In our most recent interview, News-Medical spoke to Dr. Mark Penney on his research efforts in the ongoing COVID-19 pandemic and how we can improve COVID-19 vaccinations via contact tracking apps.
What inspired your latest research in COVID-19?
By education, I am a mathematical physicist, and before the pandemic, my research focused on the applications of topology to quantum field theory. During our first lockdown, I started talking to some colleagues at the Perimeter Institute of Theoretical Physics about how we could apply models from physics, especially percolation theory, to understand COVID-19.
We ended up forming an interdisciplinary team of physicists along with experts in modeling infectious diseases and vaccination. There is some classical research in percolation theory that shows that the spread of an infectious disease increases when the population has greater variation in their number of contacts. Our first motivation was to better understand how these heterogeneities in human contact patterns affect public health interventions.
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The COVID-19 pandemic has taught us that by working together, scientific progress can be made quickly. How can this level of collaboration be implemented in COVID-19 vaccination programs?
This research was certainly the result of collaboration. When we first thought of this project, our co-author Lee Smolin was wise to suggest that if we wanted any influence, we should work with experts in infectious diseases.
So we went along with Chris Bauch, Madhur Anand and Ed Thommes. We also added Lee’s then PhD student Yigit Yargic, who is the second lead author of the paper and with whom I worked very closely.
Vaccines are somehow inherently cooperative: they provide protection not only for the person receiving it, but often also for those who come in contact with them. More specifically, there is certainly a need for cooperation and resource sharing to achieve fair access to COVID-19 vaccines in all countries.
On a local scale, vaccine campaigns can be inhibited by a strong degree of preferential binding among non-immunized people, so that the disease spreads more freely among the non-immunized. Such preference binding may be a symptom of divisions within the community around vaccination.
How do COVID-19 contract tracking apps work?
Different countries have implemented different approaches to digital contact tracking. A popular approach uses Bluetooth to create a decentralized, encrypted contact log. The Google / Apple Exposure Notification API and BlueTrace are the two most important implementations. The former is used in Canada, Europe and some US states, while the latter in Singapore, New Zealand and Australia.
The ‘decentralized contact log’ that I referred to above is at least in my opinion the most exciting part of these contract tracking apps. The basic idea is that when two people using the app are close to each other for some time, they exchange cryptographic tokens to register their contact. On each user’s device, there is a log that shows all the contacts they have had, except the records are encrypted, so it is impossible to tell who the contact was.
When a person who has tested positive for COVID-19 chooses to upload it to the app, he sends out a key that allows all the other app users to decrypt the tokens they had exchanged. This is how another user’s phone can alert them to the potential exposure.
It is important to note that in Google / Apple frameworks there is no way for public authorities or others to access the encrypted contact log. These contact tracking systems work without central collection of private information.
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Can you describe how you conducted your most recent research into COVID-19 vaccines and contact tracking apps?
The biggest technical challenge in this project was that our model should not just depend on how many contacts people had but also on how long these contacts lasted. This is due to an important finesse about how contact tracking apps register records in the contact log.
If you spend a lot of time with the same person, apps may be able to exchange tokens multiple times, with new records being added approximately every 10-15 minutes. Since tokens are encrypted, it is impossible to know whether two entries corresponded to contact with two different persons or an extended or repeated contact with the same person.
The existing literature on the application of percolation theory to infectious diseases had made the simplified assumption that contact duration does not affect the probability of transmission. This is not necessarily a terrible assumption, but given how contact tracking apps work, we also needed time to be in the picture.
So one of the things we had to do was incorporate duration into these models and reread the key formulas. On top of that, we had to determine how a vaccination program that prioritized individuals based on their number and duration of contacts affected the degree of spread.
What did you find out?
A vaccine strategy that prioritizes individuals with more exposure to others has the potential to limit the spread of disease more effectively. This is because people who have multiple contacts are both more likely to catch the disease and more likely to spread it to others. This idea has been well researched in the literature and even implemented in practice.
However, its application in the real world is limited by the ability of public health authorities to actually identify who the multi-contact individuals are. In practice, they typically make use of more rough demographic factors. For example, during a lockdown, COVID-19 vaccines in my region were prioritized for the essential workers who could not work from home due to their increased risk of infection.
We suggested a way to leverage existing contact tracking apps to more efficiently allocate vaccines. The decentralized contact log created using contact tracking apps provides a way for public health strategies, particularly vaccinations, to target high-exposure individuals without the need for public authorities to centrally collect information on population contacts. In our proposal, the app makes a decision based on the number of entries in the contact log, whether the app user is prioritized or not.
In the paper, we modeled a scenario where the demand for vaccines is much higher than the supply, and the goal is to achieve the largest reduction in disease transmission from a limited supply. We considered an idealized scenario where someone receives a vaccine if and only if they are selected by the app.
Our modeling showed that our ‘hotspotting’ strategy using contact tracking apps reduces spread very effectively, resulting in the ability to suppress the disease with fewer vaccines. In fact, for our model contact network, herd immunity was achieved with about half as many doses.
Image credit: 2021 Penney et al
How would this approach work for developing countries, where few people use contacts tracking apps?
The fewer people who use the app, the fewer people who can even potentially be selected to be prioritized by the app. So the total reduction in disease spread that could be achieved is limited by how many people use the app. An interesting result of our work, however, was that efficiency of the strategy is still high even when the number of users is low.
All vaccines assigned to people with high exposure have a relatively greater impact, and therefore the strategies are still able to achieve greater reductions from the vaccines they assigned using the hotspotting strategy.
More research needs to be done before any country can decide if the use of this technology is the right solution for them. The choice of who should prioritize vaccinations has social and health consequences beyond the rate of transmission.
Were there any restrictions on your study? If so, what were they?
It would be best to think of this study as a preliminary modeling of a proposal. As mentioned above, more detailed studies needed to be done to better understand the full effects. That said, there is one important limitation that is worth pointing out. We analyzed the app-based vaccination strategies in isolation.
In more realistic scenarios, the app-based distribution would likely occur alongside other, more traditional systems. To more fully understand the impact of the strategy, one needs to consider it as only part of a holistic approach to vaccination.
What’s next for your research?
We have built a model for a scenario more like seasonal flu vaccination. The main driving force for vaccine coverage is no longer the delivery of vaccines, but rather an individual choice to receive the shot. Contact tracking apps no longer serve to prioritize access to the vaccine.
When a user is instead selected by the app, they instead receive a notification that they are being vaccinated based on their high contact patterns. Our early results suggest that the hot-spotting strategy can be a valuable tool in reducing the burden of seasonal flu, especially given its low implementation costs.
Where can readers find more information?
About Dr. Mark Penney
Mark Penney is a postdoctoral fellow at the University of Waterloo. He received his DPhil in Mathematics from the University of Oxford in 2017 for research within the interface between topology and physics. Prior to joining the University of Waterloo, Mark spent two years at the Max Planck Institute for Mathematics in Bonn, Germany.