December 7, 2023 • 27 mins
Dr. Sebastian Meller explores the potential, and challenges, of using dogs’ olfactory capability to detect COVID-19 infection in humans. "Canine olfactory detection of SARS-CoV-2-infected humans—a systematic review” is published in the September 2023 issue (Vol. 85) of Annals of Epidemiology.
Read the full article here:
https://www.sciencedirect.com/science/article/pii/S104727972300087X
Episode Credits:
- Executive Producer: Sabrina Debas
- Technical Producer: Paula Burrows
- Annals of Epidemiology is published by Elsevier.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Patrick Sullivan (00:12):
Hello, you're listening to EPITalk
Paper, a monthly podcast fromthe Annals of Epidemiology.
I'm Patrick Sullivan,Editor-in-Chief of the journal,
and in this series we take youbehind the scenes of some of the
latest epidemiologic researchfeatured in our journal.
Today, we're talking with Dr.
(00:34):
Sebastian Meller about hisarticle "Canine olfactory
detection of SARS-CoV-2-infected humans-- a systematic
review.
" You can find the full articleonline in the November 2023
issue of the journal at www.
annalsofepidemiologyorg.
Dr.
Sebastian Meller is apostdoctoral researcher at the
Department of Small AnimalMedicine and Surgery at the
(00:56):
University of VeterinaryMedicine, Hanover, Germany.
His research focuses onneuroscience, epileptology and
behavior in the field ofclinical veterinary research and
translational medicine.
Dr.
Meller, thank you so much forjoining us today.
Yeah, thank you for having me.
So you published thismeta-analysis that pulls
together a theme of sort of OneHealth, which is where
(01:18):
veterinary medicine and humanpopulation health come together,
and also SARS-CoV-2, which isstill a really present issue in
our public health.
Can you talk a little bit aboutthe purpose of the study that
you did?
What question were you tryingto answer?
Sebastian Meller (01:33):
Yeah, so we had the first study or first
systematic review, to bringtogether the many claims that
were out there in the researchcommunity that dogs might detect
the coronavirus, and so thatwas quite new.
Of course it emerged right inthe pandemic.
I think that's logical, and sowe wanted to see if the dog is
(01:56):
really able to function as adiagnostic or as a screening
tool and to see how a dogperforms versus like established
methods, like PCR techniques,which we considered the gold
standard.
And of course we had somecriteria to exclude and include
the studies, and we were alsolooking at that.
(02:17):
There are enough, let's say,samples provided so positive,
PCR positive and PCR negativesamples, and then we tried to
make a story out of it.
Patrick Sullivan (02:27):
Great.
So what was the main finding orthe key answer to your question
, which is is there evidencethat dogs do sort of a good job,
or a sufficient job, atdetecting SARS-CoV-2 infections?
Sebastian Meller (02:38):
Yeah, so, indeed, dogs are able to detect
coronavirus.
That was like the main finding,but it is very important to
mention that the studies and thestudy designs and the
approaches were quite different,and I think this comes from the
fact that we were quite in thebeginning of the pandemic and a
lot of research groups were justlooking for something which
(03:01):
could help quickly to somehowcontrol the pandemics, and so
there were a lot of workinggroups worldwide that suddenly
came up with that idea and, yes,so in the end that research, or
my research, showed that, yeah,indeed, the dog is a quite good
(03:21):
screening or test system.
So we found that 80% of studiesshowed higher sensitivity than
80% and 90% of studies showed aspecificity of higher than 90%
and even low biased studies.
So we had some low biasedstudies.
They showed that, yeah, theyshowed a good performance of
(03:43):
over 80% sensitivity and 90%specificity too.
So that was quite nice to seethat even the unbiased studies
showed a quite good performancefor the dogs.
Yeah, but we also realized that, as I mentioned in the
beginning, we had some issueshow to standardize right, or we
saw that a lot of studies had alot of standardization issues.
(04:04):
So that's also one importantfinding we need more
standardization andcertification processes for dogs
to detect medical diseases.
Patrick Sullivan (04:13):
Two important issues that sort of came up in
the article were one about biasin studies and which studies
were more or less biased.
How do you operationalize that?
How do you decide, when you'redoing this analysis, the bias of
the studies and why is thatimportant?
Sebastian Meller (04:29):
Yeah, so we used one tool that was the
QUADAS-2 tool, which is createdfor systematic reviews and is
created for the assessment ofdiagnostic tests.
So that was one part that weused.
So we looked at dogs as adiagnostic test in this respect.
But of course dogs are not onlydiagnostic tests, they are also
(04:52):
living beings.
That's why we also use anothersystem, but we can talk about
that maybe later.
But what in the beginning weused was the QUADAS-2 tool,
which had us to see if studiesare biased or not.
So we were looking there at howwas the patient selection, how
was the index test, that means,the dog, how it was used or how
(05:12):
dogs were deployed.
We were looking at thereference standard, which was
the PCR test, and also at theflow and timing domain.
We also had a look concerning amore temporal dimension,
concerning when tests are shownto dogs after they have been
taken before from the people.
So then we looked at the biasand also the applicability and
(05:33):
we used this semi-quantitativetool to do the bias assessment
in first place.
Patrick Sullivan (05:38):
So you have a set of things about the way the
experiments were done or mightbe done that you think are
important going in, and some ofthose will favor less bias
studies and some will favor morebias studies.
Yes, exactly.
And then you have just yourself, or multiple people, assessing
the individual articles?
Sebastian Meller (05:56):
Yes, I did the main work actually, so I
assessed the articles, but Ialso had a colleague who did the
cross check.
Excellent, If we used theQUADAS-2 tool correctly and we
had a high degree of correlationbetween our results.
So we knew that the tool workedactually and we asked the right
questions.
