Dataset Overview

The dataset provides a comprehensive look at HIV/AIDS adult prevalence rates, the number of people living with HIV, and annual deaths across different countries. It is based on publicly available data sources such as the CIA World Factbook, UNAIDS AIDS Info, and other global health organizations. The dataset primarily focuses on adult HIV prevalence (ages 15–49) and includes estimates from recent years (e.g., 2023–2024).

Data Science Applications

This dataset can be used for: - Epidemiological Analysis: Understanding the regional distribution of HIV/AIDS and identifying high-prevalence areas. - Predictive Modeling: Developing machine learning models to predict HIV prevalence trends or identify risk factors. - Resource Allocation: Informing policymakers about regions requiring urgent intervention or resource allocation. - Health Outcome Monitoring: Tracking progress in combating HIV/AIDS over time. - Social Determinants Research: Analyzing the relationship between socio-economic factors and HIV prevalence.

Column Descriptors

1. Country/Region: The geographical area being analyzed.
2. Adult Prevalence (%): Percentage of adults aged 15–49 living with HIV.
3. Number of People with HIV/AIDS: Absolute count of individuals living with HIV in the region.
4. Annual Deaths from HIV/AIDS: Number of deaths attributed to HIV/AIDS annually.
5. Year of Estimate: The year when the data was collected or estimated.

Ethically Mined Data

The dataset is ethically sourced from publicly available and credible platforms such as the CIA World Factbook, UNAIDS, and WHO. These organizations ensure transparency and ethical standards in data collection, protecting individual privacy while providing aggregate statistics for research purposes.

Acknowledgments

1. Data Source Platforms:
   • CIA World Factbook
   • UNAIDS AIDS Info
   • WHO Global Health Observatory
2. Dataset Visualization Image:
   • Created using DALL-E 3 for illustrative purposes.
3. Research Support:
   • Contributions from platforms like ResearchGate, NIMH, and others for insights into data science applications in HIV research.

This dataset serves as a valuable tool for researchers, policymakers, and public health professionals in addressing the global challenge of HIV/AIDS.

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Since the introduction of antiretroviral treatment (ART), people living with HIV worldwide live into older age. This observational study described the characteristics, clinical outcomes, and mortality of older adults living with HIV (OALHIV) receiving ART from the National AIDS program in northern Thailand. Participants aged ≥ 50 years were recruited from the HIV clinics in 12 community hospitals. Data were obtained from medical records and face-to-face interviews. In 2015, 362 OALHIV were enrolled; their median (interquartile range) age and ART duration were 57 years (54–61), and 8.8 years (6.4–11.2), respectively. At study entry, 174 (48.1%) had CD4 counts ≥ 500 cells/mm3; 357 of 358 (99.6%) with available HIV RNA results were virologic-suppressed. At the year 5 follow-up, 39 died, 11 were transferred to other hospitals, 3 were lost to follow-up, and 40 did not contribute data for this analysis, but remained in care. Among the 269 who appeared, 149 (55%) had CD4 counts ≥ 500 cells/mm3, and 227/229 tested (99%) were virologic-suppressed. The probability of 5-year overall survival was 89.2% (95% confidence interval, CI 85.4–92.1%). A significantly low 5-year overall survival (66%) was observed in OALHIV with CD4 counts < 200 cells/mm3 at study entry. The most common cause of death was organ failure in 11 (28%), followed by malignancies in 8 (21%), infections in 5 (13%), mental health-related conditions in 2 (5%), and unknown in 13 (33%). In OALHIV with stable HIV treatment outcomes, mortality from non-infectious causes was observed. Monitoring of organ function, cancer surveillance, and mental health screening are warranted.

BackgroundTuberculosis (TB) is the leading cause of infectious disease deaths worldwide and is the leading cause of death among people with HIV. The World Health Organization (WHO) has called for collaboration between public and private healthcare providers to maximize integration of TB/HIV services and minimize costs. We systematically reviewed published models of public-private sector diagnostic and referral services for TB/HIV co-infected patients.MethodsWe searched PubMed, the Cochrane Central Register of Controlled Trials, Google Scholar, Science Direct, CINAHL and Web of Science. We included studies that discussed programs that linked private and public providers for TB/HIV concurrent diagnostic and referral services and used Review Manager (Version 5.3, 2015) for meta-analysis.ResultsWe found 1,218 unduplicated potentially relevant articles and abstracts; three met our eligibility criteria. All three described public-private TB/HIV diagnostic/referral services with varying degrees of integration. In Kenya private practitioners were able to test for both TB and HIV and offer state-subsidized TB medication, but they could not provide state-subsidized antiretroviral therapy (ART) to co-infected patients. In India private practitioners not contractually engaged with the public sector offered TB/HIV services inconsistently and on a subjective basis. Those partnered with the state, however, could test for both TB and HIV and offer state-subsidized medications. In Nigeria some private providers had access to both state-subsidized medications and diagnostic tests; others required patients to pay out-of-pocket for testing and/or treatment. In a meta-analysis of the two quantitative reports, TB patients who sought care in the public sector were almost twice as likely to have been tested for HIV than TB patients who sought care in the private sector (risk ratio [RR] 1.98, 95% confidence interval [CI] 1.88–2.08). However, HIV-infected TB patients who sought care in the public sector were marginally less likely to initiate ART than TB patients who sought care from private providers (RR 0.89, 95% CI 0.78–1.03).ConclusionThese three studies are examples of public-private TB/HIV service delivery and can potentially serve as models for integrated TB/HIV care systems. Successful public-private diagnostic and treatment services can both improve outcomes and decrease costs for patients co-infected with HIV and TB.

