KIMBERLY POWERS: How do we predict the future of COVID-19?
Monday, April 13, 2020 -- Without strenuous intervention efforts, COVID-19 sickens more patients than health care systems can handle, resulting in even more deaths than would be expected if ample ICU beds awaited those becoming ill. As COVID-19 epidemics have sprouted up, overwhelming health care systems, it has become apparent that aggressive measures are needed to limit the impact of this novel threat. But how do we know what those measures should be, and how do we predict their impact? Fortunately, people have been studying epidemics for many years, and we know how to approach questions like these.Posted — Updated
In the few short months that COVID-19 has been with us, we have learned some important things about it. We learned early on that SARS-CoV-2, the virus causing COVID-19, is lethal and highly contagious. It quickly became clear that without strenuous intervention efforts, the virus sickens more patients than health care systems can handle, resulting in even more deaths than would be expected if ample ICU beds awaited those becoming ill. As COVID-19 epidemics have sprouted up around the globe, overwhelming health care systems along the way, it has become abundantly apparent that aggressive measures are needed to limit the impact of this novel threat.
But how do we know what those measures should be, and how do we predict their impact? Fortunately, people have been studying epidemics for many years, and we know how to approach questions like these. We know, for example, that a key driver of infection transmission is the rate at which people come into contact with each other. In the case of a novel pathogen for which we do not have a vaccine – that is, in the case of a virus like SARS-CoV-2 – reducing this contact rate is our most powerful weapon for slowing the spread of infection. It is for this reason that “social distancing” has been the focus of mitigation efforts to date.
One tool that we use to analyze and project infection spread is a mathematical model, which is a set of equations and numbers summarizing what we know about an infection and how it is transmitted. Because the rate at which humans contact each other is a key driver of transmission, this contact rate is a key ingredient in our epidemic models. We can plug different values of this contact rate into our equations to ask what the impact of different public health strategies might be.
For example, we can ask questions like, “what if we all contacted each other 50% less frequently because we spent more time working from home and less time crowding into venues?” And then, “what would happen if we went back to normal life after four weeks of social distancing? Ten weeks?”
These are the sorts of questions we all want answered. And while we know what kinds of equations we need to answer these questions, there are many uncertainties about the numbers going into those equations. For example, it is not entirely clear how infectious the virus might be before symptoms appear, or how transmission intensity might differ between children and adults. Other uncertainties arise from incomplete testing coverage, which limits our ability to know how many cases of infection truly exist.
As we gain more experience with this virus and testing continues to improve, so will our understanding of the transmission dynamics. Even with all of the uncertainties, however, we know that reducing contact rates is the only method we currently have to limit the impact of this devastating infection, and our models indicate that strong distancing measures can substantially slow its spread.
Some oft-repeated analogies work fairly well here. One is that epidemiologists are like meteorologists, trying to predict the impact of an oncoming hurricane. In the weather analogy, forecasting models may tell us that a category 5 storm is threatening our coast, but it is difficult to pinpoint exactly when or where the hurricane will make landfall, or exactly how much damage it will ultimately cause. As with an oncoming hurricane, the better prepared we are to confront SARS-CoV-2, the safer we will be.
Another relevant analogy is that we are building an airplane while we are flying it. In the case of SARS-CoV-2, we have used our long experience building airplanes (studying disease) to assemble new flying machines (mathematical models) to help deliver us to safety. We are currently climbing toward cruising altitude in our rapidly assembled planes. As we amass more knowledge about this novel coronavirus, we will continue to add important features that will help us to descend slowly and avoid the path of a category 5 hurricane. In the meantime, the more we can slow the spread of SARS-CoV-2, the better off we will be.
Distancing measures are unquestionably painful and difficult, and they cannot be sustained forever. However, by committing to these efforts in the short term, we buy ourselves valuable time to improve our flying and forecasting machines, build the health care capacity we need to help those falling ill, and plot our course toward a safer landing.