Patrick Sullivan (06:13):
I also just have to note that you get an
award for, I think, the firstarticle in Annals of
Epidemiology ever to have smileyfaces and frowny faces in your
table.
Sebastian Meller (06:23):
So yeah, yeah, so that was quite
unconventional.
Patrick Sullivan (06:30):
Unconventional , but it gives a little color to
the article.
Sebastian Meller (06:34):
Yeah, it lives a little bit color exactly, and
it was also in the beginningfor me it was quite weird to use
smiley faces because that's notthe thing you use normally, but
on the other side it was like afresh new thing to use smiley
faces.
Patrick Sullivan (06:50):
Yeah, and I mean it conveys what you want to
convey, so that's good.
Sebastian Meller (06:54):
Exactly, and that's a point that's a
semi-quantitative way ofassessing and that's a good way
to assess semi-quantitative data, which actually, for systematic
review, is the right scale,let's say, to use for assessment
.
Yeah, so we also used anothertool, but we can come to that in
an instance.
Patrick Sullivan (07:14):
Yeah, we can go and talk about, because you
did use two approaches.
One was this QUADAS- 2 tool,but then there was also kind of
a quantitative system that youuse.
Can you say a little bit moreabout that system and what it
was used for?
Sebastian Meller (07:28):
Yes, first of all, we wanted to do a
systematic review.
So it was important for us tohave a quality feature for the
systematic review, which is, asI mentioned before, a
semi-quantitative tool likeQUADAS- 2.
And normally there are someopinions about it and some
rationales about it that youshould not use score systems for
(07:49):
systematic reviews, like scoresthat you can count, let's say.
Well, we had a dilemma becausethen we thought about, ok, what
to do?
Because on the one side, thedog when we use QUADAS- 2, the
dog, we see the dog as adiagnostic system.
But the dog is not only adiagnostic system like other
diagnostic systems, it's alsoliving being.
It's a living being who'sworking with its nose, with its
(08:11):
olfactory tool.
And that's why the other ideawas to use a quantitative tool,
that score system, because thisis a score system that assesses
the quality of sense detectionwork.
And then we decided to do both,to just put both together, to
also stress that dogs are notdevices, dogs are living beings.
(08:34):
We wanted to show thatdichotomy and that's why we use
both tests, even if the scoresystem is not adequate for
systematic reviews.
Anyhow, we thought that it is abig picture of the whole test
system living being dog, andthat's the rationale why we use
both tests.
But of course the score systemshould be considered a add-on to
(08:56):
the actual systematic review.
Diagnostic test qualityassessing tool, QUADAS- 2.
Patrick Sullivan (09:03):
Yeah, and I think that's a really just
interesting point for peoplethat are thinking about.
You know, we often get in thepattern of saying, like well,
the tool that everybody uses,this, but is this or that.
But you really sort of tookinto account the nature of the
intervention or the nature ofthe sort of public health tool
here and exactly, just added asecond.
(09:24):
You think about it astriangulation of all the issues
that are involved.
So, exactly, yeah, so can youtalk about any of the
limitations of your review orwhat you would?
You could think of them asweaknesses or you could think of
them as opportunities to donext steps.
But so what's on answer andwhat do you think some of the
(09:45):
limitations are?
Sebastian Meller (09:46):
Yeah, so one of the limitations I think that
should be mentioned is I am alsoone of the researchers who did
canine olfaction studies inSARS-CoV-2 detection and of
course those studies also fellinto the inclusion criteria.
So that is one of thelimitations, I would say.
(10:07):
But I think it's reallyimportant to say that I anyhow
looked at the guidelines that Imade.
I really stick to theguidelines and of course that's
challenging but I stick to theguidelines.
I know the dog community,olfactory detection community, I
know what groups are doing.
I know that, not everythingwhat is written in the papers.
(10:28):
So of course what is written inthe papers is, of course, the
truth, but of course there are alot of things between the lines
that happen and I know thosethings.
But anyway, I really referredonly to the published
information and I did not addany information that were known
but not published.
So it's really I really wasreally important to me to just
(10:51):
look like, okay, just have rulesand I stick to the rules and I
just worked that through and soI think I did it well.
I think we also had somestatisticians in our team who
also gave the confirmation ofhow I was working, that this was
okay, anyhow, that's one of thelimitations, of course, so I
(11:13):
was also part of it, but it wasimportant also to us to just,
you know, to state, to make astatement, also to assess
scientifically if this is reallygoing into the right direction
or not, the whole thing with thedetection dogs.
Patrick Sullivan (11:29):
I think this is a general issue which is, in
a lot of research communitiestend to have a fairly small
number of investigators and weknow each other, we see each
other at conferences, and so Ithink that the important thing
is acknowledging that that couldbe a source of bias, you know,
and saying I'm going to set upthese tools in very objective
(11:49):
ways, so that that will be mytouch point.
So I really appreciate youraising that issue, because it's
one that we don't really talkabout that often, but especially
in a smaller, more sort offocused kind of research
community, I think.
Sebastian Meller (12:02):
Yeah exactly, so we have quite a small
research community.
That's that's one of the pointsthat are important to stress,
that, yes, so we are also mostinterested in our subjects,
right?
So of course, we've right alsothe we have the knowledge to
know what is really important,what really matters in this
research field.
So that's why, yeah, thosethings happen, yeah, but it's
(12:25):
important to mention.
Yeah, of course.
Patrick Sullivan (12:27):
Yeah, it's important.
It's just important toacknowledge and be systematic.
That's all, yeah.
So are you aware of programsthat are using dogs for COVID
screening currently?
Sebastian Meller (12:36):
So actually, yes, there are some programs, or
there were some programs.
For example, in Dubai they usedogs at the airport, and even it
was not only in a scientificway, so they really use the dogs
as an established tool.
