General Census Data

If you would like the model to use your hospital's census as an input, please upload two CSV files that combined contain the following four covariates for dates since March 13th, 2020:

1. ICU COVID Census

2. ICU non-COVID Census

3. Floor COVID Census

4. General Medicine Floor non-COVID Census

5. Cumulative COVID Admits up until each Day

If you do not provide your own hospital's census, then the model will only use the inputs in the `Calculator` tab. To ease the usage, please use the provided two CSV file templates given below.

Please note:

1. Do not change the `Date` column. All uploaded files must start from March 13th, 2020, which is the default of our model. Please enter 0s into the first rows if you do not have data for the earlier dates.

2. All input values should be non-empty up to a certain date (e.g. if you have the data input up until March 20th, all cells from March 13th to March 20th should be non-empty).

Click here for Template 1.
Click here for Template 2.



If you inputted your own CSV files, the two data frames are displayed on the right hand of the screen. Please check that the data are outputted correctly before continuing.

These models are planning tools and not predictions. They are based on data from Stanford and several public sources. The tools include assumptions that are changing as more information becomes available and will continue to evolve.

General Parameters


Hospital Starting Status Parameters


COVID Patient Population Parameters

Input the % breakdown of COVID patients who go through the five different paths listed below. Note these must sum to 1.


Length of Stay Parameters

Input the average length of stay (in days) in each unit for various COVID patient cohorts. Note the second `Floor` column is for patients who are coming from the ICU.

Floor Only

Floor
ICU
Floor

Floor to ICU to Floor

Floor
ICU
Floor

Floor to ICU

Floor
ICU
Floor

ICU to Floor

Floor
ICU
Floor

ICU Only

Floor
ICU
Floor
These models are planning tools and not predictions. They are based on data from Stanford and several public sources. The tools include assumptions that are changing as more information becomes available and will continue to evolve.

Please cite as Teng Zhang et al. A model to estimate bed demand for COVID-19 related hospitalization. medRxiv doi:https://doi.org/10.1101/2020.03.24.20042762

Link of the paper
Codes posted on Github

A model to estimate bed demand for COVID-19 related hospitalization

This model is designed to facilitate hospital planning with estimates of the daily number of Intensive Care (IC) beds, Acute Care (AC) beds, and ventilators necessary to accommodate patients who require hospitalization for COVID-19 and how these compare to the available resources. To use the model, input estimates of the characteristics of the patient population and hospital capacity.

The first day of the simulation (Day 0) is fixed. For each subsequent day the model uses the projected number of new COVID-19 patients, partitions the patients into different cohorts, and updates the number of COVID-19 patients requiring IC and AC beds as follows:

COVID-19 Admissions are projected with exponential growth based on the inputs of the doubling time (the time it takes for the cumulative number of patients to double) and the initial number of patients. The patients and their length of stay are partitioned into 5 care cohorts each defined by the patient path through the hospital. These are based on inputs into the Length of Stay Parameters. The results are given as follows. The number of:

- IC beds required each day is the sum of the number of COVID+ and COVID- IC patients

- Patients to be cared for by the Medical Service each day is the sum of the number of COVID+

- AC patients and COVID- patients being cared for by the Medicine Service.

- AC patients and COVID- patients being cared for by the Medicine Service.

Ventilators required is estimated as the sum of 50% of non-COVID IC patients and 100% of COVID IC patients.

Definitions

Doubling time is defined by the amount of time it takes a population to double in size. In this case, assuming exponential growth in the number of COVID-19 cases, we are defining the doubling time as the number of days it takes for cases to double.

References

[1] Zhou, Fei, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. Published online March 11, 2020.
[2]Guan, Wei-jie, et al. Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine. Published online February 28, 2020.
Contact:
Comments and Questions are welcomed!
Created by:

Teng Zhang, Kelly McFarlane, Jacqueline Vallon, Linying Yang, Jin Xie, Jose Blanchet, Peter Glynn, Kristan Staudenmayer, Kevin Schulman, and David Scheinker


For their help, we thank:

Johannes Ferstad, Andy Shin, Raymond Ye Lee, Sehj Kashyap, Shum Kenny, Saurabh Gombar, Nigam Shah