In our last interview, News-Medical spoke to Dr. Mark Penney about his research efforts on the ongoing COVID-19 pandemic and how we can improve COVID-19 vaccinations through research applications from contacts.
What inspired your latest research on COVID-19?
By training, 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 with colleagues at the Perimeter Institute for Theoretical Physics about how we might apply models of physics, especially percolation theory, to understand COVID-19.
We ended up forming an interdisciplinary team of physicists alongside experts in infectious disease modeling and vaccination. Some classic research in percolation theory shows that the rate of spread of an infectious disease increases when the population has more variation in its number of contacts. Our initial motivation was to better understand how these heterogeneities in the modes of human contact impact 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 is certainly the result of collaboration. When we first thought about this project, our co-author Lee Smolin was wise to suggest that if we were to have an impact, we had to work alongside infectious disease experts.
So we teamed up with Chris Bauch, Madhur Anand and Ed Thommes. We also added Yigit Yargic, then Lee’s PhD student, who is the other lead author of the article and with whom I worked closely.
Vaccines are, in a sense, inherently collaborative: they offer protection not only to the person who receives them but, often, also to those who come into contact with them. More concretely, there is certainly a need for cooperation and sharing of resources to achieve equitable access to COVID-19 vaccines in all countries.
Locally, vaccination campaigns can be hampered by a high degree of preferential attachment between unimmunized people, allowing the disease to spread more freely among unimmunized people. Such preferential attachment could be a symptom of division within the community around immunization.
How do COVID-19 contract tracing apps work?
Different countries have implemented different approaches to digital contact tracing. A popular approach uses Bluetooth to create a decentralized and encrypted contact log. The Google / Apple Exposure Notification API and BlueTrace are the two main implementations. The former is used in Canada, Europe, and some US states, while the latter is used in Singapore, New Zealand, and Australia.
The “decentralized contact log” I referred to above is, at least in my opinion, the most exciting part of these contract tracing apps. The basic idea is that whenever two people using the app are close to each other for a while, they exchange crypto tokens to save their contact. On each user’s device, there is a log listing all the contacts they have had, except the entries are encrypted so that it is impossible to tell who that contact was.
When a person who tests positive for COVID-19 chooses to upload it to the app, they send a key that allows all other users of the app to decrypt the tokens they have redeemed. This is how another user’s phone can alert them to the potential exposure.
It is important to note that in the Google / Apple framework, there is no way for public authorities, or anyone, to access the encrypted contact log. These contact tracing systems operate without the central collection of private information.
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Can you describe how you conducted your latest research on COVID-19 vaccines and contact tracing applications?
The main technical challenge of this project was that our model had to depend not only on how much contacts that people had, but also on How long these contacts lasted. This is due to an important subtlety about how contact finder apps record entries in the contact log.
If you spend a lot of time with the same person, apps could potentially exchange tokens multiple times, with new entries being added about every 10 to 15 minutes. Because tokens are encrypted, it is impossible to know whether or not two entries corresponded to contact with two different people or to prolonged or repeated contact with the same person.
The existing literature on applications of percolation theory to infectious diseases had made the simplifying hypothesis that the duration of contact does not affect the probability of transmission. It’s not necessarily a terrible assumption, but given how contact tracing apps work, we needed some time to get in the picture as well.
So one of the things we had to do was incorporate the duration into these models and redefine the key formulas. On top of that, we had to determine how a vaccination program that prioritized individuals based on their number and length of contact impacted the rate of spread.
What did you find out?
A vaccine strategy that prioritizes those individuals most at risk over others has the potential to limit the spread of the disease more effectively. This is because people who have more contact are both more likely to catch the disease and more likely to pass it on to others. This idea has been well explored in the literature and even implemented in practice.
However, its use in the real world is limited by the ability of public health authorities to actually identify who are the people with the most contact. In practice, they generally use coarser demographic factors. For example, in my area during a lockdown, COVID-19 vaccines were prioritized for essential workers who could not work from home due to their increased risk of transmission.
We have proposed a way to exploit existing contact tracing applications to allocate vaccines more efficiently. The decentralized contact diary created by contact tracing applications enables public health strategies, especially vaccinations, to target high-exposure individuals without the need for public authorities to centrally collect information about them. contacts of the population. In our proposal, the app makes a decision based on the number of entries in the contact log to determine whether the app user has priority 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 greatest reduction in disease transmission from a limited supply. We envisioned an idealized scenario in which a person receives a vaccine if and only if they are chosen by the application.
Our modeling has shown that our “hot spot” strategy using contact tracing applications reduces the spread very effectively, resulting in disease suppression with fewer vaccines. Indeed, for our contact network model, collective immunity was obtained with approximately half the doses.
Image Credit: 2021 Penney et al
How would this approach work for developing countries where few people use contact tracing applications?
The fewer people who use the app, the fewer people there are who could even potentially be chosen to be prioritized by the app. Thus, the total reduction in the spread of the disease that could be achieved is limited by the number of people using the app. However, an interesting result of our work is that the Efficiency of the policy is still high even when the number of users is low.
All vaccines assigned to high-exposure people have a relatively greater impact, and so strategies are still able to achieve greater reductions compared to the vaccines they assigned using the hotspot strategy. .
More research should be done before a country can decide if using this technology is the right solution for them. The choice of people to favor for vaccinations has social and health impacts beyond the rate of transmission.
Were there any limits to your study? If so, what were they?
It would be preferable to consider this study as a preliminary modeling of a proposal. As mentioned above, more detailed studies should be carried out to better understand all the impacts. Having said that, there is one important limitation that deserves to be pointed out. We analyzed application-based vaccination strategies in isolation.
In more realistic scenarios, app-based distribution would likely occur alongside other more traditional systems. Indeed, to better understand the impact of the strategy, it should be seen as only part of a holistic approach to immunization.
What’s the next step in your research?
We built a model for a scenario closer to seasonal influenza vaccination. The main driver of vaccination coverage is no longer the supply of vaccines but rather an individual choice to be vaccinated. Contact tracing applications are no longer used to prioritize vaccine access.
Instead, when a user is selected by the app, they receive a notification that they are getting vaccinated based on their high contact habits. Our early results suggest that the hotspot strategy could be a valuable tool in reducing the burden of seasonal influenza, particularly given its low cost of deployment.
Where can readers find more information?
About Dr Mark Penney
Mark Penney is a postdoctoral researcher at the University of Waterloo. He received his PhD in Mathematics from the University of Oxford in 2017 for research at the interface of topology and physics. Prior to coming to the University of Waterloo, Mark spent two years at the Max Planck Institute for Mathematics in Bonn, Germany.