Let's say we also did somethingquite interesting, I think, but
(12:58):
that was a study, but a fieldstudy.
So we did a concert, weorganized concerts here in
Germany with a huge concertcompany and we organized
concerts just to check if dogscould be used in the entrance of
(13:19):
a concert or of a venue, to seeif dogs could, for example,
screen people in a real lifescenario, and that worked quite
well.
So there are, let's say, theway it's paved to use dogs in
those in this ways.
But anyhow, we need moreinfrastructure, the better
infrastructure, traininginfrastructure, which is similar
(13:43):
, like in the explosivedetection or drug detection dogs
.
So that's something that has tostill be established.
Patrick Sullivan (13:51):
Yeah, you mentioned this idea that
standardization of the trainingand certification of the dogs
would be important.
Next step so you've sort ofshown a scientific principle
here and you've shown that itmeets one of our criteria around
the robustness, which is thatit plays out in multiple
settings and or studies done indifferent places.
So you sort of build that piece.
(14:12):
But then really to move intoprogram, you mentioned
standardization of training andcertification of dogs.
So just building, I guess itwould build on ideas like the
explosives detections, but whatdoes it look like to put those
in place and are there anyefforts underway to do that,
either for the future of thispandemic, this ongoing pandemic,
or for future public healthchallenges?
Sebastian Meller (14:33):
So I need to admit, we have been talking a
lot with politicians here inEurope and I need to admit, well
, there were some somepoliticians and some some
administrative structures whichalso supported us to do such
studies, but somehow, you know,there was not a point in which
(14:53):
there was a decision toestablish this infrastructure
for training, for example, ofdogs, of medical detection dogs.
I think it's still quite a.
It's difficult to say, but Ithink people maybe do not trust
that much dog.
Maybe then they trust a testwhich was artificially produced
(15:13):
and not produced by biology, youknow, but by humans.
So they do trust more of thosethings.
So we don't it did not have anyopportunities to establish this
outside, let's say, outside thescientific space, but anyhow,
anyway, the scientific space isjust a lot of things are
(15:34):
happening right now, evenconcerning, for example, lung
COVID or post COVID syndrome.
We are also doing some researchand going for that, going more
in this direction.
Something happened in thescientific space, so I'm quite
interested, but the first goalwe had this did not work out to
use really dogs to help to breakthrough the infection chains of
(15:56):
the pandemic.
Patrick Sullivan (15:57):
I mean there may be work that can continue to
be done, more on a behavioralscience side, maybe about the
acceptability of this, because Ithink people don't bat an eye.
I mean, it was an airportearlier this week and somebody
came through with a dog and likepeople didn't even look up from
what they were reading.
The dog's just checking out thebags and it's obviously for
that sort of explosives purpose.
So I think, trying tounderstand, maybe through
(16:20):
qualitative methods or throughsurveys, what people's concerns
are, and if it's a useful tool,then I think it's a separate
question to ask how we introduceit.
But clearly, in the case ofairport safety and explosives
detection and I think drugdetection maybe in like customs
and immigration, this isacceptable and people just sort
(16:41):
of do their thing and the dogsdo their thing, and it's not a
big deal.
So it may just be a little bitof acclimation Exactly Great.
So I want to turn now to what wecall behind the paper, and so I
think when we talk tocolleagues, at whatever stage of
career, the questions arealways like how do you come up
with ideas, how do you overcomethe things that are challenging
about doing these and how do youwork with colleagues and all
(17:04):
those pieces that aren't part ofthe research question but
they're important about how weget work done.
So I wonder you sort of alludedto some of them but you see,
you have this idea and you canget the data and it sounds like
you're in a network.
But what was the biggestchallenge, just sort of, in
getting started software,people's involvement, skepticism
(17:27):
of colleagues, like what cameup, that you sort of had to get
in place before you could moveahead and do the work.
Sebastian Meller (17:35):
So I think the biggest challenge for me was
the reading of the papers, butnot just reading, just reading
again and again, and again andagain and again and reading
between the lines, and I reallywas keen on not missing any
detail.
And that was really, reallydifficult, because I don't know
(17:56):
if you have ever seen asupplementary table that is so
huge.
So if I have put it into themain manuscript, let's say, then
I think it would have exploded.
But really I really looked ateach detail and that was
actually the thing.
Patrick Sullivan (18:13):
That was like, yeah, but you did for
transparency, though.
Then I think the supplementarytable ends up being an online
appendix, like so people want togo and see that yes so it did
come in, I didn't see that.
I think that I do think,sometimes, figuring out in the
systematic reviews that I'vebeen involved in, it's also a
(18:34):
case that you start out with aset of things that you're
interested in and then the firstsix or eight papers that you
read, you realize like, oh, likethis thing is also of interest
and maybe I should.
Sebastian Meller (18:45):
So it's a little bit of an iterative
process, exactly, and then youstart again to read it again,
but at the same time to stick toyour guidelines, and that's
really a yeah, it may be quiteintense, just to stick to your
guidelines, but then to realize,oh, there's something else
which could be really important.
Ok, I need to start again andto read it under a different
(19:07):
light again.
Exactly, this machinery wasquite labor-intense, but the
other things around me, likesoftware or colleagues or
something that was quite so theylet me work.
We worked together and we hadmeetings and that worked quite
well.
So there was no problem.
(19:28):
It's great.
Just about the material itself.
Patrick Sullivan (19:31):
I have to ask, as someone who started not in
research training but inclinical training in veterinary
medicine myself and peoplealways ask me why did you go to
veterinary school and then doepidemiology work and people?
So really a lot of your work isrelated to dogs.
