A team of applied science researchers at the University of British Columbia and BC Children's Hospital have developed a systematic feedback method that could help public health authorities in their efforts to contain COVID-19.

"While it's likely that we'll need to keep isolating and physical distancing for several months to come, our strategy would help determine when we can relax physical distancing requirements and when we need to ramp them up again. Ultimately, this would help ensure that all of our efforts to control COVID-19 are not done in vain." —Dr. Guy Dumont 

Guy Dumont
Dr. Guy Dumont, Healthy Starts investigator, BC Children's Hospital; Associate member, Department of Anesthesia, Pharmacology and Therapeutics; Distinguished University Scholar; Professor, Department of Electrical and Computer Engineering, Faculty of Applied Science, UBC
The researchers' proposed methodology — inspired by the work of epidemiologists at Imperial College and others — does not need to rely on accurate predictive models. It uses hospital ICU capacity as a barometer for determining when physical distancing should be tightened up, and when it should be relaxed.

In this Q&A, the team that performed the analysis — Dr. Guy Dumont, Dr. Greg Stewart and Dr. Klaske van Heusden, from the Department of Electrical and Computer Engineering at UBC — explain the rationale behind the method.

Q: You're proposing a feedback-based strategy for fighting COVID-19. Why is this important?

There are signs that Canada is making progress on flattening the curve of new COVID-19 infections, but public health authorities stress that it's critical to keep the momentum going. Canadians need to continue physical distancing — the primary non-pharmaceutical weapon in this fight — over the next several months while awaiting a vaccine.

However, people need to know what our exit strategy is. When can distancing be safely relaxed for our society to continue functioning? Our methodology can enable decision makers to fine-tune the timing, duration and scope of intervention measures like self-isolation and quarantining.

This can help public health officials bring the outbreak under control and manage hospital caseloads as we wait for herd immunity to take effect or for a vaccine to be developed — while at the same time permitting safe relaxing of physical distancing.

Q: What does your feedback-based method look like?

We used the standard SEIR (susceptible, exposed, infected, recovered) epidemic model that public health researchers typically use to predict the spread and impact of an outbreak. We use the number of available hospital ICU beds as the primary measure of health care capacity. The goal is always to bring infectivity rates to manageable levels.

As an example, if hospitals in jurisdiction X are approaching overcapacity, the feedback-designed policy will suggest an increase to the physical distancing in the region. When the healthcare capacity increases, the policy can suggest an optimal time for policymakers to relax or lift these intervention strategies. It's key that many or most interventions have intermediate options that can be leveraged and thus avoid oscillations and repeated outbreaks.

Our approach emphasizes the important role that feedback can play to stabilize the system. Left on its own the epidemic is unstable, i.e., it grows exponentially. If we apply a basic control principle known as feedback stabilization, we can bring and maintain the propagation rate to a level manageable by our healthcare system. We've thus drawn on engineering principles to provide policy suggestions that take into account economic considerations and medical constraints.

Q: How does this add to our current knowledge of the novel coronavirus?

Current epidemiological models of COVID-19 do not have an accurate way of estimating when to relax and when to tighten distancing interventions. An overly aggressive on-off approach may lead to unmanageable swings in health care capacity and the number of new cases. This is, for instance, what happened in St. Louis during the influenza pandemic of 1918.

We believe that by bringing computer feedback to bear on the policymaking process we could have much better health outcomes for everyone concerned. We'd like nothing more than to work with other researchers in Canada or elsewhere to further develop this methodology, and possibly make it more interactive to help educate the public. 

Source: UBC News, March 31, 2020.

For more information, read "How Control Theory Can Help Us Control COVID-19" in IEEE Spectrum