If we have low vaccine hesitancy, or we’re very slow and cautious in how we ease back NPIs, that’s where the models send us. In three of the four scenarios, we see cases going down and staying low, deaths going down and staying low, and hospitalizations going down and staying low.
FALL FALL FALL UPON US PLUS
So these teams looked at four different scenarios: high vaccination plus a reasonable amount of NPIs, all the way down to low vaccination and not a lot of mask wearing or other types of restrictions. This is the suite of basically everything else: mask wearing, both mandated and by individual choice restaurant capacity rules and even personal decisions about whether to go out and do activities as before the pandemic occurred. We call them nonpharmaceutical interventions, or NPIs. The other dimension was control measures other than vaccination. In the low-vaccination or high-hesitancy scenario, we assume that just under 70% of the eligible population actually gets vaccinated. So one scenario demand is going to be high-most people who say they might get the vaccine eventually get it, and we get up to around 83% of the eligible population being vaccinated at a national level. While in previous rounds the questions around vaccination were often about supply, now they’re about hesitancy and demand. We looked at a 2-by-2 table of scenarios where one dimension was all about vaccination. But we’re perfectly upfront and clear that changes in policy, changes in how people react, or unexpected new variants of the disease could completely change these, so we don’t claim them to be forecasts. We specify some conditions about how things might unfold based on our best understanding of the world now, and then, under multiple scenarios where things unfold in slightly different ways, project how the epidemic might unfold. And then we get into the realm of what I call planning scenarios. The COVID-19 Forecast Hub, another multi-modeling effort, realizes that and limits their forecasts to four weeks into the future.īut people who are planning for, say, how long we will need to support people who are put out of work by the pandemic, or what the impacts might be of lots of vaccination hesitancy-they need to know how things might unfold in longer timeframes of three months, six months, or maybe even longer. For a lot of reasons, we can only do that for a little bit into the future, kind of like in weather forecasting, where you can have a decent 10-day or maybe 14-day forecast, but you really can’t go much further than that. In forecasting, we’re really trying to say what will happen. What is the difference between scenario modeling and forecasting, and how that is applicable to this model that you build for the future? This is how we do weather forecasting, and we think it’s the right way to do infectious diseases as well. We wanted to bring in teams with different perspectives and different assumptions to try to get the best sense across the scientific community of how things might proceed in different conditions. This is a multi-modeling effort we call the COVID-19 Scenario Modeling Hub. You’ve spent a lot of your time in the last year working on models to understand the spread of the virus, and you have recently worked with a group of modelers to look to the future from where we are today. In this Q&A, adapted from the June 4 episode of Public Health On Call, Lessler discusses several different scenarios-and the troubling element that the models didn’t consider.
Justin Lessler, an infectious disease epidemiologist at the Johns Hopkins Bloomberg School of Public Health, worked with several teams of modelers to explore possibilities based on different vaccination rates and levels of other controls, such as mask wearing and distancing. Last fall and winter saw a surge in COVID-19 cases in the U.S.
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