The flu is estimated to cause 400,000 respiratory deaths each year on average across the world. These deaths come from pneumonia and other respiratory symptoms caused by the flu. People also die from other complications of the flu – such as a stroke or heart attack – but global estimates have not been made of their death toll. The Spanish flu caused the largest influenza pandemic in history. Yet, data on the flu is limited. With better testing, countries could improve their response to flu epidemics. It could help to rapidly identify new strains, detect epidemics early, and design better-matched vaccines to target flu strains circulating in the population.

this data set contains the vaccine coverage around the world from 2018 to 2022.

Deaths counts for influenza, pneumonia, and COVID-19 reported to NCHS by week ending date, by state and HHS region, and age group.

this graph was created in OurDataWorld, R , Loocker and Tableau

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fcae3bb501c71af31a491739671842d0d%2Fgraph1.png?generation=1712001396965624&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fb9bb57abd368d522f4f70edd77e44cd5%2Fgraph2.png?generation=1712001404173500&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Ffcc4718729d85efd4fb21eb4cdfb1ee3%2Fgraph3.jpg?generation=1712001411161330&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F3a1dd72755f5473cf5f9c0758edc1dd3%2Fgraph4.png?generation=1712001416526151&alt=media" alt="">

Introduction: Seasonal influenza, often perceived as a common illness, carries a significant global burden, claiming hundreds of thousands of lives annually. Despite advancements in healthcare and vaccination efforts, the flu remains a formidable threat, particularly affecting vulnerable populations such as infants and the elderly. This article delves into the intricacies of influenza-related mortality, examining regional disparities, contributing factors, and the implications for public health.

The Global Landscape of Influenza Mortality: Data from the Global Pandemic Mortality Project II sheds light on the magnitude of influenza-related deaths, drawing from surveillance metrics spanning from 2002 to 2011. These estimates, while informative, underscore the challenge of accurately gauging mortality rates, especially in low-income countries where testing and mortality records may be lacking.

Respiratory Symptoms and Beyond: The conventional understanding of influenza-related fatalities primarily revolves around respiratory complications. Pneumonia and other respiratory ailments serve as prominent causes of death, contributing to the staggering toll of 400,000 lives claimed annually. However, it is imperative to acknowledge that the impact of influenza extends beyond respiratory symptoms. Complications such as strokes and heart attacks, though not explicitly captured in mortality estimates, further amplify the disease's lethality, warranting comprehensive preventive measures.

Vulnerability Across Age Groups: Influenza's lethality is not uniform across age demographics. Infants and the elderly emerge as the most susceptible cohorts, bearing the brunt of severe complications and mortality. Among individuals aged over 65, the mortality rate stands at approximately 31 per 100,000 in Europe alone, reflecting the disproportionate impact on older populations. The interplay of age-related factors, including weakened immune responses and underlying health conditions, exacerbates the severity of influenza outcomes among these groups.

Regional Disparities and Determinants: A notable aspect of influenza mortality lies in its disparate distribution across regions. While Europe and North America exhibit relatively lower death rates, countries in South America, Africa, and South Asia grapple with higher mortality burdens. This regional divide underscores the complex interplay of socio-economic factors, healthcare accessibility, and vaccination coverage. Poverty, inadequate healthcare infrastructure, and suboptimal vaccination rates converge to heighten vulnerability to influenza-related complications, amplifying mortality rates in resource-constrained settings.

Implications for Public Health: The revelation of significant regional differentials in influenza mortality necessitates a tailored approach to public health interventions. Strengthening healthcare systems, particularly in low-income regions, is paramount to bolstering surveillance, enhancing diagnostic capabilities, and facilitating timely interventions. Furthermore, targeted vaccination campaigns, coupled with education initiatives, hold promise in mitigating influenza's toll, especially among vulnerable populations. Addressing socio-economic disparities and bolstering healthcare resilience emerge as pivotal strategies in fortifying global defenses against seasonal influenza.