So what inspired you or whatwas your path to get in to be in
(19:56):
a college of veterinarymedicine and to do this work
around with dogs in terms of thedirection of your research
career, yeah, so after or duringmy veterinary studies, I
realized that I wanted toresearch.
Sebastian Meller (20:10):
Somehow something happened in my head
that I realized OK, I am quitemore interested in what, the
background, what is happening inthe background, let's say.
So that was one way, and Istarted with preclinical studies
and then came back to theclinic, let's say, and now I'm a
postdoc at the interface ofclinic and basic research.
(20:33):
So it's really an interestingfield, especially in dogs,
because dogs are great.
So we are living with dogstogether for thousands of years
and there are great models fordiseases for humans, but not
only this.
They are like our, they areliving with us, they are our
family members and that's reallyinteresting to also have a look
(20:54):
on them, to also research them,to bring the importance of
those living beings also moreinto the foreground.
I can tell you something I hadone inspiring meeting in my high
school time.
There was an elderly lady thatwas a good friend of my father
they had, they were in a group,a photography group, let's say,
(21:14):
and that was their hobby andthey were like traveling around
Europe from cities and makingpictures of cultural and of
historical stuff, and then theywere meeting once a week and
just discussing pictures in agroup.
So that was quite a verycreative space and I have been
there sometimes, and as a littlechild.
Then when I grew older, I ofcourse was not there that much.
(21:36):
But she asked then somehow myfather, what about my exams for
high school, how is it going?
And my father told her that Ialso do a part of my exams in
French because I lived at theFrench-German border and she was
French, she was a translator inFrench and German, and so she
asked my father if I wouldaccept her to come once a week
(21:57):
to our home just to discussabout subjects, reading
newspapers, discuss about thedifferent things in the
newspapers, and so that was areally inspiring space for me
and she was really inspiring.
And suddenly she asked me whatwould she do after your high
school?
And I was telling her I want tobe a veterinarian, and then she
(22:20):
did not say oh, ok, interesting, so you will be a clinician,
interesting, what will you dothere?
No, she was.
Her first sentence was wow,there is so much to discover in
veterinary medicine.
And I did not think aboutdiscovering something in
veterinary medicine.
Of course you can.
You always have to discover asyou grow in your work you
discover new things.
That's important.
(22:40):
But I did not think about theresearch, research aspect of
discovering something that thereis so much to discover, and
that was like a little sparklefrom a very inspiring person to
me.
That became, yeah, became aflame, became a fire, and then I
think that had also a big,great role for me.
You know meetings with, forexample, elderly, wise people.
(23:01):
So that's an advice to theyoung folks put away your phone
and talk to the people aroundyou.
It's quite important, I think,and sometimes, when it's getting
hard, I sometimes think of herand think of her sentence that
there's so much to discover andthat's something that is showing
me the big picture.
So that's, that's the energysource, I think, sometimes.
So that was really inspiring.
(23:22):
Maybe this is too long, just asmall sorry.
Patrick Sullivan (23:26):
No, I think this is.
I think this is absolutelyperfect because I think how we
get to where we are, two thingsOne, how we get to where we are,
and sharing that is a generousthing, because there will be
some people who are, you know,well earlier in their careers.
And I think what you're sort ofsaying is that there's a value
in sort of in talking to peoplelike whether they're in our
(23:49):
field or not about what you do,of explaining what you do and
being inspired by, you know, byother people's thoughts.
And I think in these, in thesepodcasts, we sort of ask about
this very thing, which is likeyou know, how did you end up
getting here?
And I think these kinds ofevents one are nice to
acknowledge Exactly.
I hope if she's a podcastlistener, you know that you can
(24:11):
share it onwards, but I thinkit's inspiring for people just
to listen to those things thathappen in our lives that feel
important, because they oftenare important and lead us to
important places.
So who or what do you think hasbeen the biggest influence on
your professional work and path?
Sebastian Meller (24:30):
Yes, so for my current work, I think that head
of department here Holger ishis name he has, I think, one of
the biggest influence on thework.
So he's an incredibly creativeand really positive guy and a
real out of the box thinker and,yeah, he makes actually you to
like to leave your comfort zoneand I think this is one of the
(24:53):
most important things to havethe energy for changing
something and to changesomething to the better.
And in the first moment onethinks when he has a new idea
that it's crazy, but thensuddenly everything makes sense.
So I don't know how he's doingthat, but he has quite talented
concerning this.
Patrick Sullivan (25:12):
What a nice acknowledgement of his role.
And you know, my old careerperson perspective is as you
move on, think about what thatmeant to you and how you can
create that kind of environmentwhen you get in that position.
Along the same lines, if youcould think back to yourself you
talked about like an earlierstage in your education and when
(25:32):
you were a younger person, ifyou could talk to that younger
person and give your youngerself a piece of advice, what
would it be?
Sebastian Meller (25:39):
So I think, well, maybe don't worry too much
.
That's one of an advice I wouldtell to myself.
I would maybe also tell myselftrust the process.
So, whatever, whatever processit is, just give it more trust.
And I also think that, well, Iwould maybe also say that if it
feels not comfortable, it is theright way to investigate this
(26:02):
feeling and not to run away fromit.
And I think now, right now I amthere that you know, if it just
does not feel like 100% right,just don't turn away, just
investigate it.
Maybe there's an opportunity,you know.
That's, I think, importantadvice.
Patrick Sullivan (26:19):
So that brings us to the end of our episode.
We covered a very broad span oftopics and I'm so grateful for
you, Dr Miller, for joining ustoday.
It was a real pleasure to haveyou and hopefully we'll see some
more of your work in thepublished literature and get a
chance to talk again.
Thank you so much.
Sebastian Meller (26:36):
Thank you so it was a pleasure to me.
Thank you.
