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Rede: Emma Hodcroft

How many of you have had influenza this year, or know someone who has? It’s just part of winter, right? The temperatures drop, we start spending more time indoors – and soon enough we're hearing about this year's influenza wave. We’re told about how vulnerable people should get an updated vaccine. We hear about case counts – how bad the season might be. Maybe it's easier this year, maybe it might be a little tougher. If we're expecting more cases, then we might hear about how hospitals are preparing, or that we should avoid unnecessary trips to the emergency room.

This information becomes the background noise for the pre-Christmas preparations, the normal return of the January coughs and the February colds. We take for granted that we have this much information about influenza – that we can track it, can make new vaccines, tell the public about how the season may go, and prepare hospitals and clinics, if we’re expecting a particularly bad season. But actually, what we have for influenza is undeniably unique. We have a system where scientists all around the world, all through the year, collect samples, sequence them and share them – on a global scale – so that we can understand how influenza changes from week to week from month to month from season to season and we can respond appropriately.

Particularly since influenza generally moves from the tropics to the north and south hemispheres in our respective winters – having this global data is invaluable to knowing what to expect. Without this, we’d be driving blind: Flu samples from Europe’s own previous season would be sorely inadequate to predict what comes next winter. We’d lack critical information for updating our vaccines, and for predicting how severe a season may be and the preparation that allows. And information is what allows a speedy response when the flu season starts – and speed matters: Sharing data saves time, and saving time saves lives.

How important was sharing during COVID?

What may surprise you is that this really is unique for influenza: We do not have a system like this in place for any other virus – not even in the pandemic. Thankfully, in the pandemic data was shared openly, which was critical for response. That’s what allowed us to develop vaccines, understand new variants, and develop treatments. It allowed public science communication, and even citizen scientists to contribute to the pandemic effort in identifying new mutations and variants. It allowed anyone, anywhere, to work on the pandemic at the same time!

This meant that instead of sequential publications and manuscripts building on each others’ work only after the previous work was complete, that scientists could instead communicate, analyze, and update predictions and information in parallel – in near-real time. Normally – often – science is careful, and slow. But during COVID, we chose speed and transparency. This didn’t lower the quality of the work that was done – in fact because so many could combine efforts on the same problem, this meant that our efforts were multiplied.

But all of this was more due to chance rather than by design – there is no rule that says viral genetic data like this must be shared openly or widely. Instead, early scientists set an example, a precedent, which thankfully was followed by others – but this doesn’t always happen, and may not be what happens in the next epidemic or outbreak.

What is the data being shared?

Before we get to why data sharing doesn’t always happen, it’s worth taking a step back and asking a more basic question: What kind of data do we mean, when we talk about sharing data for infectious diseases and public health?

You will be familiar with some of it already. You will remember hearing about case counts – how many people are getting sick, whether the numbers are rising or falling. We hear about hospitalisations, or whether intensive care units are under pressure. We can also learn from the experiences of other countries: In the pandemic we looked to other countries to understand what happened when travel restrictions were lifted, or when a new treatment was introduced. More recently, we’ve looked to the UK to understand this year’s flu season and the impact of a new variant. All of this information helps governments, hospitals, and individuals make decisions about how to respond.

But there is another kind of data that is less visible, and yet absolutely central to how we understand and respond to viral disease – and that is genetic data from viruses themselves. Viruses change constantly. Every time a virus infects someone and replicates, small changes can occur in its genetic material – it’s «DNA». We can inspect that genetic material by «sequencing» – extracting that material and visualizing it as letters – a code. By sequencing viruses from different people, in different places, over time, we can see how a virus is evolving, changing, and spreading.

This allows us to answer questions that case counts, and hospitalizations, alone cannot. For example: Are infections in one country linked to infections somewhere else? Is a surge in cases due to increased transmission, or because a new variant has emerged? Are changes appearing that might affect how well vaccines or treatments work? These are questions that comparing genetic data from different samples can help answer – often faster, and earlier than traditional surveillance methods.