Conclusion: Seasonal influenza, often underestimated in its impact, exacts a substantial toll on global health each year. The multifaceted nature of influenza-related mortality underscores the need for a nuanced understanding and comprehensive mitigation strategies. By addressing regional disparities, prioritizing vulnerable populations, and fortifying healthcare systems, the global community can strive towards mitigating the burden of seasonal influenza, safeguarding lives, and fostering resilient health systems for generations to come.

Provisional counts of the number of death occurrences in England and Wales due to coronavirus (COVID-19) and influenza and pneumonia, by age, sex and place of death.

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

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.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Annual Season Influenza Vaccinations - This indicator shows the percentage of adults who are vaccinated annually against seasonal influenza. For many people, the seasonal flu is a mild illness, but for some it can lead to pneumonia, hospitalization, or death. Vaccination of persons in high-risk populations is especially important to reduce their risk of severe illness or death. Link to Data Details

Seasonal influenza epidemics have a substantial public health and economic burden in the United States (US). On average, over 200,000 people are hospitalized and an estimated 23,000 people die from respiratory and circulatory complications associated with seasonal influenza virus infections each year. Annual direct medical costs and indirect productivity costs across the US have been found to average respectively at $10.4 billion and $16.3 billion. The objective of this study was to estimate the economic impact of severe influenza-induced illness on the US Veterans Affairs population. The five-year study period included 2010 through 2014. Influenza-attributed outcomes were estimated with a statistical regression model using observed emergency department (ED) visits, hospitalizations, and deaths from the Veterans Health Administration of the Department of Veterans Affairs (VA) electronic medical records and respiratory viral surveillance data from the Centers for Disease Control and Prevention (CDC). Data from VA’s Managerial Cost Accounting system were used to estimate the costs of the emergency department and hospital visits. Data from the Bureau of Labor Statistics were used to estimate the costs of lost productivity; data on age at death, life expectancy and economic valuations for a statistical life year were used to estimate the costs of a premature death. An estimated 10,674 (95% CI 8,661–12,687) VA ED visits, 2,538 (95% CI 2,112–2,964) VA hospitalizations, 5,522 (95% CI 4,834–6,210) all-cause deaths, and 3,793 (95% CI 3,375–4,211) underlying respiratory or circulatory deaths (inside and outside VA) among adult Veterans were attributable to influenza each year from 2010 through 2014. The annual value of lost productivity amounted to $27 (95% CI $24–31) million and the annual costs for ED visits were $6.2 (95% CI $5.1–7.4) million. Ninety-six percent of VA hospitalizations resulted in either death or a discharge to home, with annual costs totaling $36 (95% CI $30–43) million. The remaining 4% of hospitalizations were followed by extended care at rehabilitation and skilled nursing facilities with annual costs totaling $5.5 (95% CI $4.4–6.8) million. The annual monetary value of quality-adjusted life years (QALYs) lost amounted to $1.1 (95% CI $1.0–1.2) billion. In total, the estimated annual economic burden was $1.2 (95% CI $1.0–1.3) billion, indicating the substantial burden of seasonal influenza epidemics on the US Veterans Affairs population. Premature death was found to be the largest driver of these costs, followed by hospitalization.

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.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

Context

The 2009 swine flu pandemic was an influenza pandemic that lasted for about 19 months, from January 2009 to August 2010, and the second of two pandemics involving H1N1 influenza virus.

Content

1. data.csv - contains day by day country wise no. of cases & deaths from 4th April to 6th July 2009

Note

• Although the pandemic went on for more than 2 years the data is only from 24th April 2009 to 6th July 2009.
• Because the countries were no longer required to test and report individual cases from 6th July 2009.
• So that day by day data from 6th July 2009 is not available.

Acknowledgements / Data Source

https://www.who.int/csr/disease/swineflu/updates/en/

Collection methodology

https://github.com/imdevskp/h1n1_data_webscrapping

Cover Photo

Photo from CDC Blog https://blogs.cdc.gov/publichealthmatters/2019/04/h1n1/

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• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
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• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov. Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

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.

