeling as well as extracted predicted occupancy data that we used to create figures).

Description of the data and file structure

Google Community Mobility data: This is the dataset downloaded from https://www.google.com/covid19/mobility/ that measures the mobility changes throughout the world during the COVID-19 lockdown. Please visit the above website for more information about the data. Please see the "Anthropause_AMCR_02112024" R file (uploaded to Zenodo) for details on how we processed the raw data.

| Dataset name | Dataset description ...

General characteristics of 14 patients with COVID-19 confirmed.

In April 2020, the Sakha (Yakutiya) Republic recorded the most significant price drop in real estate prices in Russia with a roughly five percent price fall per square meter. In the Moscow and Leningrad Regions, the price of residential properties dropped by 3.2 and 3 percentage points per square meter over the given period, respectively.

As of March 2020, ** percent of companies in the Dutch hospitality sector predicted that they did not have sufficient financial resources to survive the coronavirus epidemic for more than two to three months. Of this group, more than half indicated that they could not even survive for *** month without (governmental) financial support. A mere *** percent of businesses expected that they would be able to endure the epidemic for six months to a year. Government regulations as response to the coronavirus diminished the occupancy rate in the hospitality sector in March 2020.

COVID-19 and its impact on businesses

The COVID-19 outbreak in 2020 did not only affect the health of the Dutch population, but also that of its businesses. The hospitality sector was among those hit the hardest by the coronavirus epidemic in 2020. As of March 2020, it was estimated that food services in the Netherlands could face revenue losses of *** million euros per month. The deterioration of small and large businesses prompted the government to provide financial aid worth tens of billions of euros. Nonetheless, the epidemic caused the bankruptcy of many stores, restaurants, cafes and businesses in the (travel) servicing industry.

Government regulation and changing consumer behavior

Two underlying factors contributed to the decline of the hospitality sector: government regulations and a rising level of concern about the virus. Firstly, the government of the Netherlands forced the closure of many non-essential business, including those in the hospitality sector. In addition, new social distancing regulation such as the *** meter-rule made it near-impossible for many businesses to remain operational. Secondly, potential customers stayed away from inner cities and shopping centers due to a fear of infection. As the lion’s share of hospitality businesses is located in inner cities and near shopping areas, most businesses were affected.