Patrick Sullivan (26:38):
I'm your host, Patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
Epi Talk Brought to you byAnnals of Epidemiology, the
official journal of the AmericanCollege of Epidemiology.
For a transcript of thispodcast or to read the article
featured in this episode andmore from the journal, you can
visit us online atwwwannalsofepidemiologyorg.
Hello, you're listening to EPITalk
Paper, a monthly podcast fromthe Annals of Epidemiology.
I'm Patrick Sullivan,Editor-in-Chief of the journal,
and in this series we take youbehind the scenes of some of the
latest epidemiologic researchfeatured in our journal.
Today, we're talking with Dr.
(00:34):
Sebastian Meller about hisarticle "Canine olfactory
detection of SARS-CoV-2-infected humans-- a systematic
review.
" You can find the full articleonline in the November 2023
issue of the journal at www.
annalsofepidemiologyorg.
Dr.
Sebastian Meller is apostdoctoral researcher at the
Department of Small AnimalMedicine and Surgery at the
(00:56):
University of VeterinaryMedicine, Hanover, Germany.
His research focuses onneuroscience, epileptology and
behavior in the field ofclinical veterinary research and
translational medicine.
Dr.
Meller, thank you so much forjoining us today.
Yeah, thank you for having me.
So you published thismeta-analysis that pulls
together a theme of sort of OneHealth, which is where
(01:18):
veterinary medicine and humanpopulation health come together,
and also SARS-CoV-2, which isstill a really present issue in
our public health.
Can you talk a little bit aboutthe purpose of the study that
you did?
What question were you tryingto answer?
Sebastian Meller (01:33):
Yeah, so we had the first study or first
systematic review, to bringtogether the many claims that
were out there in the researchcommunity that dogs might detect
the coronavirus, and so thatwas quite new.
Of course it emerged right inthe pandemic.
I think that's logical, and sowe wanted to see if the dog is
(01:56):
really able to function as adiagnostic or as a screening
tool and to see how a dogperforms versus like established
methods, like PCR techniques,which we considered the gold
standard.
And of course we had somecriteria to exclude and include
the studies, and we were alsolooking at that.
(02:17):
There are enough, let's say,samples provided so positive,
PCR positive and PCR negativesamples, and then we tried to
make a story out of it.
Patrick Sullivan (02:27):
Great.
So what was the main finding orthe key answer to your question
, which is is there evidencethat dogs do sort of a good job,
or a sufficient job, atdetecting SARS-CoV-2 infections?
Sebastian Meller (02:38):
Yeah, so, indeed, dogs are able to detect
coronavirus.
That was like the main finding,but it is very important to
mention that the studies and thestudy designs and the
approaches were quite different,and I think this comes from the
fact that we were quite in thebeginning of the pandemic and a
lot of research groups were justlooking for something which
(03:01):
could help quickly to somehowcontrol the pandemics, and so
there were a lot of workinggroups worldwide that suddenly
came up with that idea and, yes,so in the end that research, or
my research, showed that, yeah,indeed, the dog is a quite good
(03:21):
screening or test system.
So we found that 80% of studiesshowed higher sensitivity than
80% and 90% of studies showed aspecificity of higher than 90%
and even low biased studies.
So we had some low biasedstudies.
They showed that, yeah, theyshowed a good performance of
(03:43):
over 80% sensitivity and 90%specificity too.
So that was quite nice to seethat even the unbiased studies
showed a quite good performancefor the dogs.
Yeah, but we also realized that, as I mentioned in the
beginning, we had some issueshow to standardize right, or we
saw that a lot of studies had alot of standardization issues.
(04:04):
So that's also one importantfinding we need more
standardization andcertification processes for dogs
to detect medical diseases.
Patrick Sullivan (04:13):
Two important issues that sort of came up in
the article were one about biasin studies and which studies
were more or less biased.
How do you operationalize that?
How do you decide, when you'redoing this analysis, the bias of
the studies and why is thatimportant?
Sebastian Meller (04:29):
Yeah, so we used one tool that was the
QUADAS-2 tool, which is createdfor systematic reviews and is
created for the assessment ofdiagnostic tests.
So that was one part that weused.
So we looked at dogs as adiagnostic test in this respect.
But of course dogs are not onlydiagnostic tests, they are also
(04:52):
living beings.
That's why we also use anothersystem, but we can talk about
that maybe later.
But what in the beginning weused was the QUADAS-2 tool,
which had us to see if studiesare biased or not.
So we were looking there at howwas the patient selection, how
was the index test, that means,the dog, how it was used or how
(05:12):
dogs were deployed.
We were looking at thereference standard, which was
the PCR test, and also at theflow and timing domain.
We also had a look concerning amore temporal dimension,
concerning when tests are shownto dogs after they have been
taken before from the people.
So then we looked at the biasand also the applicability and
(05:33):
we used this semi-quantitativetool to do the bias assessment
in first place.
Patrick Sullivan (05:38):
So you have a set of things about the way the
experiments were done or mightbe done that you think are
important going in, and some ofthose will favor less bias
studies and some will favor morebias studies.
Yes, exactly.
And then you have just yourself, or multiple people, assessing
the individual articles?
Sebastian Meller (05:56):
Yes, I did the main work actually, so I
assessed the articles, but Ialso had a colleague who did the
cross check.
Excellent, If we used theQUADAS-2 tool correctly and we
had a high degree of correlationbetween our results.
So we knew that the tool workedactually and we asked the right
questions.
Patrick Sullivan (06:13):
I also just have to note that you get an
award for, I think, the firstarticle in Annals of
Epidemiology ever to have smileyfaces and frowny faces in your
table.
Sebastian Meller (06:23):
So yeah, yeah, so that was quite
unconventional.
Patrick Sullivan (06:30):
Unconventional , but it gives a little color to
the article.
