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.

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.

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 linear time, quadratic time, and periodicity.

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

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.

Frequency of the statement related with knowledge level on COVID-19 (KLC-19).

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus.

Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and become seriously ill or die at any age.

The best way to prevent and slow down transmission is to be well informed about the disease and how the virus spreads. Protect yourself and others from infection by staying at least 1 metre apart from others, wearing a properly fitted mask, and washing your hands or using an alcohol-based rub frequently. Get vaccinated when it’s your turn and follow local guidance.

The virus can spread from an infected person’s mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe. These particles range from larger respiratory droplets to smaller aerosols. It is important to practice respiratory etiquette, for example by coughing into a flexed elbow, and to stay home and self-isolate until you recover if you feel unwell.

Where are cases still high?

Daily global cases fell after a spike in the spring but are now rising again, with the emergence of the BA.4 and BA.5 subvariants of the Omicron variant.

Studies suggest that Omicron - which quickly became dominant in numerous countries - is milder than the Delta variant, but far more contagious. The subvariants are even more contagious.

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.

Hospitality operators around the world have increased their focus on health and hygiene as a result of the coronavirus (COVID-19) pandemic. As of June 2020, a global survey was conducted to determine the share of hospitality operators who spaced their tables and chairs in dining venues at least *** meters apart and frequently disinfected their public areas. The vast majority of respondents, ** percent, reported having done so, while only ***** percent of respondents reported having done otherwise.

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 R...

Effect of change in FIES score from wave 1 to wave 2 on PSS, KLC-19 and ALC-19 scores.

This dataset was obtained by recording pedestrian crowds in diverse settings in a
passive and privacy-respective way and manually tracking pedestrians to
extract their trajectories, head orientations, and information about their group
membership.

These field data were collected in Lyon, France, between July 2020 and January 2021
at diverse times during the Covid-19. Details about the settings can be found in the reference below, as well as on the webpage https://seppieton.herokuapp.com/scenarios/

FORMAT
The data are saved as text files, with each file corresponding to a different recording
place. The first line of the file is the header and explains the structure of each line:

1) ped_no: the pedestrian label (an integer)
2) group_no: the label of the group to which the pedestrian was associated, by visual
identification of group members
3) time: time point (in seconds)
4) x: first coordinate (in metres) of the position of the centre of the pedestrian's head at this time
5) y: second coordinate (in metres) of the position of the centre of the pedestrian's head at this time
6) theta: angular orientation (in radian) of the pedestrian's head, i.e., the orientation
of the line joining the centre of the head to the mouth

The data are free to re-use, but please cite the reference below (or the associated
published version) if you make use of them for academic or business purposes.

REFERENCE

Garcia, W., Mendez, S. Fray, B., & Nicolas, A. Model-based assessment of the risks of viral
transmission in non-confined crowds, Safety Science 144 (2021), 105453, https://www.sciencedirect.com/science/article/pii/S0925753521002964. Also on arXiv:2012.08957 and
https://hal.archives-ouvertes.fr/hal-03060324/

Frequency of the statement related with attitude level on COVID-19 (ALC-19).

Do your part. Wear a non-medical mask or face covering to protect others when you can't maintain a 2 metre distance.

The data underpins a study which aimed to investigate the impact of remoteness on the travel time and population catchment for all COVID-19 point-of-care-test sites within Victoria during Stage 4 restrictions during July 2020.

There are two files 'mesh_block_summary' and 'testing_site_summary'.

In relation to 'mesh_block_summary', please consider the points below. - The data provides the average travel time (in minutes) and distance (in metres) to the closest point-of-care-test site for each mesh block. MB_CODE16: Mesh block identifier Duration: Distance in metres Distance: Travel time in minutes MB_Category_Name_2016: Mesh block category Dwelling: Number of dwellings Person: Number of people

In relation to 'testing_site_summary', please consider the points below. - The data provides the average travel time (in minutes) and distance (in metres) for mesh blocks which were closest (based on travel time) to each test site. Site_Name: Name of point-of-care-test site Facility: Type of site Website: Site website COVID_Lat: Latitude coordinate COVID_Long: Longitude coordinate Dwelling: Number of dwellings within mesh blocks which were closest (based on travel time) to each test site. Population: Number of people within mesh blocks which were closest (based on travel time) to each test site. Mean_distance: Average distance (in metres) for closest mesh blocks Mean_duration: Average travel time (in minutes) for closest mesh blocks N_mesh_blocks: Number of mesh blocks which are closest Mean_catchment_IRSD: Mean 'Index of Relative Socioeconomic Disadvantage' for closest mesh blocks

The methodology to derive the data above has been detailed within the reference below: Lakhani A, Wollersheim D. COVID-19 test sites in Victoria approaching Stage 4 restrictions: evaluating the relationship between remoteness, travel time and population serviced. Aust N Z J Public Health. 2021 Dec;45(6):628-636. doi: 10.1111/1753-6405.13154. Epub 2021 Oct 28. PMID: 34709703; PMCID: PMC8652517.