Importantly, this kind of data doesn’t tell about an individual person – it’s only the sequence from a virus, not a human. It tells us about that virus – how it moves through populations, how it changes, and how it responds to the pressures, and changing circumstances. When shared quickly and widely, this information allows scientists and public health teams around the world to work from the same picture of reality, rather than trying to piece it together in isolation.

This is why genetic data became so powerful during the pandemic. It didn’t replace case counts or clinical data – it complemented them. Together, these different kinds of data form the foundation of an effective public health response. But it doesn’t work alone – having only samples from Switzerland, for example is useful – it can tell us some things are happening here – but it won’t warn us about a new variant elsewhere that’s more infectious, or resists a treatment. Which is why it’s so critical this data is shared quickly and openly around the world: Because it’s real worth is if the data is a part of a bigger picture.

Data sharing – why doesn’t it happen?

If we know sharing is critical for timely response – for saving lives – then why doesn’t it just happen? Why don’t people just share it? But you and I know data sharing isn’t easy, from our personal lives! It’s something on many of our minds at the moment – what data to share, who to share it with, and how it’s shared – this matters! And causes – rightfully – hesitation in how we might choose to share. The same is true for viral data – what to share, when to share it, and what the drawbacks and benefits are matter! And influence whether that data is shared – and whether it’s shared quickly.

Why sharing is hard: effort required

One of the main reasons that data isn’t shared is technical difficulty. Imagine a doctor working during a surge in the pandemic. Several colleagues are off sick – the wards are full – the emergency room is backed up. Every bed matters, every decision matters, and every minute is accounted for. Between patients, the doctor sits down at her desk – the most recent tests have come back – including sequences of the new variant. She’d like to share them – ensure that others can inspect them for any important changes. She has a few minutes before she needs to get back to the emergency room.

She opens the file with the new sequences, and tries to upload it – file rejected. She tries again – modifying the sample dates a little – was it year month day, or day month year? File rejected. One more time – same result. It’s no use – she has to get back to work. Frustrated, she shuts the laptop and forgets the file.

When there's an emergency situation happening – resources are limited, time is at a premium, and pressure is high. If sharing data is difficult – if it takes too much time and effort, it simply won’t happen. Running tests and treating patients will outweigh formatting files and uploading data.

Sharing doesn’t fail here because people don’t care – but because they’re overwhelmed. In an emergency, everything feels urgent – but not everything is possible. Even outside of pandemics and outbreaks – we have to acknowledge that time is always a limited resource – often the most limited one. Sharing data has to be easy, and it has to be straightforward.

Why sharing is hard: getting credit

Another reason data doesn’t get shared is credit.

In the world of research and science, papers – publications – are what really count. This is how we convince funders to fund our projects. It's how we get jobs at universities, hospitals, or research institutions. What we publish and how that's received is the bedrock of our career.

Unfortunately this creates a system of conflicting incentives – sharing virus data quickly and openly is what’s right, and what’s best for the greater good – but if you share data, someone else could publish on it and get credit for your work – in turn reducing your ability to keep your job, and get more funding.

Imagine being a researcher who’s managed to convince a funder – or the government – to give you money to do this kind of sequencing – to help uncover critical information about how a virus is spreading, and changing – perhaps something that could cut cases or help develop vaccines. Responsibly – you share your data as soon as it comes in – because you know, it could help someone else too. But you open your email one morning to find out – someone else has taken it all, and published a paper about the situation in your country – all without even speaking to you. How will you explain this to your funders, your government? How will you convince them to give you money again, when you didn’t even get the publication? And is there any chance you’ll share next time? Probably not!

We may not like this system, but we cannot ignore it if it prevents sharing. This is how people keep their jobs – we can’t hold it against them if endangering that makes them hesitate to share. This credit and acknowledgement to those generating the data is critical if we want to encourage sharing.