Sebastian Meller (06:34):
Yeah, it lives a little bit color exactly, and
it was also in the beginningfor me it was quite weird to use
smiley faces because that's notthe thing you use normally, but
on the other side it was like afresh new thing to use smiley
faces.
Patrick Sullivan (06:50):
Yeah, and I mean it conveys what you want to
convey, so that's good.
Sebastian Meller (06:54):
Exactly, and that's a point that's a
semi-quantitative way ofassessing and that's a good way
to assess semi-quantitative data, which actually, for systematic
review, is the right scale,let's say, to use for assessment
.
Yeah, so we also used anothertool, but we can come to that in
an instance.
Patrick Sullivan (07:14):
Yeah, we can go and talk about, because you
did use two approaches.
One was this QUADAS- 2 tool,but then there was also kind of
a quantitative system that youuse.
Can you say a little bit moreabout that system and what it
was used for?
Sebastian Meller (07:28):
Yes, first of all, we wanted to do a
systematic review.
So it was important for us tohave a quality feature for the
systematic review, which is, asI mentioned before, a
semi-quantitative tool likeQUADAS- 2.
And normally there are someopinions about it and some
rationales about it that youshould not use score systems for
(07:49):
systematic reviews, like scoresthat you can count, let's say.
Well, we had a dilemma becausethen we thought about, ok, what
to do?
Because on the one side, thedog when we use QUADAS- 2, the
dog, we see the dog as adiagnostic system.
But the dog is not only adiagnostic system like other
diagnostic systems, it's alsoliving being.
It's a living being who'sworking with its nose, with its
(08:11):
olfactory tool.
And that's why the other ideawas to use a quantitative tool,
that score system, because thisis a score system that assesses
the quality of sense detectionwork.
And then we decided to do both,to just put both together, to
also stress that dogs are notdevices, dogs are living beings.
(08:34):
We wanted to show thatdichotomy and that's why we use
both tests, even if the scoresystem is not adequate for
systematic reviews.
Anyhow, we thought that it is abig picture of the whole test
system living being dog, andthat's the rationale why we use
both tests.
But of course the score systemshould be considered a add-on to
(08:56):
the actual systematic review.
Diagnostic test qualityassessing tool, QUADAS- 2.
Patrick Sullivan (09:03):
Yeah, and I think that's a really just
interesting point for peoplethat are thinking about.
You know, we often get in thepattern of saying, like well,
the tool that everybody uses,this, but is this or that.
But you really sort of tookinto account the nature of the
intervention or the nature ofthe sort of public health tool
here and exactly, just added asecond.
(09:24):
You think about it astriangulation of all the issues
that are involved.
So, exactly, yeah, so can youtalk about any of the
limitations of your review orwhat you would?
You could think of them asweaknesses or you could think of
them as opportunities to donext steps.
But so what's on answer andwhat do you think some of the
(09:45):
limitations are?
Sebastian Meller (09:46):
Yeah, so one of the limitations I think that
should be mentioned is I am alsoone of the researchers who did
canine olfaction studies inSARS-CoV-2 detection and of
course those studies also fellinto the inclusion criteria.
So that is one of thelimitations, I would say.
(10:07):
But I think it's reallyimportant to say that I anyhow
looked at the guidelines that Imade.
I really stick to theguidelines and of course that's
challenging but I stick to theguidelines.
I know the dog community,olfactory detection community, I
know what groups are doing.
I know that, not everythingwhat is written in the papers.
(10:28):
So of course what is written inthe papers is, of course, the
truth, but of course there are alot of things between the lines
that happen and I know thosethings.
But anyway, I really referredonly to the published
information and I did not addany information that were known
but not published.
So it's really I really wasreally important to me to just
(10:51):
look like, okay, just have rulesand I stick to the rules and I
just worked that through and soI think I did it well.
I think we also had somestatisticians in our team who
also gave the confirmation ofhow I was working, that this was
okay, anyhow, that's one of thelimitations, of course, so I
(11:13):
was also part of it, but it wasimportant also to us to just,
you know, to state, to make astatement, also to assess
scientifically if this is reallygoing into the right direction
or not, the whole thing with thedetection dogs.
Patrick Sullivan (11:29):
I think this is a general issue which is, in
a lot of research communitiestend to have a fairly small
number of investigators and weknow each other, we see each
other at conferences, and so Ithink that the important thing
is acknowledging that that couldbe a source of bias, you know,
and saying I'm going to set upthese tools in very objective
(11:49):
ways, so that that will be mytouch point.
So I really appreciate youraising that issue, because it's
one that we don't really talkabout that often, but especially
in a smaller, more sort offocused kind of research
community, I think.
Sebastian Meller (12:02):
Yeah exactly, so we have quite a small
research community.
That's that's one of the pointsthat are important to stress,
that, yes, so we are also mostinterested in our subjects,
right?
So of course, we've right alsothe we have the knowledge to
know what is really important,what really matters in this
research field.
So that's why, yeah, thosethings happen, yeah, but it's
(12:25):
important to mention.
Yeah, of course.
Patrick Sullivan (12:27):
Yeah, it's important.
It's just important toacknowledge and be systematic.
That's all, yeah.
So are you aware of programsthat are using dogs for COVID
screening currently?
Sebastian Meller (12:36):
So actually, yes, there are some programs, or
there were some programs.
For example, in Dubai they usedogs at the airport, and even it
was not only in a scientificway, so they really use the dogs
as an established tool.
Let's say we also did somethingquite interesting, I think, but
(12:58):
that was a study, but a fieldstudy.
So we did a concert, weorganized concerts here in
Germany with a huge concertcompany and we organized
concerts just to check if dogscould be used in the entrance of
(13:19):
a concert or of a venue, to seeif dogs could, for example,
screen people in a real lifescenario, and that worked quite
well.
