Global Disease and Treatment Data

Columns

• Country: String - Geographical location (e.g., Russia, South Africa).
• Year: Integer - The year the data was recorded, ranging from 2000 to 2024.
• Disease Name: String - Specific disease (e.g., COVID-19, Zika).
• Disease Category: String - Broader classification of disease (e.g., Metabolic, Parasitic).
• Prevalence Rate (%): Float - Percentage of population with the disease, ranging from 0.1% to 20%.
• Incidence Rate (%): Float - Percentage of new cases, ranging from 0.1% to 15%.
• Mortality Rate (%): Float - Percentage of deaths due to the disease, ranging from 0.1% to 10%.
• Age Group: Integer - Age classification (exact groups not detailed but values represent various age cohorts).
• Gender: String - Sex of the population (Male, Female, Other).
• Population Affected: Integer - Number of people affected, ranging from 1,000 to 1,000,000.
• Healthcare Access (%): Float - Percentage of population with healthcare access, ranging from 50% to 100%.
• Doctors per 1000: Float - Number of doctors per 1,000 people, ranging from 0.5 to 5.
• Hospital Beds per 1000: Float - Number of hospital beds per 1,000 people, ranging from 0.5 to 10.
• Treatment Type: String - Category of treatment (e.g., Surgery, Therapy).
• Average Treatment Cost (USD): Float - Mean cost of treatment in US Dollars, ranging from $100 to $50,000.
• Availability of Vaccines/Treatment: Boolean - Indicates if vaccines or treatment are available (True/False).
• Recovery Rate (%): Float - Percentage of patients who recover, ranging from 50% to 99%.
• DALYs: Float - Disability-Adjusted Life Years, ranging from 1 to 5,000.
• Improvement in 5 Years (%): Float - Percentage improvement over a five-year period, ranging from 0% to 10%.
• Per Capita Income (USD): Float - Average income per person in US Dollars, ranging from $500 to $100,000.
• Education Index: Float - Educational attainment metric, ranging from 0.4 to 0.9.
• Urbanization Rate (%): Float - Percentage of population living in urban areas, ranging from 20% to 90%.

Distribution

Usage

• Healthcare Policy Analysis: To understand disease prevalence and identify countries requiring further investment in healthcare infrastructure.
• Epidemiological Studies: For examining relationships between disease prevalence and socioeconomic factors such as income, education, and urbanisation.
• Machine Learning Models: For developing predictive models to forecast disease trends, mortality rates, and treatment effectiveness using historical data.
• Global Health Research: To pinpoint regions that could benefit from targeted interventions or public health initiatives.

Coverage

License

Who Can Use It

• Healthcare Policy Makers: To inform decisions on resource allocation and public health strategies.
• Epidemiologists: For studying disease patterns and their determinants.
• Data Scientists/Machine Learning Developers: To build and train models for health predictions.
• Global Health Researchers: To gain insights into worldwide health challenges and solutions.

Dataset Name Suggestions

• Global Health Statistics
• Global Disease and Treatment Data
• International Health Outcomes Dataset
• Worldwide Health Trends Data

Attributes