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 ---

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.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Flu vaccine uptake (%) in children aged 2 to 3 years old, who received the flu vaccination between 1st September to the end of February as recorded in the GP record. The February collection has been adopted for our end of season figures from 2017 to 2018. All previous data is the same definitions but until the end of January rather than February to consider data returning from outside the practice and later in practice vaccinations.RationaleInfluenza (also known as Flu) is a highly infectious viral illness spread by droplet infection. The flu vaccination is offered to people who are at greater risk of developing serious complications if they catch the flu. The seasonal influenza programme for England is set out in the Annual Flu Letter. Both the flu letter and the flu plan have the support of the Chief Medical Officer (CMO), Chief Pharmaceutical Officer (CPhO), and Director of Nursing.Vaccination coverage is the best indicator of the level of protection a population will have against vaccine-preventable communicable diseases. Immunisation is one of the most effective healthcare interventions available, and flu vaccines can prevent illness and hospital admissions among these groups of people. Increasing the uptake of the flu vaccine among these high-risk groups should also contribute to easing winter pressure on primary care services and hospital admissions. Coverage is closely related to levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise.The UK Health Security Agency (UKHSA) will continue to provide expert advice and monitoring of public health, including immunisation. NHS England now has responsibility for commissioning the flu programme, and GPs continue to play a key role. NHS England teams will ensure that robust plans are in place locally and that high vaccination uptake levels are reached in the clinical risk groups. For more information, see the Green Book chapter 19 on Influenza.The Annual flu letter sets out the national vaccine uptake ambitions each year. In 2021 to 2022, the national ambition was to achieve at least 70% vaccine uptake in those aged 2 to 3 years old. Prior to this, the national vaccine uptake ambition was 75% in line with WHO targets.Definition of numeratorNumerator is the number of vaccinations administered during the influenza season between 1st September and the end of February.Definition of denominatorDenominator is the GP registered population on the date of extraction including patients who have been offered the vaccine but refused it, as the uptake rate is measured against the overall eligible population. For more detailed information please see the user guide, available to view and download from https://www.gov.uk/government/collections/vaccine-uptake#seasonal-flu-vaccine-uptakeCaveatsThis collection has received approval from the Data Coordination Board (DCB).Data is final and represents a percentage of all GP practices in England responding to the final survey. Where a total for England is quoted (e.g., a sum of the number of patients registered and number vaccinated), this is taken from the GP practice sample and is therefore not an extrapolated figure.For definitions of clinical at-risk groups for those aged 6 months to under 65 years, see the annual flu letter published at Annual Flu Programme.The age under 65 clinical at-risk group data includes pregnant women with other risk factors but excludes otherwise 'healthy' pregnant women and carers.All figures are derived from data as extracted from records on GP systems or as submitted by GP practices, Area Teams, and CCGs.Data source: ImmForm website: registered patient GP practice data, Influenza Immunisation Vaccine Uptake Monitoring Programme, OHID.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Percentage of influenza-vaccinated patients by underlying conditions in laboratory-confirmed influenza cases receiving care at the GTiPUH in Badalona (Spain) during the 2018–2019 influenza epidemic season.

This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Collection of socio-economic and meteorological indicators as well as travel patterns and cases of H1N1 during the swine flu pandemic in Sweden in 2009. Comprise the supplementary information for the paper titled "Socioeconomic and environmental patterns behind H1N1 spreading in Sweden" by András Bóta, Martin Holmberg, Lauren Gardner and Martin Rosvall, Sci Rep 11, 22512 (2021). https://doi.org/10.1038/s41598-021-01857-4 Identifying the critical socio-economic, travel and climate factors related to influenza spreading is critical to the prediction and mitigation of epidemics. In the paper we study the 2009 A(H1N1) outbreak in the municipalities of Sweden, following it for six years between 2009 and 2015. Our goal is to discover the relationship between the above indicators and the timing of the epidemic onset of the disease. We also identify the municipalities playing a key role in the outbreak as well as the most critical travel routes of the country.

Publication available at: https://doi.org/10.1038/s41598-021-01857-4

Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/

Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license

Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code

1. giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09

2. giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row.

Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities).

1. giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities).

2. giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

H9N2 avian influenza viruses are endemic and persistent in China, but those that are prevalent in different provinces are also causes of wide epidemics, related to the spread of wild birds and the cross-regional trade in live poultry. For the past 4 years, beginning in 2018, we have sampled a live-poultry market in Foshan, Guangdong, in this ongoing study. In addition to the prevalence of H9N2 avian influenza viruses in China during this period, we identified isolates from the same market belonging to clade A and clade B, which diverged in 2012–2013, and clade C, which diverged in 2014–2016, respectively. An analysis of population dynamics revealed that, after a critical divergence period from 2014 to 2016, the genetic diversity of H9N2 viruses peaked in 2017. Our spatiotemporal dynamics analysis found that clade A, B, and C, which maintain high rates of evolution, have different prevalence ranges and transmission paths. Clades A and B were mainly prevalent in East China in the early stage, and then spread to Southern China, becoming epidemic with clade C. Strains from different regions converge at the same live-poultry market to communicate, which may be one reasons the H9N2 viruses are difficult to eradicate and increasingly dominant throughout China. Selection pressure and molecular analysis have demonstrated that single amino acid polymorphisms at key receptor binding sites 156, 160, and 190 under positive selection pressure, suggesting that H9N2 viruses are undergoing mutations to adapt to new hosts. Live-poultry markets are important because people who visit them have frequent contact with poultry, H9N2 viruses from different regions converge at these markets and spread through contact between live birds and humans, generating increased risks of human exposure to these viruses and threatening public health safety. Thus, it is important to reducing the cross-regional trade of live poultry and strengthening the monitoring of avian influenza viruses in live-poultry markets to reduce the spread of avian influenza viruses.