Context

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs). DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise)

• 01. Country/Territory - Name of the Country/Territory
• 02. Code - Country/Territory Code
• 03. Year - Year of the Incident
• 04. Meningitis - No. of People died from Meningitis
• 05. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
• 06. Parkinson's Disease - No. of People died from Parkinson's Disease
• 07. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
• 08. Malaria - No. of People died from Malaria
• 09. Drowning - No. of People died from Drowning
• 10. Interpersonal Violence - No. of People died from Interpersonal Violence
• 11. Maternal Disorders - No. of People died from Maternal Disorders
• 12. Drug Use Disorders - No. of People died from Drug Use Disorders
• 13. Tuberculosis - No. of People died from Tuberculosis
• 14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
• 15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
• 16. Neonatal Disorders - No. of People died from Neonatal Disorders
• 17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
• 18. Self-harm - No. of People died from Self-harm
• 19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
• 20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
• 21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
• 22. Neoplasms - No. of People died from Neoplasms
• 23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
• 24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
• 25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
• 26. Poisonings - No. of People died from Poisoning
• 27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
• 28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
• 29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
• 30. Digestive Diseases - No. of People died from Digestive Diseases
• 31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
• ...

About Dataset

Context:

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs).

DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content:

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise):

1. Country/Territory - Name of the Country/Territory
2. Code - Country/Territory Code
3. Year - Year of the Incident
4. Meningitis - No. of People died from Meningitis
5. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
6. Parkinson's Disease - No. of People died from Parkinson's Disease
7. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
8. Malaria - No. of People died from Malaria
9. Drowning - No. of People died from Drowning
10. Interpersonal Violence - No. of People died from Interpersonal Violence
11. Maternal Disorders - No. of People died from Maternal Disorders
12. Drug Use Disorders - No. of People died from Drug Use Disorders
13. Tuberculosis - No. of People died from Tuberculosis
14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
16. Neonatal Disorders - No. of People died from Neonatal Disorders
17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
18. Self-harm - No. of People died from Self-harm
19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
22. Neoplasms - No. of People died from Neoplasms
23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
26. Poisonings - No. of People died from Poisoning
27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
30. Digestive Diseases - No. of People died from Digestive Diseases
31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
32. Acute Hepatitis - No. of People died from Acute Hepatitis Structure of the Dataset

Acknowledgement:

This Dataset is created from Our World in Data. This Dataset falls under open access under the Creative Commons BY license. You can check the FAQ for more informa...

Dataset Overview 📝

This dataset includes key health indicators for over 200 countries, covering the period from 1999 to 2023.

Health Indicators:

• Current Health Expenditure (% of GDP): Shows the percentage of a country’s GDP allocated to health expenditure.
• Life Expectancy at Birth (Total Years):The average number of years a newborn is expected to live, assuming age-specific mortality rates remain constant.
• Maternal Mortality: The number of maternal deaths per 100,000 live births.
• Infant Mortality Rate:The number of infant deaths (under 1 year) per 1,000 live births.
• Neonatal Mortality Rate: The number of deaths of children under 28 days of age per 1,000 live births.
• Under-5 Mortality Rate: The number of deaths of children under 5 years of age per 1,000 live births.
• Prevalence of HIV (% of population): The percentage of the population aged 15-49 years living with HIV.
• Incidence of Tuberculosis (per 100,000 people):The number of incidence of tuberculosis per 100,000 people.
• Prevalence of Undernourishment (% of population):The percentage of the population whose caloric intake is below the minimum required for a healthy life.

Data Source 🌐

World Bank: This dataset is compiled from the World Bank's health database, providing reliable, updated statistics on health indicators worldwide.

Potential Use Cases 🔍

• Health Policy Research: This dataset is ideal for understanding how different countries allocate resources to health and how these investments correlate with health outcomes such as life expectancy and mortality rates.
• Global Health Trends: Analyzing trends in health spending, mortality rates, and disease prevalence across various regions.
• Predictive Modeling: Building models to predict health outcomes based on historical trends and identifying potential health disparities.
• Health Interventions: Understanding the impact of government spending on health and how it affects different demographics.

Key Questions You Can Explore 🤔

• How does health expenditure correlate with life expectancy across different countries?
• What are the trends in maternal, infant, and under-5 mortality rates over time?
• How do HIV prevalence and tuberculosis incidence vary by region, and what factors contribute to these differences?
• Can we predict which countries are likely to achieve universal health coverage based on current trends?

Important Note ⚠️

• Missing Data: Some values may be missing for certain years or countries, particularly for specific health indicators. Consider using techniques like forward filling, backward filling, or interpolation when performing time series analysis.

Context

The global and regional projections of mortality by cause for years 2015 and 2030 were carried out in 2012 based on the GHE2012 estimates of causes of death for 2011. Earlier projections from 2004 to 2030 were updated using the estimates of deaths by cause for year 2011 as a starting-point, together with revised projections of HIV deaths prepared by UNAIDS and WHO, and revised forecasts of economic growth by region published by the World Bank (baseline scenario). For further information on these estimates and on data sources and methods, refer to The global burden of disease: 2004 update and to the published paper here. It is intended to update these projections soon using the most recent GHE2015 estimates for year 2015 as a starting point.

Acknowledgements

All this data could be founded on WHO site, you can read the paper about this dataset here: http://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.0030442&type=printable

Inspiration

I'm working on a research about depression and need other illness and mortality data.

Every year, over 10 million people worldwide contract tuberculosis (TB). The 2024 World Health Organisation TB global report indicated that 32% of the total deaths were children and adolescents under 15 years old. The scale of TB highlights the urgent need for action to end the global epidemic by 2030. This study aims to evaluate the mortality rate, survival probabilities, and factors associated with mortality among children and adolescents with TB in Tanzania. A retrospective cohort study was conducted from the Tanzania National Tuberculosis and Leprosy Programme data, which included individuals under 15 years old who began TB treatment between 1st January 2023 and 31st December 2023. The last patient’s end-of-follow-up time was on 16th June 2024. The primary outcome of interest in our study was death. We calculated overall and covariate-specific TB mortality rates per 1,000 person-months. The Kaplan-Meier curve was employed to estimate survival probabilities. A total of 10,491 children and adolescents receiving TB treatment were included, nearly half of whom, 5,940 (56.62%), were under age 5 years. A total of 177 (1.69%) died, resulting in a crude mortality rate of 2.86 per 1,000 person-months. Furthermore, TB and HIV co-infection individuals had five times the risk of death (aHR = 5.03, 95% CI = 3.40-7.47, p 

Tuberculosis is one of the most common causes of death globally.

By Saloni Dattani, Fiona Spooner, Hannah Ritchie and Max Roser

Data description:

In richer countries, the impact of tuberculosis has been reduced significantly over history, but in poorer parts of our world, it continues to be a major challenge even today: it causes an estimated 1.2 million deaths annually.

Tuberculosis is caused by the bacteria Mycobacterium tuberculosis.

The bacteria spreads through respiratory particles and tends to cause tuberculosis in people with risk factors such as undernourishment, HIV/AIDS, smoking, and existing chronic conditions.

The disease involves symptoms like coughing, fatigue and night sweats, and can damage the lungs, the brain, kidneys and other organs, which can be fatal.

But it is treatable with a combination of specific antibiotics. Without being diagnosed correctly, however, people do not receive the proper treatment. This leaves them vulnerable, and also increases the risk that antibiotic-resistant strains of the bacteria will develop, which are much more difficult and expensive to treat.

With greater effort to tackle its risk factors and improve testing and treatment for the disease, the world can relegate tuberculosis to history — not just in the richer parts of the world, but for everyone.

Data number 1: Tuberculosis is still common in many parts of the world In high-income countries, tuberculosis is largely a disease of the past. Since the beginning of the 20th century, its impact has been significantly reduced with the development of antibiotics and improvements in healthcare and living standards.

Data number 2: Tuberculosis kills over a million people annually, most of whom are adults Tuberculosis kills over a million people each year, as you can see in the chart. The chart shows that most of those who die from tuberculosis are adults.

Data number 3: Many people with tuberculosis are undiagnosed Although tuberculosis is typically a disease of the lungs, the bacteria can affect many organs in the body, and people who are infected don’t always have respiratory symptoms. Instead, they may experience weight loss, breathlessness, fever, or night sweats.

Data number 4: Antibiotic resistance is an important consideration during treatment People with tuberculosis require treatment with a specific combination of antibiotic medications that can kill the bacteria.

Data number 5: HIV increases the risk of developing tuberculosis An HIV infection is a major risk factor for developing tuberculosis.

Good luck

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#

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