Our group at UC Berkeley is working to help forecast the severity of the epidemic both for individual counties and individual hospitals. As a byproduct, we have and will continue to produce models, visualizations, and curated datasets (including confirmed cases/deaths, demographics, risk factors, social distancing data) that can be used by other teams in the fight against COVID-19. We are collaborating with Response4Life, a non-profit organization, whose goal is to blunt the effect of COVID-19 through the production and appropriate distribution of PPE, medical equipment, and medical personnel to healthcare facilities across the United States.
For visualizations (updated daily), see the project website
- Data: We have compiled and cleaned a large corpus of hospital- and county-level data from a variety of public sources to aid data science efforts to combat COVID-19.
- At the hospital level, the data include the location of the hospital, the number of ICU beds, the total number of employees, the hospital type, and contact information
- At the county level, our data include socioeconomic factors, social distancing scores, and COVID-19 cases/deaths from USA Facts and NYT, automatically updated every day.
- Modeling: Using this data, we have developed a short-term (3-5 days) forecasting model for mortality at the county level. This model combines a county-specific exponential growth model and a shared exponential growth model through a weighted average, where the weights depend on past prediction accuracy.
- Severity index: The Covid pandemic severity index (CPSI) is designed to help aid the distribution of medical resources to hospitals. It takes on three values (3: High, 2: Medium, 1: Low), indicating the severity of the covid-19 outbreak for a hospital on a certain day. It is calculated in three steps.
- county-level predictions for number of deaths are modeled
- county-level predictions are allocated to hospitals within counties proportional the their total number of employees
- final value is decided by thresholding the number of cumulative predicted deaths for a hospital (=current recorded deaths + predicted future deaths)
- download the processed data (as a pickled dataframe
df_county_level_cached.pkl) from this folder and place into thedatadirectory - Can now load/merge the data:
import load_data
df = load_data.load_county_level(data_dir='/path/to/data')
print(df.shape) # (1212, 7306)- note: (non-cumulative) daily cases + deaths are in
data/usafacts/confirmed_cases.csvanddata/usafacts/deaths.csv(updated daily) - note: abridged csv with county-level info such as demographics, hospital information, risk factors, social distancing, and voting data is at
data/df_county_level_abridged_cached.csv - for more data details, see ./data/readme.md
- we are constantly monitoring and adding new data sources
- we are keeping track of relevant data news here
- To get deaths predictions of the naive exponential growth model, the simplest way is to call (for more details, see ./modeling/readme.md)
df = add_preds(df, NUM_DAYS_LIST=[1, 2, 3]) # adds keys like "Predicted Deaths 1-day"
# NUM_DAYS_LIST is number of days in the future to predict- County-level data summaries from JHU
- More aggregated county-level data from Caltech
- UChicago GeoData visualization team
The UC Berkeley Departments of Statistics, EECS led by Professor Bin Yu (group members are all alphabetical by last name)
- Yu group team (Data/modeling): Nick Altieri, Rebecca Barter, James Duncan, Raaz Dwivedi, Karl Kumbier, Xiao Li, Robbie Netzorg, Briton Park, Chandan Singh (student lead), Yan Shuo Tan, Tiffany Tang, Yu Wang
- the response4Life team and volunteers (Organization/distribution)
- Kolak group team (Geospatial visualization): Qinyun Lin
- Medical team (Advice from a medical perspective): Aaron Kornblith, David Jaffe
- Shen Group team (IEOR): Junyu Cao, Shunan Jiang, Pelagie Elimbi Moudio
- Helpful input from many including: SriSatish Ambati, Rob Crockett, Marty Elisco, Valerie Karplus, Andreas Lange, Samuel Scarpino, Suzanne Tamang, Tarek Zohdi