Global COVID-19 Time Series Data (Confirmed, Recovered, Deaths by Location)
Public Health & Epidemiology
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Comprehensive time series data on the global spread of COVID-19, detailing the total number of confirmed cases, recoveries, and deaths by location. Compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) in collaboration with the ESRI Living Atlas Team and JHU Applied Physics Lab (APL), this dataset offers insights into the pandemic’s progression across diverse regions and time periods.
Features:
- Date: Specific date of recorded data.
- Country/Region: Geographic location, typically at the country or region level.
- Province/State: Subdivision data, where available (e.g., states in the US, provinces in Canada).
- Confirmed Cases: Cumulative number of confirmed COVID-19 cases.
- Deaths: Cumulative number of deaths attributed to COVID-19.
- Recovered: Cumulative number of recoveries (where available).
Usage:
The dataset is useful for:
- Statistical analysis of COVID-19 trends and patterns.
- Forecasting models to estimate the spread and potential impact of pandemics.
- Visualization projects to map and display the pandemic’s impact over time.
Coverage:
Data is provided globally, covering various countries, regions, and some states/provinces where available. The dataset's granularity allows for comprehensive insights into the pandemic’s reach and severity across different locations.
License:
CC BY 4.0 (Creative Commons)
Who can use it:
Epidemiologists, data scientists, public health researchers, and policy-makers can utilize this dataset for research, statistical modeling, and public health strategy development.
How to use it:
- Conduct historical analyses to understand the progression of COVID-19 cases across regions.
- Build predictive models to forecast infection rates and mortality trends.
- Develop visualization tools (e.g., heat maps, time-series graphs) to illustrate the pandemic's evolution over time.