Abstract Introduction the first influenza pandemic of the 21st century occurred in 2009, caused by the new subtype of influenza virus, influenza A (H1N1) pdm09. Objective to analyze the factors associated with death due to Severe Acute Respiratory Infection (SARI) caused by influenza A (H1N1) pdm09 in residents of the city of Rio de Janeiro. Method Analysis of secondary data, including 1,191 confirmed cases of influenza A (H1N1) pdm09 with clinical criteria for SARI, residents of the city of Rio de Janeiro, in 2009. Descriptive statistical analysis and logistic regression for the study of factors associated with death. Results 60.3% occurred in women, with 185 pregnant women; 48.1% in children under 20 years old; 35.7% had comorbidities; 91.4% were hospitalized, and 7.4% died. There was a higher chance of death associated with low educational level, presence of comorbidity, radiological pattern of interstitial infiltrate, consolidation or mixed; laboratory confirmation; vaccination status ignored. Conclusion individuals with low educational level who had at least one comorbidity and had pulmonary involvement with a radiological pattern with interstitial infiltrate, consolidation or mixed had a higher chance of dying. Knowledge of this profile allows for more efficient planning of health care.

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.

Influenza virus is the cause of the death of millions of people with about 3–4 pandemics every hundred years in history. It also turns into a seasonal disease, bringing about approximately 5–15% of the population to be infected and 290,000–650,000 people to die every year. These numbers reveal that it is necessary to be on the alert to work towards influenza in order to protect public health. There are FDA-approved antiviral drugs such as oseltamivir and zanamivir recommended by the World Center for Disease Prevention. However, after the recent outbreaks such as bird flu and swine flu, increasing studies have shown that the flu virus has gained resistance to these drugs. So, there is an urgent need to find new drugs effective against this virus. This study aims to investigate new drug candidates targeting neuraminidase (NA) for the treatment of influenza by using computer aided drug design approaches. They involve virtual scanning, de novo design, rational design, docking, MD, MMGB/PBSA. The investigation includes H1N1, H5N1, H2N2 and H3N2 neuraminidase proteins and their mutant variants possessing resistance to FDA-approved drugs. Virtual screening consists of approximately 30 thousand molecules while de novo and rational designs produced over a hundred molecules. These approaches produced three lead molecules with binding energies for both non-mutant (-34.84, −59.99 and −60.66 kcal/mol) and mutant (-40.40, −58.93, −76.19 kcal/mol) H2N2 NA calculated by MM-PBSA compared with those of oseltamivir −25.64 and −18.40 respectively. The results offer new drug candidates against influenza infection. Communicated by Ramaswamy H. Sarma

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

Context

In the time of epidemics, what is the status of HIV AIDS across the world, where does each country stands, is it getting any better. The data set should be helpful to explore much more about above mentioned factors.

Content

The data set contains data on

1. No. of people living with HIV AIDS
2. No. of deaths due to HIV AIDS
3. No. of cases among adults (19-45)
4. Prevention of mother-to-child transmission estimates
5. ART (Anti Retro-viral Therapy) coverage among people living with HIV estimates
6. ART (Anti Retro-viral Therapy) coverage among children estimates

Acknowledgements / Data Source

• WHO : https://www.who.int/hiv/data/en/
• UNESCO : https://data.unicef.org/resources/dataset/hiv-aids-statistical-tables/

Collection methodology

https://github.com/imdevskp/hiv_aids_who_unesco_data_cleaning

Cover Photo

Photo by Anna Shvets from Pexels https://www.pexels.com/photo/red-ribbon-on-white-surface-3900425/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

Context

• Severe acute respiratory syndrome (SARS) is a viral respiratory disease of zoonotic origin caused by the SARS coronavirus (SARS-CoV).
• Between November 2002 and July 2003, an outbreak of SARS in southern China caused an eventual
• 8,098 cases, resulting in 774 deaths reported in
• 17 countries (9.6% fatality rate), with the majority of cases in mainland China and Hong Kong.
• No cases of SARS have been reported worldwide since 2004.
• In late 2017, Chinese scientists traced the virus through the intermediary of civets to cave-dwelling horseshoe bats in Yunnan province.
• More information https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome

