As far back as 2001, a John Hopkins Bloomberg School of Public Health report highlighted how unprepared the world was for a global outbreak of infectious diseases. The report singled out the strain pandemics would put on the world’s healthcare systems and how they’ll struggle to provide adequate care. Two decades later, the COVID-19 epidemic has shown this to be true as healthcare and medical facilities struggle to deal with the influx of patients amidst limited resources.
The early figures from the disease outbreak saw countries scrambling to acquire more resources such as ventilators, masks, gloves or personal protective equipment (PPE) to ensure medical personnel could handle it. Although the effects of this reactive approach to dealing with pandemics are still being studied, the outbreak has proved that a preemptive approach to pandemics saves more lives.
A case in point was the ramping up of test-kit production, PPE availability and general care capacity in Germany and Switzerland. Statistics from the World Health Organization (WHO) show that these nations fared and are faring better with providing care during the pandemic. Their relative successes have been attributed to adequate capacity planning and decision making powered by decision and simulation modeling.
In response to the COVID-19 outbreak and the need to analyze the need for critical healthcare resources during pandemics, the Simio team has created an analytical model for resource planning.
Simio’s Infectious Diseases and Resource Planning Model
The ability of a healthcare provider and/or facility to combat infectious diseases is directly impacted by the number of critical resources available to them. The Simio ‘Infectious Disease and Resource Planning Model’ employed the use of Discrete Event Simulation (DES) to anticipate the demand for critical resources which includes:
- Bed space
- Ventilator machines
- Masks
- Gloves
Purpose:
The ‘Infectious Diseases and Resource Planning Model’ is designed to help healthcare providers, government agencies, and non-governmental organizations plan better for pandemics. The model analyzes the need for the critical resources listed above against a community’s population and the infection rate within the specified population.
Approach:
To ensure accuracy, the model uses real-time data such as the contagion factor (R0) of COVID -19, and the social distancing practices recommended by WHO. The model also made use of approximations such as approximating the population a healthcare facility services, the number ofinfected cases , and the resources available to the facility. Thus, within the model, the following parameters are available:
- Service Area Population – The population for the model is diversified to integrate all age groups. The probability for each age group to need hospitalization or intensive care is also factored in.
- Contagion Factor – The expected number of secondary cases produces by one infected case in a susceptible population.
- Initial Reported Cases – The ‘initial reported cases’ takes into consideration both symptomatic and asymptomatic patients which affects reporting.
- Social Distancing Factor – A value between 0 and 1 represents the social distancing factor. A value of 0 means no social distancing was observed within the population. A value of 0.5 indicates approximately 50% observed social distancing practice while 1 represents the entirety of the population observed social distancing.
The model also includes the following resource capacity availability metrics for the healthcare service provider and/or facility:
- Hospital Bed Capacity
- Initial Ventilator Capacity
*It is important to note that system assumptions and real-time data can be changed to accommodate the resources and situation of specific healthcare providers, populations, and diverse strains of infectious diseases.
Model Assumptions:
The model also makes some important assumptions based on real-time occurrences. These assumptions include the following:
- The input for the hospital bed capacity refers to the number of beds the hospital can provide for patients infected during the pandemic. It does not take into account reserved bed spaces for other emergencies.
- If the facility runs out of available bed spaces, infected patients are sent to another facility or are treated at home and self-quarantined to eliminate its spread.
- The recovered population does not return to the facility and are assumed to be immune to the disease and cannot infect others.
- Patients in need of ventilators are assumed to be in the ICU and require ventilators through the entire duration of their hospital stay.
- Patients needing ventilators and cannot be assigned one are sent to other facilities.
Using the Infectious Diseases and Resource Planning Model
The model has been simplified to ensure it can be used with ease by stakeholders and anyone providing or planning to provide healthcare services during pandemics. The central area represents the facility and objects are the cases admitted in the hospital.
To run and manipulate the model, buttons have been provided to make changes in real-time. This includes:
- Beds button : Clicking on the ‘+1’ button adds a bed space while ‘-1’ removes a bed space. This is same for the other bed buttons as they add or subtract the corresponding number of bed spaces they represent.
- Ventilators Button: You can also add and subtract the number of available ventilators within the models using the ventilator buttons.
- Controls – System Assumptions: The system assumptions window allows you change system assumption metrics to mirror particular situations before running the model. Unlike the quick use buttons, the controls represent the initial situation at the start of a pandemic.
With these buttons and controls, dynamic experiments can be executed using the model as the pandemic unfolds.
Springfield Community as A Case Study
Using the fictional town of Springfield as a case study, the town has a population of 30,000 individuals. This population’s age demographic consist of:
- Age 50 and above – 25%
- Age 30 to 50 – 35%
- Age 1 to 30 – 40%
The Springfield hospital has a bed capacity of 100 spaces, 20 ventilators, and a thousand PPE and has always provided excellent healthcare services to its community. With the expected outbreak of COVID-19, stakeholders are expected to plan for an increased influx of infected patients.
The model integrates the population and its age demographics, available resources, and the spread of the disease. With the model, stakeholders can experiment and learn more about how the addition of extra bed spaces and ventilators can help ensure adequate care is provided to the community.
The model also provides real-time estimates highlighting deficiencies in the availability of bed spaces, ventilators, and PPE as the infected cases increases. As a stakeholder, you can click on the ‘add bed’ and add ‘ventilator’ buttons to estimate how adding an extra 10, 100 or 200 beds will help deal with the pandemic. These additional bed spaces will also lead to the need for more PPE and the model will track the number of additional PPE healthcare providers will require with the increased care-taking capacity.
Stakeholders of the healthcare facility can estimate how a week, month, two months or more of containing the pandemic will affect the facility’s resources in real-time. The result gotten from the model will help the facility and community apply a preemptive approach to dealing with infectious diseases by sourcing for critical resources ahead of time.
You can watch a video of Simio’s ‘Infectious Diseases and Resource Planning Model’ in action here. The video consist of a basic experiment showcasing how changing the system controls impacts the deficiency count of critical equipment a health facility requires during a pandemic.