So there are, let's say, theway it's paved to use dogs in
those in this ways.
But anyhow, we need moreinfrastructure, the better
infrastructure, traininginfrastructure, which is similar
(13:43):
, like in the explosivedetection or drug detection dogs
.
So that's something that has tostill be established.
Patrick Sullivan (13:51):
Yeah, you mentioned this idea that
standardization of the trainingand certification of the dogs
would be important.
Next step so you've sort ofshown a scientific principle
here and you've shown that itmeets one of our criteria around
the robustness, which is thatit plays out in multiple
settings and or studies done indifferent places.
So you sort of build that piece.
(14:12):
But then really to move intoprogram, you mentioned
standardization of training andcertification of dogs.
So just building, I guess itwould build on ideas like the
explosives detections, but whatdoes it look like to put those
in place and are there anyefforts underway to do that,
either for the future of thispandemic, this ongoing pandemic,
or for future public healthchallenges?
Sebastian Meller (14:33):
So I need to admit, we have been talking a
lot with politicians here inEurope and I need to admit, well
, there were some somepoliticians and some some
administrative structures whichalso supported us to do such
studies, but somehow, you know,there was not a point in which
(14:53):
there was a decision toestablish this infrastructure
for training, for example, ofdogs, of medical detection dogs.
I think it's still quite a.
It's difficult to say, but Ithink people maybe do not trust
that much dog.
Maybe then they trust a testwhich was artificially produced
(15:13):
and not produced by biology, youknow, but by humans.
So they do trust more of thosethings.
So we don't it did not have anyopportunities to establish this
outside, let's say, outside thescientific space, but anyhow,
anyway, the scientific space isjust a lot of things are
(15:34):
happening right now, evenconcerning, for example, lung
COVID or post COVID syndrome.
We are also doing some researchand going for that, going more
in this direction.
Something happened in thescientific space, so I'm quite
interested, but the first goalwe had this did not work out to
use really dogs to help to breakthrough the infection chains of
(15:56):
the pandemic.
Patrick Sullivan (15:57):
I mean there may be work that can continue to
be done, more on a behavioralscience side, maybe about the
acceptability of this, because Ithink people don't bat an eye.
I mean, it was an airportearlier this week and somebody
came through with a dog and likepeople didn't even look up from
what they were reading.
The dog's just checking out thebags and it's obviously for
that sort of explosives purpose.
So I think, trying tounderstand, maybe through
(16:20):
qualitative methods or throughsurveys, what people's concerns
are, and if it's a useful tool,then I think it's a separate
question to ask how we introduceit.
But clearly, in the case ofairport safety and explosives
detection and I think drugdetection maybe in like customs
and immigration, this isacceptable and people just sort
(16:41):
of do their thing and the dogsdo their thing, and it's not a
big deal.
So it may just be a little bitof acclimation Exactly Great.
So I want to turn now to what wecall behind the paper, and so I
think when we talk tocolleagues, at whatever stage of
career, the questions arealways like how do you come up
with ideas, how do you overcomethe things that are challenging
about doing these and how do youwork with colleagues and all
(17:04):
those pieces that aren't part ofthe research question but
they're important about how weget work done.
So I wonder you sort of alludedto some of them but you see,
you have this idea and you canget the data and it sounds like
you're in a network.
But what was the biggestchallenge, just sort of, in
getting started software,people's involvement, skepticism
(17:27):
of colleagues, like what cameup, that you sort of had to get
in place before you could moveahead and do the work.
Sebastian Meller (17:35):
So I think the biggest challenge for me was
the reading of the papers, butnot just reading, just reading
again and again, and again andagain and again and reading
between the lines, and I reallywas keen on not missing any
detail.
And that was really, reallydifficult, because I don't know
(17:56):
if you have ever seen asupplementary table that is so
huge.
So if I have put it into themain manuscript, let's say, then
I think it would have exploded.
But really I really looked ateach detail and that was
actually the thing.
Patrick Sullivan (18:13):
That was like, yeah, but you did for
transparency, though.
Then I think the supplementarytable ends up being an online
appendix, like so people want togo and see that yes so it did
come in, I didn't see that.
I think that I do think,sometimes, figuring out in the
systematic reviews that I'vebeen involved in, it's also a
(18:34):
case that you start out with aset of things that you're
interested in and then the firstsix or eight papers that you
read, you realize like, oh, likethis thing is also of interest
and maybe I should.
Sebastian Meller (18:45):
So it's a little bit of an iterative
process, exactly, and then youstart again to read it again,
but at the same time to stick toyour guidelines, and that's
really a yeah, it may be quiteintense, just to stick to your
guidelines, but then to realize,oh, there's something else
which could be really important.
Ok, I need to start again andto read it under a different
(19:07):
light again.
Exactly, this machinery wasquite labor-intense, but the
other things around me, likesoftware or colleagues or
something that was quite so theylet me work.
We worked together and we hadmeetings and that worked quite
well.
So there was no problem.
(19:28):
It's great.
Just about the material itself.
Patrick Sullivan (19:31):
I have to ask, as someone who started not in
research training but inclinical training in veterinary
medicine myself and peoplealways ask me why did you go to
veterinary school and then doepidemiology work and people?
So really a lot of your work isrelated to dogs.
So what inspired you or whatwas your path to get in to be in
(19:56):
a college of veterinarymedicine and to do this work
around with dogs in terms of thedirection of your research
career, yeah, so after or duringmy veterinary studies, I
realized that I wanted toresearch.
Sebastian Meller (20:10):
Somehow something happened in my head
that I realized OK, I am quitemore interested in what, the
background, what is happening inthe background, let's say.
