Intensive Care Unit Simulation

what? • why? • how? • start simulating • About Autonomio • Issues • License

---

ICUSIM is a Monte Carlo simulator for understanding and forecasting the demand for Intensive Care Unit (ICU) and ventilation resources.

---

What?

ICUSIM dramatically simplifies the process ICU demand, capacity, and fatality simulation. The simulation is based on a logic that closely resembles the current empirical understanding of the problem. The power of Monte Carlo simulation can be summarized in two points:

• Input parameter ranges are based on empirical evidence
• There is no ambiquity in terms of results

Fig 1: An example of simulation result where we test how often peak daily demand for standard ICU capacity stays below 278 (the official forecast of THL in Finland).

This allows the consumer of the information to establish their own point-of-view regarding how likely a certain outcome may be. The Monte Carlo method entirely takes away doubt from the question "given a range of parameters, how often so and so values appear".

---

Why?

• Make forecasts to increase preparadness
• Test plausibility of forecasts made with other methods

---

How?

ICUSIM follows a straightforward logic:

• There is a certain number of patients to start with
• Patients are split between standard and ventilated ICU
• Patients can not move between standard and ventilated ICU
• New patients come in based on doubles_in_days input parameter
• As new patients come in, each is assigned with a probability to survive
• As new patients come in, each is assigned a stay duration
• Released or dead, it happens when stay duration is completed
• If there is less capacity than there is demand, patients will die accordingly

Outcomes are controlled through Input Parameters, which are provided separately for standard ICU and ventilated ICU.

name	type	description
initial_patient_count	int	the number of patients to start with
days_to_simulate	int	number of days to simulate
total_capacity_min	int	minimum for total available capacity
total_capacity_max	int	maximum for total available capacity
ventilated_icu_share_min	float	minimum for ventilated capacity
ventilated_icu_share_max	float	maximum for ventilated capacity
standard_cfr_min	float	minimum case fatality rate for standard ICU
standard_cfr_max	float	maximum case fatality rate for standard ICU
ventilated_cfr_min	float	minimum case fatality rate for ventilated ICU
ventilated_cfr_max	float	maximum case fatality rate for ventilated ICU
standard_duration_min	float	minimum mean duration for standard ICU stay
standard_duration_max	float	maximum mean duration for standard ICU stay
ventilated_duration_factor_min	float	minimum ratio for ventilated capacity per standard standard
ventilated_duration_factor_max	float	maximum ratio for ventilated capacity per standard standard
doubles_in_days_min	float	minimum number of days it takes for exponental growth to happen
doubles_in_days_max	float	maximum number of days it takes for eponental growth to happen
ventilation_rate_min	float	minimum rate at which ventilation is required
ventilation_rate_max	float	maximum rate at which ventilation is required
show_params	bool	prints out the parameters if True

---

💾 Install

Released version:

pip install icusim

Daily development version:

pip install git+https://github.com/autonomio/ICUSIM

---

Start Simulating

To run a simulation, you need two things:

• parameter dictionary
• icusim.MonteCarlo() command

Make sure to follow parameter ranges that you can established with available empirical evidence. A fully functional example that is relevant for Finland is provided below. You can simply change the values to meet the evidence for the area/s of your interest.

params = {'initial_patient_count': 80,
          'days_to_simulate': 50,
          'total_capacity_min': 200,
          'total_capacity_max': 1000,
          'ventilated_icu_share_min': .4,
          'ventilated_icu_share_max': .6,
          'standard_cfr_min': 0.2,
          'standard_cfr_max': 0.6,
          'ventilated_cfr_min': 1.3,
          'ventilated_cfr_max': 1.7,
          'standard_duration_min': 8.5,
          'standard_duration_max': 25.5,
          'ventilated_duration_factor_min': .9,
          'ventilated_duration_factor_max': 1.1,
          'doubles_in_days_min': 2.0,
          'doubles_in_days_max': 12.0,
          'ventilation_rate_min': 0.3,
          'ventilation_rate_max': 0.8}

Next you can start the simulation:

import icusim
results = icusim.MonteCarlo(rounds=1000, param_dict=params)

Access the results of the simulation:

results.df

If you want to also perform sensitivity analysis:

import icusim
results = icusim.SobolSensitivity(rounds=1000, params)

Once the rounds are completed, get the sensitivities:

results.sensitivity('metric_name')

You can also run a single round simulation with daily output:

import icusim

params = icusim.params()
icusim.simulate(params)

Draw a histogram for analyzing the results:

astetik.hist(df, 'ventilated_icu_total_demand')

---

💬 How to get Support

I want to...	Go to...
...troubleshoot	GitHub Issue Tracker
...report a bug	GitHub Issue Tracker
...suggest a new feature	GitHub Issue Tracker
...get support	GitHub Issue Tracker · Discord Chat
...have a discussion	Discord Chat

---

📢 Citations

If you use ICUSIM for published work, please cite:

Autonomio's ICUSIM [Computer software]. (2020). Retrieved from http://github.com/autonomio/ICUSIM.

---

📃 License

MIT License