Tag Archives: pandemic

COVID-19 is a pandemic that requires systems thinking and solutions

I was on the INFORMS Resoundingly Human to talk about COVID-19 and first responders. You can listen here:

https://pubsonline.informs.org/do/10.1287/orms.2020.02.11p/full/

In the podcast, I discuss supply chains, rationing resources, and disaster planning, and I note how everything old becomes new again. For example, the US is not experiencing its first N95 mask shortage. Systems concepts are important for understanding how to prepare for and respond to a pandemic.

In this post, I want to dig deeper into systems concepts. I wrote a quick primer on systems thinking and explain why systems concepts are important for understanding the COVID-19 pandemic.

What is a system?

A system is a set of things—people, vehicles, basketball teams, hospital beds, or whatever—interconnected in such a way that they produce their own pattern of behavior over time.

Here are three examples:

(1) A car is just a vehicle. A collection of cars can be a traffic jam.

(2) A single ventilator can be used to treat a patient. A hospital’s collection of beds and ventilators are available for treating patients. When a surge of patients require these resources, they may have to wait and queue for these limited resources.

(3) An N95 mask protects first responders from infectious disease when they treat patients. A supply chain of personal protective equipment (PPE) can have delays and shortages, leading to first responders not having the N95 masks they need at any given moment.

How is systems engineering relevant to COVID-19?

COVID-19 is absolutely a medical challenge. It is also a systems challenge that require systems thinking and systems solutions. In systems, decisions are not made in isolation, but rather, decisions are interrelated.

My discipline is operations research: the science of making decisions using advanced analytical methods. Systems require a series of decisions to operate effectively with or without patient surges in a pandemic. Operations research provides the analytical tools required to design and operate systems more effectively and efficiently.

In systems there are many trade-offs and complicated interactions. Here are examples of how systems engineering is important now:

(1) If a first responder does not have adequate personal protective equipment (PPE) such as latex gloves and N95 masks, they are at higher risk from acquiring COVID-19. If they do, they will not be able to treat patients in the coming months, thereby reducing the number of first responders (a critical resource) in the future. This informs how responders should treat patients and ration resources now.

(2) Surges in COVID-19 cases may lead to more patients requiring ventilators than are available in hospitals. This could lead to rationing and painful choices that would not be considered without a patient surge.

Systems concepts will continue to be important in the future. Here is a third example:

(3) One person who gets a vaccine has immunity. If enough people receive vaccines or have immunity from previously having had the disease, we can achieve herd immunity and eliminate person-to-person transmission of the disease even among those who do not have immunity. With herd immunity, the benefits are greater than the sum of its parts.

What can systems thinking tell us about the fatality rate for COVID-19?

It depends. We know that it depends on age, gender, and co-morbidities. The fatality rate is not an exogenously given number, but rather it is a function of the resources available for treating patients, which is endogenous to the system. The fatality rate for COVID-19 is a systems concept. If the number of infected individuals is low enough so that hospitals can handle the surge and give every patient the treatment they require, the fatality rate will be lower (relatively speaking. In absolute terms it will still be too high). The fatality rate will be a lot higher if hospitals are over capacity and have to ration beds and ventilators.

How are my personal decisions related to healthcare systems in the COVID-19 pandemic?

The resources in our healthcare system are being stretched to the limit. The resources include personnel (physicians, nurses, first responders), hospital beds, ventilators, and personal protective equipment. When there are not enough resources to give every COVID-19 patient the best treatment they require, physicians will have to ration resources and make tough choices. Our efforts to delay the second wave as long as possible and to reduce the number of people who require medical treatment will save lives. Flattening the curve is a systems concept aimed at reducing painful tradeoffs and complicated interactions.

How can we prevent the next wave?

Preventing the next wave of any infectious disease is a numbers game. I do not know how to practice medicine but I know how to crunch numbers. The key is to lower the overall transmission rate. The best way to lower the transmission rate varies according to the disease, but there are some basic principles for preventing a disease outbreak from becoming another wave of a pandemic. Best practices include better hygiene practices such as washing your hands and your mobile phones with soap and water, and covering your cough. Limiting the number of people you come in contact with reduces the opportunities for transmission. All those trips to the store to buy extra toilet paper increase one’s chance of contracting COVID-19.

What can we do to prepare for a second wave?

A second wave in a prolonged pandemic is not going to be easy for many of us. I use mathematical models and analytics in my research, and I find them to be useful in my everyday life. My research tells me that I make better decisions with better information and that I should use limited resources wisely. When I think about what it means to apply these principles to my decisions in a pandemic, I realized I can achieve both of these goals by gathering up to date information and following instructions from official, trusted sources such as local and state governments, local police and emergency medical service departments, and the Centers for Disease Control and Prevention. I plan to use the official sources to limit what I think about, worry about, and do in any upcoming waves of the pandemic. We are all inundated with conflicting information and advice from many sources, and it is taking its toll and potentially leading us to make unsafe choices such as making repeated trips to grocery stores to stockpile items we do not need.

 

Related posts:


Travel Bans Can’t Stop this Pandemic

My op-ed entitled “Travel bans can’t stop this pandemic” was published in The Hill. You can read it here.