So that was one way, and Istarted with preclinical studies
and then came back to theclinic, let's say, and now I'm a
postdoc at the interface ofclinic and basic research.
(20:33):
So it's really an interestingfield, especially in dogs,
because dogs are great.
So we are living with dogstogether for thousands of years
and there are great models fordiseases for humans, but not
only this.
They are like our, they areliving with us, they are our
family members and that's reallyinteresting to also have a look
(20:54):
on them, to also research them,to bring the importance of
those living beings also moreinto the foreground.
I can tell you something I hadone inspiring meeting in my high
school time.
There was an elderly lady thatwas a good friend of my father
they had, they were in a group,a photography group, let's say,
(21:14):
and that was their hobby andthey were like traveling around
Europe from cities and makingpictures of cultural and of
historical stuff, and then theywere meeting once a week and
just discussing pictures in agroup.
So that was quite a verycreative space and I have been
there sometimes, and as a littlechild.
Then when I grew older, I ofcourse was not there that much.
(21:36):
But she asked then somehow myfather, what about my exams for
high school, how is it going?
And my father told her that Ialso do a part of my exams in
French because I lived at theFrench-German border and she was
French, she was a translator inFrench and German, and so she
asked my father if I wouldaccept her to come once a week
(21:57):
to our home just to discussabout subjects, reading
newspapers, discuss about thedifferent things in the
newspapers, and so that was areally inspiring space for me
and she was really inspiring.
And suddenly she asked me whatwould she do after your high
school?
And I was telling her I want tobe a veterinarian, and then she
(22:20):
did not say oh, ok, interesting, so you will be a clinician,
interesting, what will you dothere?
No, she was.
Her first sentence was wow,there is so much to discover in
veterinary medicine.
And I did not think aboutdiscovering something in
veterinary medicine.
Of course you can.
You always have to discover asyou grow in your work you
discover new things.
That's important.
(22:40):
But I did not think about theresearch, research aspect of
discovering something that thereis so much to discover, and
that was like a little sparklefrom a very inspiring person to
me.
That became, yeah, became aflame, became a fire, and then I
think that had also a big,great role for me.
You know meetings with, forexample, elderly, wise people.
(23:01):
So that's an advice to theyoung folks put away your phone
and talk to the people aroundyou.
It's quite important, I think,and sometimes, when it's getting
hard, I sometimes think of herand think of her sentence that
there's so much to discover andthat's something that is showing
me the big picture.
So that's, that's the energysource, I think, sometimes.
So that was really inspiring.
(23:22):
Maybe this is too long, just asmall sorry.
Patrick Sullivan (23:26):
No, I think this is.
I think this is absolutelyperfect because I think how we
get to where we are, two thingsOne, how we get to where we are,
and sharing that is a generousthing, because there will be
some people who are, you know,well earlier in their careers.
And I think what you're sort ofsaying is that there's a value
in sort of in talking to peoplelike whether they're in our
(23:49):
field or not about what you do,of explaining what you do and
being inspired by, you know, byother people's thoughts.
And I think in these, in thesepodcasts, we sort of ask about
this very thing, which is likeyou know, how did you end up
getting here?
And I think these kinds ofevents one are nice to
acknowledge Exactly.
I hope if she's a podcastlistener, you know that you can
(24:11):
share it onwards, but I thinkit's inspiring for people just
to listen to those things thathappen in our lives that feel
important, because they oftenare important and lead us to
important places.
So who or what do you think hasbeen the biggest influence on
your professional work and path?
Sebastian Meller (24:30):
Yes, so for my current work, I think that head
of department here Holger ishis name he has, I think, one of
the biggest influence on thework.
So he's an incredibly creativeand really positive guy and a
real out of the box thinker and,yeah, he makes actually you to
like to leave your comfort zoneand I think this is one of the
(24:53):
most important things to havethe energy for changing
something and to changesomething to the better.
And in the first moment onethinks when he has a new idea
that it's crazy, but thensuddenly everything makes sense.
So I don't know how he's doingthat, but he has quite talented
concerning this.
Patrick Sullivan (25:12):
What a nice acknowledgement of his role.
And you know, my old careerperson perspective is as you
move on, think about what thatmeant to you and how you can
create that kind of environmentwhen you get in that position.
Along the same lines, if youcould think back to yourself you
talked about like an earlierstage in your education and when
(25:32):
you were a younger person, ifyou could talk to that younger
person and give your youngerself a piece of advice, what
would it be?
Sebastian Meller (25:39):
So I think, well, maybe don't worry too much
.
That's one of an advice I wouldtell to myself.
I would maybe also tell myselftrust the process.
So, whatever, whatever processit is, just give it more trust.
And I also think that, well, Iwould maybe also say that if it
feels not comfortable, it is theright way to investigate this
(26:02):
feeling and not to run away fromit.
And I think now, right now I amthere that you know, if it just
does not feel like 100% right,just don't turn away, just
investigate it.
Maybe there's an opportunity,you know.
That's, I think, importantadvice.
Patrick Sullivan (26:19):
So that brings us to the end of our episode.
We covered a very broad span oftopics and I'm so grateful for
you, Dr Miller, for joining ustoday.
It was a real pleasure to haveyou and hopefully we'll see some
more of your work in thepublished literature and get a
chance to talk again.
Thank you so much.
Sebastian Meller (26:36):
Thank you so it was a pleasure to me.
Thank you.
Patrick Sullivan (26:38):
I'm your host, Patrick Sullivan.
Thanks for tuning in to thisepisode and see you next time on
Epi Talk Brought to you byAnnals of Epidemiology, the
official journal of the AmericanCollege of Epidemiology.
For a transcript of thispodcast or to read the article
featured in this episode andmore from the journal, you can
visit us online atwwwannalsofepidemiologyorg.
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