COMMENTARY

COVID-19 Data Dives: Making the Case for PPE

William P. Hanage, PhD

Disclosures

May 15, 2020

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William P. Hanage, PhD

Having spent a lot of time thinking about the work (including preprints) of others, here's one from me and my colleagues, Joel Miller and Xueting Qiu, that models what happens if COVID-19 gets into the part of healthcare that is treating patients without the virus—the non-COVID cohort. We consider a basic transmission model. The model has two major components: transmission within the broader community, and transmission within a cohort of healthcare workers (HCWs) and patients who are initially not infected with SARS-CoV-2.

Our study was a stochastic simulation of transmission among HCWs and patients. The virus arrives through individuals in either category who do not know that they are infected. We assume that those who develop symptoms or have a positive test are removed. Patients who become positive are moved to the COVID cohort, which we don't model. HCWs are quarantined before returning to work.

The upshot is that, unsurprisingly, in the absence of personal protective equipment (PPE) or testing, our model finds that all HCWs eventually become infected. The red line in Figure 1 shows an early wave in HCWs; the gray line represents that wave in patients.

Figure 1. If you take no action—no testing, no PPE.
Solid blue: susceptible general population; Solid orange: infected general population; Green: susceptible HCW; Red: infected active HCW; Purple: quarantined HCW; Brown: recovered HCW; Pink: susceptible patient; Gray: infected patient

And this is with both patients and healthcare workers using effective PPE. Testing has benefits too but they're not as dramatic (depending on the exact amounts of presymptomatic transmission).

Figure 2. Both patients and HCWs using effective PPE.

What about less effective PPE? Even if you cut the ability of PPE to stop transmission by half, it is still better than doing nothing. This should not be a reason to cut back on PPE. However, it demonstrates the importance of stopping transmission—for the general public, too.

Our data support the need for adequate and appropriate PPE throughout healthcare, and assuming that everyone might be asymptomatic but infectious unless shown otherwise. This is well understood in some places but not in all. And in some of those places, supplies are short.

We do not explicitly look at what happens to the patients who become infected, but we know that comorbidities are associated with worse outcomes. So avoiding infection—especially in these individuals—is important.

There is a lot more available in the preprint that analyzes the effect of different proportions of asymptomatic infections (longer duration matters) and the impact of sub-cohorts (smaller groups can be helpful in limiting potential for onward transmission).

Bill Hanage is an associate professor at the Center for Communicable Disease Dynamics in the Department of Epidemiology at the Harvard T. H. Chan School of Public Health. He specializes in pathogen evolution. Follow him on Twitter.

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