 


emergency response during mass casualty incidents

Today’s blog post about my research on mass casualty events and emergency response given that COVID-19 has been declared a pandemic by the World Health Organization (WHO). I have four papers in the area that are relevant in the area of emergency medical services (EMS) during mass casualty incidents.

A mass casualty incident (MCI) is an event in which the demand for service overwhelms local resources. Since fire and EMS departments operate at the local level, they can be overwhelmed quite easily. Anything from a multiple vehicle accident to a weather disaster to a hospital evacuation can be considered an MCI. Fire and EMS departments have “mutual aid” agreements with neighboring departments to address the more routine of these incidents and have “Standard Operating Procedures” for a range of more severe incidents. However, switching between such policies in practice is not simple. Moreover, not all mass casualty incidents are the same. Responding to calls for service during a hurricane is different than during a pandemic. In the latter, paramedics and emergency medical technicians can become sick and should stop treating patients, leading to fewer resources for responding to patients that require service. Additionally, we would expect less road congestion and wind during pandemics than in a hurricane evacuation. However, both cases may see a surge of low-acuity patients who request service.

My research focuses on emergency response during MCIs lifts limiting assumptions made by papers in the literature, which often assume that there are enough resources available all the time (which is not a reasonable assumption during MCIs). Here is a summary of four of my papers that have addressed MCIs.

Dubois, E. Albert, L.A. 2020. Dispatching Policies During Prolonged Mass Casualty Incidents. Technical report, University of Wisconsin-Madison.

The newest paper is available as a technical report and is most relevant to COVID-19. It focuses on a large surge of patients that overwhelmed EMS resources. Here, we lift the assumption that a patient’s priority is a fixed input. Instead, we consider patients whose conditions deteriorate over time as they wait for service.  We consider how to assign two types of ambulances to patients, advanced and basic life support. We study how to dispatch ambulances during MCIs while allowing ambulances to idle while less emergent patients are queued. This is similar to keeping a reserve stock of advanced life support ambulances (see the last paper listed in this post). The inherent trade-off is that when low-priority patients are asked to wait for service, they can become high-priority patients. When high-priority patients are asked to wait for service, they can become critical or die. Our solution method is to find dynamic response policies to match two types of ambulances with these three types of patients.  We observe that, under the optimal policies, advanced life support ambulances often remain idle when less emergent patients are queued to provide quicker service to future more emergent patients. It is counter-intuitive to not use all resources all the time during an MCI. However, keeping some resources in reserve ensures that there are resources available at the time the most critical patients need them.

McLay, L.A., Brooks, J.P., Boone, E.L., 2012. Analyzing the Volume and Nature of Emergency Medical Calls during Severe Weather Events using Regression Methodologies. Socio-Economic Planning Sciences 46, 55 – 66.

The second paper seeks to characterize the volume and characteristics of EMS and fire calls for service. It was motivated by the need to deliver routine emergency service during weather emergencies and disasters. What typically happens during emergencies is that there are more calls for service, most of which are low priority calls. Triage becomes more important in these situations, because the most severe calls for service can be drowned out by so many low-priority requests. However, call surges are not the only stress on fire and EMS departments. Road congestion and slow travel times mean that each call takes more time to serve, which can further stress limited resources. As a result, it becomes important to triage calls and assign appropriate resources.

Kunkel, A., McLay, L.A. 2013. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model. Health Care Management Science 16(1), 14 – 26.

The third paper studies staffing levels during a blizzard, where a surge of calls can temporarily overwhelm resources that are available. Additional staff are usually scheduled during emergencies when call volumes increase. We specifically focus on snow events, and the results have insight into other situations. To determine staffing levels that depend on weather, we propose a data-driven model that uses a discrete event simulation of a reliability model to identify minimum staffing levels that provide timely patient care,with regression used to provide the input parameters. We consider different response options, including asking low priority patients to wait for service, and we take into account that service providers often work faster when systems are congested. The latter issue of allowing adaptive service rates is important, since it makes the model more realistic by limiting the assumption that service rates are constant. A key observation is that when it is snowing, intrinsic system adaptation with respect to service rates has similar effects on system reliability as having one additional ambulance.

Yoon, S., Albert, L. 2018. An Expected Coverage Model with a Cutoff Priority Queue. Health Care Management Science 21(4), 517 – 533.

The final paper examines how to locate and dispatch ambulances when resources can be temporarily overwhelmed. In this paper, there are prioritized calls for service in a congested system, but the system is not completely overwhelmed by an MCI such as a hospital evacuation. Typically, models in the literature implicitly assume that there are always enough resources to respond immediately to all calls for service that are received. This is not a good assumption when there is an MCI. As a result, we need new models and analyses to provide insights into how to allocate resources when there is congestion and many service providers are busy treating patients.

We formulate new models to characterize policies when ambulances are held in reserve for high priority calls. When the system is so congested that it hits the “reserve” stock of ambulances, low priority patients are either diverted to neighboring EMS systems through mutual aid or added to a queue and responded to when the congestion has reduced. Interestingly, we find that by adopting such an approach for sending (and not sending) ambulances to patients, this affects where we might want to locate ambulances at stations.