Developers, data-scientists and researchers are publishing and sharing COVID-19 information and tools on Github. Some of these tools and data sources are definitely worth to take a look at.
CT-scans of COVID-19 patients
Take a look at the ‘covid-chestxray-dataset‘ project. Here they are building a dataset of COVID-19 cases where X-ray or CT data has been made public. This data is then integrated into the models.
Their goal is to use these images to develop AI based approaches to predict and understand the infection. The group will work to release these models using the open source Chester AI Radiology Assistant platform which is designed to scale to a global need by performing the computation locally.
The tasks are as follows using chest X-ray or CT (preference for X-ray) as input to predict these tasks:
- Healthy vs Pneumonia (prototype already implemented Chester with ~74% AUC, validation study here)
- Bacterial vs Viral vs COVID-19 Pneumonia
- Survival of patient
COVID-19 timeseries dataset
This dataset includes time series data tracking the number of people affected by COVID-19 worldwide, including:
- confirmed tested cases of Coronavirus infection
- the number of people who have reportedly died while sick with Coronavirus
- the number of people who have reportedly recovered from it
Data is in CSV format and updated daily. It is sourced from this upstream repository maintained by the amazing team at Johns Hopkins University Center for Systems Science and Engineering (CSSE) who have been doing a great public service from an early point by collating data from around the world.
COVID-19 Hospital Impact Model for Epidemics
The CHIME (COVID-19 Hospital Impact Model for Epidemics) Application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. CHIME enables capacity planning by providing estimates of total daily (i.e. new) and running totals of (i.e. census) inpatient hospitalizations, ICU admissions, and patients requiring ventilation.
These estimates are generated using a SIR (Susceptible, Infected, Recovered) model, a standard epidemiological modeling technique. Our model has been validated by several epidemiologists including Michael Z. Levy, PhD, Associate Professor of Epidemiology, Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine.