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Inside Precision Medicine speaks with Kaitlyn Johnson, PhD, senior data analyst at the Rockefeller Foundation’s Pandemic Prevention Institute (PPI), where her work leverages data analysis and modeling to provide real-time guidance individuals and decision makers to prevent and mitigate pandemics. Johnson is an interdisciplinary researcher passionate about developing quantitative solutions to improve public health and medicine.

Johnson completed his doctorate in biomedical engineering at the University of Texas at Austin in April 2020, just as the COVID-19 pandemic was taking off. During her graduate studies, she worked in a systems biology laboratory which developed genomics-based tools to better understand the treatment of cancer cells. Specifically, they have developed technologies to track cancer cell lines, developing links between genotype and phenotype.

Johnson worked on the data analysis side by integrating results from sequencing data with longitudinal data of cancer cell populations over time to understand how they responded to drugs and developed resistance to chemotherapy.

She began working at the UT COVID-19 Modeling Consortium led by Lauren Ancel Meyers, PhD, for her postdoc, using models of infectious disease dynamics helping to provide situational awareness and scenario-based projections for help guide the pandemic response for UT and the City of Austin.

She joined PPI last year to build on her previous work and develop it into tools so that others, outside of close associates, can benefit from it. Her goal was to combine science with the product and technology side of the world – to create tools from some of the academic science she was immersed in.

We asked Johnson about his work, the PPI, COVID-19 and future pandemics.

This interview has been edited for clarity and length.

The best : I know your work revolves around data and decision making. Can you explain it in a bit more detail?

Kaitlyn Johnson
Kaitlyn Johnson, Ph.D.
Senior Data Analyst
Rockefeller Foundation Institute for Pandemic Prevention

Johnson: Let me start by saying that we don’t make policy. What we do is analysis to inform policy. That said, we always consider politics in our work.

When I conceptualize a data-to-action pipeline, I think of this question in terms of three different compartments.

The first bucket is to “launch now” or understand the current state. What state are we in and what are we dealing with? In the example of a new emerging pathogen, this includes questions such as, what is the basic reproduction number (Rº) of this pathogen? How effective is vaccination? What are these properties that help us to be able to answer political questions? We need a baseline to dive into the effects of policy on the epidemic context.

The second bucket is forecasting. Based on current trends, what do we think will happen? In pandemic prediction, it is difficult because it is affected by human behavior and it is difficult to predict human behavior. So, predictions tend to only happen in the next two to three weeks – what do we think will happen based on what the data is telling us?

The final bucket, which relates more to information policy, is the decision-making part of our analysis. This is where we could make scenario-based projections to assess the effect of different policies, where we try to mathematize what a policy is. An example would be to analyze different vaccine allocation strategies based on age, geography or other factors – and the effects of the speed and timing of these deployments on health outcomes. Another example, and something that was done at PPI, was to analyze the difference between requiring rapid COVID-19 tests or COVID-19 vaccines at an event, to see what mitigation measures, or combination measures, are the best to prevent event attendees from arriving infected.

Again, we’re not saying this is what you should do. We say, here is the evidence for you to decide.

The best : How is your work communicated so that it can be implemented? How do you bridge this gap?

Johnson: In some cases, after building a tool, we write an accompanying blog post. In there, we can clarify what we recommend based on the analysis. Like writing a scientific paper…results are separate from interpretation. Both parties can allow the public to have the evidence base to make an informed decision.

We believe that having quantitative evidence can help someone point to this tool when talking to friends and family. They can tell, that’s what the scan shows and because of that, I’m going to ask you to take a test before you come to my holiday dinner.

The best : How does the PPI communicate your results?

Johnson: This is one of the main challenges we face as a broader public health community. We need to make these tools so useful that everyone wants and wants to use them. There is always a tension between the complexity of the scientific message and the need for something that is easily communicated and interpretable.

One of the analogies we think of is the weather app. People use it daily; it helps guide their day-to-day decision-making. Because people are so dependent on weather forecasts, it encourages the collection and submission of data to the system. The idea that we have, as an Institute, is to have this type of desire for these tools both among the public and among decision-makers. And that requires us to work with them, answer the questions that matter most to them, and present it all in a user-friendly way.

We think a lot about how to make our results publicly interpretable so that they are more easily accessible and how we can make them more widely available to people around the world. It will have more impact with a wider reach.

The best : How do you feel when the evidence-based actions you recommend are not implemented or ignored altogether?

Johnson: What motivates me and drives me forward is science.

The challenge is how we can meet the person or community we are trying to serve where they are, and how to determine the key issues of interest to them. Instead of sitting at our desks and assuming we know what people need.

For example, I worked a lot in my postdoc supporting university policies. We found that the university had different concerns outside of the areas from a pure health perspective. We focused on the level of infection while they were more interested in absenteeism, quarantine time, and testing costs that we hadn’t initially considered. We therefore included them in our analysis.

The best : The pandemic changes all the time. How do you deal with constant change?

Johnson: PPI has partnerships that allow us to work directly with people. And we seek to develop tools not only for high-income countries, but also for low- and middle-income countries. We need to figure out what questions they need to answer? What types of tools are they looking for? We have to make sure that the policy options we try to mathematize and demonstrate are relevant to them. For example, emphasizing COVID-19 testing is not helpful if the tests are not readily available. So we need to think about other policy options that we can consider integrating. And when their needs and questions change, our methods must also adapt to reflect that.

The best : What have you learned over the past year? And what are you thinking about next?

Johnson: We have learned the importance of interdisciplinary collaboration between sectors. That is, take pandemic science from academia, incorporate profitability and economic modeling, bring the communication aspect to the fore, add user center design, and then turn it all into something that’s ready for production and scalable.

Since SARS-CoV-2 is a new virus, these tools were not in place at the start of the pandemic. What we’re trying to do is put in place these systems to circulate currently

pathogens and for new pathogens. We want to be able to transmit new data, answer these questions quickly, and then communicate quickly.

In general, the scientific community struggles to communicate uncertainty. So we need to be able to say that’s what we think might happen, based on those sets of assumptions, and with a lot of uncertainty about how it might play out. Because all of this is dynamic and constantly changing. Being adaptable, while being clear about your message, is important.

The best : Are you planning a break after COVID-19? Or are you bracing for one new emerging pathogen after another in the future?

Johnson: There are so many other pathogens, including endemic viruses, that could use better response tools. We hope to take advantage of what is currently circulating to be able to react and detect new pathogens quickly as well.

This is part of the work we have done in the area of ​​wastewater monitoring. These are multi-pathogen tests so we can get a better idea of ​​the baseline levels of these pathogens circulating in wastewater and easily modify them for new or re-emerging pathogens.

The best : What keeps you up at night?

Johnson: The effect of climate change and its interaction with zoonotic fallout. In the past, we had a major respiratory pandemic every hundred years. But will this pace pick up? And are we going to start seeing more new pathogens emerging due to changes in the environment that cause species to move to different regions?

The best : I just read a natural paper exactly on that. He is intitulated, “Climate change increases the risk of cross-species viral transmission.”

Johnson: One of the other data analysts on our team worked closely with this first author (Carlson et al.). We thought about it a lot. What keeps me awake at night is how we are going to handle this evolution as a society.

Julianna LeMieux has been a science writer at GEN/IPM for four years, where she covers synthetic biology, genomics, infectious diseases, genome editing, and more. Previously, she spent years training on the bench while studying pathogenic bacteria during her PhD and postdoctoral work. She has a passion for explaining complex scientific concepts to a wide variety of audiences.