Why sharing is hard: getting the benefit$

The last reason is one of the most pernicious: What’s the benefit of sharing?

You can relate to this as well – when we’re asked to share data with big tech companies, we want to know: What do we get back? What’s the benefit? We need to feel we get something in return in order to feel comfortable sharing.

When it comes to viral data, the benefit is global, but that’s not magical – it’s through concrete things: preparedness, treatments, vaccines. But are those «global benefits» globally distributed? Not always. A country might share data that could contribute to a new vaccine –which they then cannot afford to buy for their population. They could share data that helps create a new treatment – which has been bought up by richer countries first, meaning none is available for them. In such a system, why share?

This isn’t about bad intentions or a lack of solidarity. It’s about how rational decisions are made in an unequal system. When resources are limited, countries and institutions have to prioritise what will benefit the people they are responsible for. If sharing data means taking on extra work and extra risk, but the benefits – new vaccines, new treatments, new tools – arrive late, or not at all, then sharing starts to look less like cooperation and more like a one-sided contribution.

Over time, this will shape behaviour. Data is shared more slowly. Or it’s shared only in limited ways. Or it’s held back until protections or assurances are in place. None of this happens because people don’t understand the global good. It happens because experience has taught them that the global good does not always translate into local benefit.

And the consequence of that hesitation isn’t abstract. Delayed or incomplete data means delayed understanding. It means outbreaks that last longer, spread further, and become harder to control – increasing risk everywhere, not just in the places where sharing felt like a bad deal.

In a world where infectious diseases cross borders effortlessly, fairness isn’t just a moral concern. It’s a practical one. If sharing doesn’t help the people who share, it won’t happen fast enough – and when it doesn’t, all of us are less well protected. We have to acknowledge: The incentive for data sharing is greatly reduced when we cannot ensure that the benefits are shared as well.

The principles we need

So how can we build a system that will ensure we’re better prepared as a globe, to handle the diseases currently impacting us – and those that may come to our door tomorrow? What principles – what rules of the road – make sharing the obvious thing to do, rather than a decision to be made?

When resources are limited and time is essential – we must ensure that sharing is easy. Interfaces and databases need to meet scientists and public health workers where they are: They need to be easy-to-use, with simple tools and programs, for those who are less tech-savvy – and also have advanced computer scripts that plug into existing workflows, for institutions that can make sharing a direct part of their existing systems. This ensures that sharing takes minimal extra effort, and can blend seamlessly into daily work – even in a crisis. Sharing should be the easy option – and take seconds, not stamina.

We may not like an academic and scientific system that creates conflicting incentives for publications vs sharing – but at least short term, we can’t change it. So instead, we need to acknowledge the problem – and create protections to ensure that those who generate the data, those who contribute data, are visibly and clearly acknowledged and credited. And protected, to be able to publish first. And though publications are critical in our current system, let’s not be limited by them. Let’s come up with new metrics – ones that recognise the contribution of data sharing. These should reflect that sharing in and of itself is a critical part of a system that allows all of us to build on, and benefit from, that knowledge. Credit needs to travel with the data, and speed should be rewarded!

And we must acknowledge that benefits matter – not just for us personally, Switzerland, or Europe – but for everyone who we want to contribute. Why invest in a system that never pays out? We should not assume that because benefits are global in theory – that the benefits really do go «to all». We need to actively work to improve our global response, so that when data sharing contributes to medical advances, insights and developments – these are accessible and available to everyone. And this isn’t just to encourage sharing, and not just because it’s the right thing to do – but because when a disease lingers anywhere in the world, all of us are less protected from it. Sharing should help the people who share – and that should be everyone.

Because this question of benefit sharing is so central, there is a major global effort underway – right now – to try to find a workable solution. Governments, scientists, public health experts, and companies are all at the table, grappling with how data should be shared, and how benefits from shared data should be defined, delivered, and distributed.