Analysis of Spanish Apartment Pricing and Size Post-COVID-19

Investigating the Impact of the Pandemic

By [source]

About this dataset

This dataset provides an in-depth insight into Spanish apartment prices, locations and sizes, offering a comprehensive view of the effects of the Covid-19 crisis in this market. By exploring the data you can gain valuable knowledge on how different variables such as number of rooms, bathrooms, square meters and photos influence pricing, as well as key details such as description and whether or not they are recommended by reviews. Furthermore, by comparing average prices per square meter regionally between different areas you can get a better understanding of individual apartment value changes over time. Whether you are looking for your dream home or simply seeking to understand current trends within this sector this dataset is here to provide all the information necessary for both people either starting or already familiar with this industry

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How to use the dataset

This dataset includes a comprehensive collection of Spanish apartments that are currently up for sale. It provides valuable insight into the effects of the Covid-19 pandemic on pricing and size. With this guide, you can take advantage of all the data to explore how different factors like housing surface area, number of rooms and bathrooms, location, number of photos associated with an apartment, type and recommendations affect price.

• First off, you should start by taking a look at summary column which summarizes in one or two lines what each apartment is about. You can quickly search some patterns which could give important information about the market current situation during COVID-19 crisis.

• Explore more in depth each individual apartment by looking at its description section for example if it refers to particular services available like swimming pool or gymnasiums . Consequently those extra features usually bumps up the prices higher since buyers are keen to have such luxury items included in their purchase even if it’s not so affordable sometimes..

• Start studying locationwise since it might gives hint as to what kind preof city we have eirther active market in terms equity investment , home stay rental business activities that suggest opportunities for considerable return on investment (ROI). Even further detailed analysis such as comparing net change over time energy efficient ratings electrical or fuel efficiency , transport facilities , educational level may be conducted when choosing between several apartments located close one another ..

• Consider multiple column ranging from price value provided (price/m2 )to size sqm surface area measure and count number of rooms & bathrooms . Doing so will help allot better understanding whether purchasing an unit is worth expenditure once overall costs per advantages estimated –as previously acknowledged apps features could increase prices significantly- don’t forget security aspect major item critical home choice making process affording protection against Intruders ..

• An interesting but tricky part is Num Photos how many were included –possibly indicates quality build high end projects appreciate additional gallery mentioning quite informative panorama around property itself - while recomendation customarily assumes certain guarantees warranties unique promise provided providing aside prospective buyer safety issues impose trustworthiness matters shared among other future residents …

• Finally type & region column should be taken into account reason enough different categories identifies houses versus flats diversely built outside suburban villas contained inside specially designed mansion areas built upon special requests .. Therefore usage those two complementary field help finding right desired environment accompaniments beach lounge bar attract nature lovers adjacent mountainside

Research Ideas

• Creating an interactive mapping tool that showcases the average prices per square meter of different cities or regions in Spain, enabling potential buyers to identify the most affordable areas for their desired budget and size.
• Developing a comparison algorithm that recommends the best options available depending on various criteria such as cost, rooms/bathrooms, recommended status, etc., helping users make informed decisions when browsing for apartments online.
• Constructing a model that predicts sale prices based on existing data trends and analyses of photos and recommendations associated wit...

The total take-up of office space in the Netherlands increased in 2021, after falling below *********** square meters for the first time since 2012. While take-up picked up in 2021, at about *** million square meters, it was still below the 2019 value. As the economy has begun to recover from the impact of the coronavirus (COVID-19) pandemic, more companies have shown their readiness to pay the right price for an office building.

Introduction: Social or physical distancing has been an effective measure for reducing the spread of COVID-19 infections. Investigating the determinants of adherence to social distancing can inform public health strategies to improve the behaviour. However, there is a lack of data in various populations. This study investigates the degree to which South Africans complied with social distancing during the country's COVID-19 lockdown and identifies the determinants associated with being in close contact with large numbers of people.Materials and Methods: Data was collected from a South African national online survey on a data free platform, supplemented with telephone interviews. The survey was conducted from 8 to 29 April 2020. The primary outcome was the number of people that participants came into close contact with (within a 2-metre distance) the last time they were outside their home during the COVID-19 lockdown. Multivariate multinomial regression investigated the socio-demographic, psychosocial and household environmental determinants associated with being in contact with 1–10, 11–50 and more than 50 people.Results: Of the 17,563 adult participants, 20.3% reported having not left home, 50.6% were in close physical distance with 1–10 people, 21.1% with 11–50 people, and 8.0% with >50 people. Larger household size and incorrect knowledge about the importance of social distancing were associated with being in contact with >50 people. Male gender, younger age and being in the White and Coloured population groups were significantly associated with being in contact with 1–10 people but not with larger numbers of people. Employment, at least secondary school education, lack of self-efficacy in being able to protect oneself from infection, and moderate or high risk perception of becoming infected, were all associated with increased odds of close contact with 1–10, 11–50, and >50 people relative to remaining at home.Conclusion: The findings identify subgroups of individuals that are less likely to comply with social distancing regulations. Public health communication, interventions and policy can be tailored to address these determinants of social distancing.

Do your part. Wear a non-medical mask or face covering to protect others when you can't maintain a 2 metre distance.