ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: SOFI: Sequence-based, cross-sectoral, One-health surveillance of food-borne infections | IMMEM XIV

Hello, my name is Kaspar Rømer-Willemsen and I'm with the SSI in Copenhagen, Denmark.

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Hello, my name is Kaspar Rømer-Willemsen and I'm with the SSI in Copenhagen, Denmark.

I'm here to tell you about the SOFI,

a platform for sequence-based cross-sectoral,

one-health-based surveillance of foodborne infections in Denmark.

But before I get started,

I just want to thank the organizers of the MM conference

for organizing a fantastic conference in an absolutely amazing location in Porto.

Thank you so much.

I also want to thank all the co-authors that are on this poster

because essentially they were the ones involved in the design and the implementation of the SOFI platform.

They're the ones that did the bulk of the work.

I'm just basically allowed to come here and talk about it.

So I'm very, very grateful.

Thank you.

So if we look at how surveillance

or at least how investigation of outbreaks work in the Danish system in terms of foodborne bacterial infections,

if we look at how that works before the implementation of SOFI,

well, basically you have two different sectors.

So we have three different actors here.

We have the National Food Institute at DTU.

There's the Danish Veterinary and Food Administration.

And then there's us at the SSI.

So constituting basically the public health side.

And then there's also a food and veterinary side.

So the way things would work before the implementation of SOFI was that you would have these two different silos.

So there would be strong collaborations across.

We have a central outbreak management group.

They meet every week, basically just coordinating whatever comes in.

Are there any signals?

Are there any ongoing outbreaks? do we need to coordinate any efforts?

But essentially the way that everything was analyzed was kind of siloed

so that you would have these two sectoral silos where you would have samples coming in,

DNA extracted, sequencing done, all the different analysis would then be run within each silo.

If there's any suspicion of anything that might go across these two silos,

then you would have ad hoc preparations for sharing of sequences

so that you can do comparisons in a collaborative manner across from that.

So what has actually changed now is that now we have this the SOFI platform.

So certain things are the same

so basically samples are still coming in to the respective institution

public health side everything in terms of samples derived from patients come to us at SSI.

We run the samples we do the sequencing

but once the sequencing is instead of running everything in a

continued siloed approach now everything is coming into this sophie platform

so what is sophie

so basically sophie is um at one on the one hand it's a data sharing platform

so that basically means

that prior to everything um you know everything had to be

sorted out in terms of all the different legal aspects we're

mixing like personal sensitive information sensitive information about food producers and stuff like that.

So everything has to be completely sealed down in a legal manner,

but also having to make sure that once we enter into this SOFI data sharing aspect,

we need to make sure that it's very,

very clear who has access to what,

what is shared, what is common,

what is open to common analysis,

and what is absolutely not shared,

what has to be completely cordoned off to each of the respective partner institutions.

So, but from surveillance, or outbreak investigation standpoint,

things are now coming into the same pipeline instead of having this siloed approach,

it's the same pipeline.

So now everything is being analyzed according to different analysis modules.

So, and that ranges from initial quantity control,

but also to all sorts of species specific modules.

So like species specific toxin detection,

virulence factors, stuff like that.

But most importantly for this,

actually also there's a CGMLST component.

So once all this is done,

now we end up with everything ending up in the same user interface down here.

So what that means is that instead of having a siloed approach where ad hoc,

we would have to make sure that we share relevant data

and we like someone had to have the suspicion that there might be something going on,

everything is now available in the same interface So that if I'm doing this like just routine surveillance,

I actually have the access not only to every data point from the public health side,

but also from the food and veterinary side.

That means that we now have an even closer collaboration than we did before.

And it also means that we have the possibility to be much quicker about cluster detection,

outbreak investigation, and consequently quicker to implement any countermeasures.

So all in all, a win-win-win situation.

That was a very, very quick flydown.

Check out the poster in the online collection from the conference.

If there's any questions, absolutely feel free to contact us.

Thank you for your attention.

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