And it’s not an easy problem. Companies that develop treatments and vaccines are understandably cautious about open-ended obligations to share benefits. At the same time, many countries are understandably reluctant to share data without clear and reliable assurances that doing so will benefit their populations. These positions don’t come from bad faith – they come from experience, responsibility, and risk. But if everyone remains fixed in their most extreme positions, we won’t end up with a system that works. We’ll end up with no system at all. Finding a path forward requires compromise, trust, and a willingness to design solutions that are practical, not perfect – but solutions that move us closer to faster, fairer sharing when it matters most.

Finally, it is critical that these discussions are happening – and that they continue – because we need to ensure we’re building these systems now – before we need them. We shouldn't rely on ad-hoc sharing, on hoping that sharing is done right, on assuming that people will share because they’ve done it before. We should develop frameworks that set out everything I’ve touched on – ease of use, guarantee of credit, and more equitable benefits – so that everyone knows what’s expected, and so that our incentives align, to make sharing the expectation, not the exception. We need to ensure these rules exist before we have a crisis.

Call to attention

Every winter, we assume that certain things will be there for us: vaccines, warnings, forecasts, and guidance about risk – just like they have this flu season. They feel routine – part of modern life. But none of those things work without data. And that data only helps us if it moves quickly and freely.

That’s easy to forget, because when it works well, it’s invisible. We don’t notice the sharing that happened months or years earlier – we only notice the outcome: The vaccine that arrived in time. The warning that came early enough. The hospital that was prepared.

And that data doesn’t come from nowhere: It comes from samples taken from ordinary people – like you. It’s generated by scientists and public health workers – like me. And it’s paid for, in large part, with public money. That means this isn’t just a technical issue, or a scientific one – it’s part of our public health infrastructure, just like hospitals, or emergency services, or clean water. And like all infrastructure, it has to be built and maintained before we need it. And just like all infrastructure we build – we should care whether it works when we need it!

I know – most of you can’t personally decide how viral data is shared, and you shouldn’t be expected to. But awareness matters. Expectations matter. Because systems like this don’t appear by accident. Whether data is shared quickly, fairly, and openly depends on what we choose to reward, what we choose to fund, and what we expect our institutions to prioritise – not during a crisis, but long before one begins.

We celebrate breakthroughs – but we rarely celebrate the everyday success of sharing data with the global community for the global good. What protects us next time is not just science, but the choices we make now about how we’re going to share it.

Alle Winterreden 2026 findest du fortlaufend hier zum Nachlesen

• Christian Huser: ««Erhaltung und Förderung des dualen Bildungssystems.», Montag, 12. Januar 2026
• Saphir Ben Dakon: «Wir haben nicht ewig Zeit, für Behinderungen zu sensibilisieren; Zeit, dass wir mit Behinderungen konfrontieren.», Dienstag, 13. Januar 2026
• Philippe Koch: «Die Wohnungskrise ist real, aber nicht alternativlos.», Mittwoch, 14. Januar 2026
• Franz Hohler: «‹Wie geht’s?› fragte die Trauer die Hoffnung. ‹Ich bin etwas traurig›, sagte die Hoffnung. ‹Hoffentlich›, sagte die Trauer.», Donnerstag, 15. Januar 2026
• Anja Derungs: «Wir dürfen uns nicht an die (alltägliche) Gewalt gewöhnen.», Freitag, 16. Januar 2026
• Dr. phil. Yuvviki Dioh: «Theater und Kunst können uns Perspektiven eröffnen, die die Frage nach sozialer Gerechtigkeit nicht vereinfachen, sondern erfahrbar vertiefen.», Dienstag, 20. Januar 2026
• Anita Borer: «Es war einmal eine selbstbestimmte Schweiz…», Mittwoch, 21. Januar 2026
• Maya Tharian: «Was heisst schon Heimat?», Donnerstag, 22. Januar 2026
• Hannan Salamat: «Zukunft wächst dort, wo wir Perspektiven öffnen und Pluralität mutig als demokratische Kraft entfalten.», Freitag, 23. Januar 2026