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

Context

New York has presented the most cases compared to all states across the U.S..There have also been critiques regarding how much more unnoticed impact the flu has caused. My dataset allows us to compare whether or not this is true according to the most recent data.

Content

This COVID-19 data is from Kaggle whereas the New York influenza data comes from the U.S. government health data website. I merged the two datasets by county and FIPS code and listed the most recent reports of 2020 COVID-19 cases and deaths alongside the 2019 known influenza cases for comparison.

Acknowledgements

I am thankful to Kaggle and the U.S. government for making the data that made this possible openly available.

Inspiration

This data can be extended to answer the common misconceptions of the scale of the COVID-19 and common flu. My inspiration stems from supporting conclusions with data rather than simply intuition.

I would like my data to help answer how we can make U.S. citizens realize what diseases are most impactful.

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.

How did Brazil become a global epicenter of the outbreak? After seeming to ease, is the virus making a comeback?

A world leader in infections and deaths.

Latin America became an epicenter of the coronavirus pandemic in May, driven by Brazil’s ballooning caseload. Ten months after its first known case, Brazil has had more than 7.9 million cases and over 200,000 deaths.

In early June, Brazil began averaging about 1,000 deaths per day from Covid-19, joining the United States — and later India — as the countries with the world’s largest death tolls.

This dataset contains information about COVID-19 in Brazil extracted on the date 16/06/2021. It is the most updated dataset available about Covid in Brazil

Features:

🔍 date: date that the data was collected. format YYYY-MM-DD.
🔍 state: Abbreviation for States. Example: SP
🔍 city: Name of the city (if the value is NaN, they are referring to the State, not the city)
🔍 place_type: Can be City or State
🔍 order_for_place: Number that identifies the registering order for this location. The line that refers to the first log is going to be shown as 1, and the following information will start the count as an index.
🔍 is_last: Show if the line was the last update from that place, can be True or False
🔍 city_ibge_code: IBGE Code from the location
🔍confirmed: Number of confirmed cases.
🔍deaths: Number of deaths.
🔍estimated_population: Estimated population for this city/state in 2020. Data from IBGE
🔍estimated_population_2019: Estimated population for this city/state in 2019. Data from IBGE.
🔍confirmed_per_100k_inhabitants: Number of confirmed cases per 100.000 habitants (based on estimated_population).
🔍death_rate: Death rate (deaths / confirmed cases).

Acknowledgements

This dataset was downloaded from the URL bello. Thanks, Brasil.IO! Their main goal is to make all Brazilian data available to the public DATASET URL: https://brasil.io/dataset/covid19/files/ Cities map file https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2020/Brasil/BR/

Similar Datasets

COVID-19 - https://www.kaggle.com/rafaelherrero/covid19-brazil-full-cases-17062021 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

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.

All-cause, COVID-19, and non-COVID-19 ASDR for ages 25+ by state and time period.

Background The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. Objective To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. Methods We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Results Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 – 2.18), Renal failure (OR:1.76; CI:1.61 – 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 – 1.67), Other bacterial diseases (OR:1.45; CI:1.31 – 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 – 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 – 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 – 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 – 1.32), Other forms of heart disease (OR:1.18; CI:1.09 – 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 – 1.27), Diabetes mellitus (OR:1.14; CI:1.03 – 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 – 1.21). Conclusion We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.