Q&A: Public Health Preparedness: Moving On Without Moving Backward

There is a general hope from the global community that we are entering the end of this pandemic. While hope is important to maintain, the longing for an end has contributed to a decrease in testing infrastructure usage, minimizing the utility of testing and case data as public health surveillance tools. To be prepared for future public health emergencies, we need to maintain quality surveillance data and public health infrastructure. Dr. Crystal Watson, Public Health Lead for the Coronavirus Resource Center, believes we can help protect ourselves from future disasters if we intelligently bolster and utilize the domestic and international disease surveillance systems established through COVID-19 emergency funding measures instead of moving on like nothing ever happened.

Why is testing data becoming less reliable?

First of all, we have widespread availability of at-home antigen testing, which is allowing people to test when they feel like they need to. Possibly they are testing more often now, but probably they are not reporting those results to public health officials who perform disease surveillance. Undetected cases combined with unreported cases tested at home have resulted in many not being recorded officially. The second reason is that we've had less demand for testing because, as we've come down from the Omicron surge, there's less transmission and less infection in communities. Also, some of the capacity that has existed in public health facilities, hospitals, and other testing locations is starting to disappear.

What are some critical warning signals in the data apart from cases?

What we're most concerned about is if hospitalizations rise quickly. However, because that's a lagging indicator I am concerned that we may get deep into a surge before we can respond to it. That's why it's important to keep a system of case surveillance in place to try and keep an eye on the public health landscapes before problems progress to hospitalizations. We're hopeful that the decoupling of hospitalizations from cases will continue to trend in the right direction, but we also need backstops in place. We need a sampling plan that allows us to keep tabs on community health and disease transmission. Maintaining wastewater surveillance and sequencing efforts to detect new variants are also really important.

How can we develop effective, ongoing testing surveillance?

We have to think more creatively about how we do surveillance because we know a lot of people aren't going to seek out testing. We need to think proactively about how we sample and test people in order to have accurate representation of communities’ statuses. Some really great minds in epidemiology and biostatistics have thought this through, and there have been a few examples of strategically planned random sampling to understand what's happening more broadly in the community and extrapolate to larger populations. Hopefully that's something that the CDC is thinking through for the future.

Indiana has already done this as a collaboration between academia and health departments. Working together both to design and implement these sampling, testing, and surveillance strategies is really important. Because we have such a big country, we need to have these conversations at the state or territorial level and at the national level to put surveillance in place. There are a few different approaches, such as sending random people at-home antigen tests and then collecting that data at certain intervals, giving them incentives to participate, and providing good explanations for why we're doing this surveillance. We now know people have the capacity to take the test at home, interpret them, and use them for their decision making, which was a legitimate question because we haven't done much of that previously.

How would continued open data support effective disease surveillance?

I would appreciate open, transparent, and easily digestible data on transmission, incidence, and other aspects of epidemiology that have been valuable over the last two years. One of our colleagues at the Center for Health Security, Dr. Caitlin Rivers, is actually working with the CDC to establish a center for disease forecasting. Her analogy is comparing disease surveillance to the National Weather System where you can look at what's happening in your community on a daily basis to inform your decisions. That's a work in progress, but we can strive for something better than what we have right now, and data accuracy and availability will greatly strengthen those efforts.

How can we quantify public health preparedness?

The Center for Health Security has developed a Global Health Security index, which summarizes the traditional indicators of public health preparedness. Those indicators are all important, but they're not enough to create a successful public health response to a pandemic. On paper, the U.S. was more prepared than much of the rest of the world based on the investments that we made, but the only place we really saw returns was in developing medical countermeasures. We need to pay a little bit more attention to some of the more nebulous aspects of preparedness and resilience, such as cohesion in a community: How do you build trust in public health? How do you have a stable public health system in place that people will interact with more regularly? That capacity building is going to be really important for the next public health crisis, and we need to figure out how best to measure that going forward.

We have a project that has been led by the Bloomberg School of Public Health by Dr. John Links called COPEWELL that predicts community resilience in a disaster or emergency. One thing that's novel about that approach is that it really looks at the baseline functioning of the community. Is there a lot of inequality and deprivation or are people on more even footing? Are they interacting together? Do they have good views of the government? All of these things contribute to a solid foundation for a community. Then you have other factors concerning how far community functioning falls during a disaster and how quickly that community can recover. This type of data will be crucial to assessing community public health preparedness and for suggesting improvements to increase resilience.