Coronavirus Response: Using Data to Make a Difference
Abstract
This blog post provides an overview of the March 2020 Nature Scientific Data publication "Epidemiological data from the COVID-19 outbreak, real-time case information." Subscribers can access the full article through Nature's website.
A real-time epidemiological database for COVID-19
To help inform public health decision making amid the COVID-19 pandemic, Dr. Sumiko Mekaru, a Booz Allen life science expert, has teamed with fellow scientists and researchers to create an openly available, real-time database of individual-level epidemiological data. This data can be used to further understand the epidemiological COVID-19 outbreak around the globe.
The project, recently published in Nature, collected 18,529 geopositioned records from December 2019 to February 2020. In conducting this work, Sumiko and her teammates aim to aid in the analysis and tracking of the pandemic and better understand the nature of the virus at the individual level. The data has been formatted into a global COVID-19 pandemic map which continues to be updated in real time.
“This project allowed the team to look at data from different countries and provide the best data that's publicly available to everyone, not just data that's available in a language or format that they can read.”
- Dr. Sumiko Mekaru, Booz Allen life science expert
Epidemiology is a branch of medicine that works with the incidence, distribution, and potential control of diseases and other health factors. Real-time case data is important to communicate risks, evaluate outbreaks, and anticipate the spread of infection. Since the outbreak began in December 2019, when numerous cases of coronavirus-infected pneumonia were recorded in Wuhan, China, epidemiologists have been working nonstop to understand how COVID-19 behaves, incubates, and spreads.
To generate their database, Sumiko and her colleagues used official government sources, peer-reviewed scientific papers, and recent news reports. Points highlighted in the data include key dates in the disease lifecycle, symptoms, demographic information for effected parties, and geographic info down to the district level.
A full breakdown of the dataset is accessible via GitHub. The team will continue to update it as new information becomes available.
