DOI: 10.5061/dryad.1ns1rn8rx
title: An interactive tool to forecast us hospital needs in the Coronavirus 2019 pandemic

description: We developed an application (https://rush-covid19.herokuapp.com/) to aid
 US hospitals in planning their response to the ongoing COVID-19
 pandemic. Our application forecasts hospital visits, admits,
 discharges, and needs for hospital beds, ventilators, and personal
 protective equipment by coupling COVID-19 predictions to models of time
 lags, patient carry-over, and length-of-stay. Users can choose from seven
 COVID-19 models, customize a large set of parameters, examine trends in
 testing and hospitalization, and download forecast data. The data and
 scripts contained herein are used to generate Figure 1 of the associated
 manuscript, which presents general forms of the models used by
 our application and presents results for each model across time.
description: Date provided here are based on data our application uses.
 Specifically, aggregated reports of cumulative cases across US states and
 territories from the Johns Hopkins University Center for Systems Science
 and Engineering (JHU CSSE) public GitHub repository, and state and
 territory population sizes based on publically avialable US Census Bureau
 data (2010 – 2019). See the associated mansuscript and/or our
 application's public GitHub repository
 (https://github.com/Rush-Quality-Analytics/SupplyDemand) for greater
 details.  <strong>To recreate Figure 1 of the
 main manuscript, run the following scripts in a python environment or
 suitable terminal window with python
 3.6+ installed:</strong> 1. ModelFxns_Figs.py --
 A script to generate the general predicted forms of each model; associated
 with Figure 1 of the associated manuscript. Generates the Model_Forms.png
 file located in the figures folder. 2.
 ModelPerformance_Figs.py -- A script to generate results figures for each
 model; associated with Figure 1 of the associated manuscript. Generates
 the Model_Performance.png file located in the figures folder.<br>
   <strong>Included
 data files</strong> 1. 
 model_results_dataframe.pkl -- A python-specific pickle file located in
 the data folder and which contains results for each model's
 predictive accuracy across time and US states and terroritories. Used
 by ModelPerformance_Figs.py. 2. COVID-CASES-DF.txt -- A
 file located in the data folder and which contains data downloaded and
 curated from JHU CSSE. 3. StatePops.csv -- A file
 located in the data folder and which contains data on US State and
 territory population sizes as well as reported dates of COVID-19 arrival
 (gathered from state/territory public websites).
 <strong>Additional python scripts</strong>
 1. results_dataframe.py -- A script to regenerate the
 model_results_dataframe.pkl file. Warning: This script may take several
 days to run because of the many iterations needed for the SEIR-SD model.
 Running this script will overwrite the existing
 model_results_dataframe.pkl file, so take necessary precautions.
 2. model_fxns.py -- A script containing functions for running
 models used by our application. This script is used by
 results_dataframe.py to generatth the model_results_dataframe.pkl
 file.
description: 1. The model_results_dataframe.pkl file is a python specific file
 format. 2. Running the ModelFxns_Figs.py
 and ModelPerformance_Figs.py is all that is needed to recreate the
 subplots of figure 1. The user should have the following
 libraries/softwares installed: Python 3.6 or
 greater<br> numpy 1.16 or greater<br> pandas 0.24 or
 greater