Content

• sars_2003_complete_dataset_clean.csv - The file contains day by day no. from March to July 2003 across the world.
• summary_data_clean.csv - Final no.s from across the world

Acknowledgements / Data Source

https://www.who.int/csr/sars/country/en/

Collection methodology

https://github.com/imdevskp/sars-2003-outbreak-data-webscraping-code

Cover Photo

Photo from CDC website https://www.cdc.gov/dotw/sars/index.html#

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

This dataset is no longer updated, find vaccination data here From 24 March 2022, Public Health Scotland (PHS) began reporting the number of people who have received a fourth dose of Covid-19 vaccination. Vaccine uptake statistics among care home residents and those who are severely immunosuppressed will be reported initially. PHS will include further updates as the Spring/Summer vaccination programme rolls out. In addition, as part of our continuous review of reporting, PHS made some changes to vaccine uptake statistics. From 24 March 2022, the deceased and those who no longer live in Scotland are no longer be included in vaccine uptake statistics. Historic trend data have been updated to take into account this new methodology for all apart from the Daily Trends by JCVI Priority Group table (more details about the data in this table are below). Scotland level data for all vaccinations administered (i.e. including those who have since died or moved from Scotland) are still available in the Daily Trend of All Vaccinations Delivered in Scotland table. Also from 24 March 2022, Dose 3/Booster doses are termed "Dose 3". To allow new data to be fully processed and available at 14:00, the Daily COVID-19 in Scotland and COVID-19 Vaccination in Scotland datasets will be temporarily unavailable from 12:45 to 14:00. During this window, the datasets will not be visible and any queries made to these datasets will return a 404 - Not found error. At all other times the datasets will be available in full as usual. PHS reviewed the JCVI priority group uptake figures from 18 November 2021, specifically how we derive the numerator and the denominator. The rational for the change is to ensure we report on most up to date living population for each group. For this, the list of individuals in each cohort has been refreshed to be more current. We have also removed individuals who have since died to reflect the current living population. From the 24 March 2022 those who are no longer living in Scotland have also been removed from the numerator and denominator for JCVI priority group uptake figures. This means all the JCVI cohorts and populations have changed for both numerator and denominators on these two dates and care should be taken when interpreting trends. On 08 December 2020, a Coronavirus (COVID-19) vaccine developed by Pfizer BioNTech (Comirnaty) was first used in the UK as part of national immunisation programmes. The AstraZeneca (Spikevax) vaccine was also approved for use in the national programme, and rollout of this vaccine began on 04 January 2021. Moderna (Vaxzevria) vaccine was approved for use on 8 January 2021 and rollout of this vaccine began on 07 April 2021. These vaccines have met strict standards of safety, quality and effectiveness set out by the independent Medicines and Healthcare Products Regulatory Agency (MHRA). Those giving the vaccine to others were the first to receive the vaccination. In the first phase of the programme, NHS Scotland followed the independent advice received from the Joint Committee on Vaccination and Immunisation (JCVI) and prioritised delivery of the vaccine to those with the greatest clinical need, in line with the recommended order of prioritisation. For booster vaccinations a similar approach has been adopted. Definitions used in the vaccine uptake by JCVI priority group resource can be found in the JCVI Priority Group Definitions table. Individuals can appear in more than one JCVI priority group. This dataset provides information on daily number of COVID vaccinations in Scotland. Data on the total number of vaccinations in Scotland is presented by day administered and vaccine type, by age group, by sex, by non-age cohorts and by geographies (NHS Board and Local Authority). As the population in the cohorts can change with time, these will be refined when updated data are available. Additional data sources relating to this topic area are provided in the Links section of the Metadata below. Data visualisation and additional notes are available on the Public Health Scotland - Covid 19 Scotland dashboard.