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Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık and Suyog Chandramouli.
Takeaways:
Chapters:
00:00 Introduction to Epidemiological Modeling
05:16 The Role of Bayesian Methods in Epidemic Forecasting
11:29 Real-World Applications of Models in Public Health
19:07 Common Misconceptions About Epidemiological Data
27:43 Understanding the Spread of Ideas and Beliefs
32:55 Workflow and Collaboration in Epidemiological Modeling
34:51 Modeling Approaches in Epidemiology
40:04 Challenges in Model Development
45:55 Uncertainty in Epidemiological Models
48:46 The Impact of AI on Epidemiology
54:55 Educational Initiatives for Future Epidemiologists
Links from the show:
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.
Today, am honored to host Adam Brzezarski, a leading expert in infectious disease modeling
and epidemic forecasting.
2
:Adam is a professor of infectious disease epidemiology and co-director of the Center for
Epidemic Preparedness and Response at the London School of Hygiene and Tropical Medicine.
3
:His research focuses on harnessing data and analytics to improve epidemic preparedness,
and he has contributed real-time analysis
4
:governments and health agencies during major outbreaks, including Ebola, Zika and
COVID-19.
5
:In this episode, Adam takes us inside the world of epidemiological modeling, discussing
how Bayesian methods help refine predictions and inform public health decisions.
6
:We explore the challenges of modeling infectious diseases from data uncertainty to
real-time forecasting and the importance of communicating findings effectively to
7
:policymakers and the public.
8
:Adam also highlights common misconceptions about epidemiological data and dives into the
role of automation and AI in epidemic response.
9
:This is Learn Invasions Statistics, episode 130, recorded November 26, 2024.
10
:Welcome Bayesian Statistics, a podcast about Bayesian inference, the methods, the
projects, and the people who make it possible.
11
:I'm your host, Alex Andorra.
12
:You can follow me on Twitter at alex-underscore-andorra.
13
:like the country.
14
:For any info about the show, learnbasedats.com is Laplace to be.
15
:Show notes, becoming a corporate sponsor, unlocking Bayesian Merge, supporting the show on
Patreon, everything is in there.
16
:That's learnbasedats.com.
17
:If you're interested in one-on-one mentorship, online courses, or statistical consulting,
feel free to reach out and book a call at topmate.io slash alex underscore and dora.
18
:See you around, folks.
19
:and best patient wishes to you all.
20
:And if today's discussion sparked ideas for your business, well, our team at Pimc Labs can
help bring them to life.
21
:Check us out at pimc-labs.com.
22
:my dear patients, hope you're doing well.
23
:Two main announcements for today.
24
:First and foremost, thank you so much to all of you who sent testimonials about learning
vision statistics and intuitive base to support my green card application.
25
:I was genuinely touched, surprised and moved by the number, kindness and generosity of
your messages.
26
:I am so happy and grateful to be surrounded by truly open-minded, kind
27
:and helpful people like you.
28
:I received more than 40 testimonials.
29
:So thank you so much to all of you who've taken the time out of their busy day to work
about a very nerdy endeavor and how it changed the field of statistics according to you.
30
:I will of course let you know what happens with my application and I'm already planning a
very special in-person surprise for all of you for February 20th.
31
:6 but more info will come in good time.
32
:So again, thank you so much and in the meantime, live long and prosper or shall we say
live base and prosper.
33
:And talking about prospering, if you are interested in baseball and patient statistics and
working in an MLP team like let's say the Miami Marlins, so that would mean working with
34
:me, if you'd love that, get to Miami, get to meet me and maybe
35
:working together in the baseball research and baseball solutions teams.
36
:Well, now is the time our teams are growing fast.
37
:And this is your chance to get in on something that's very special.
38
:Honestly, I absolutely love what's going on there.
39
:And we've just opened two brand new roles one baseball analyst for the solutions group.
40
:That's more here toward junior applicants and senior one, which is senior data scientist
in research group.
41
:It's mine.
42
:So
43
:If you are passionate about baseball research, you love working on collaborative teams.
44
:And well, bonus points, you know your way around advanced modeling approaches like for
instance, patient methods with the PIMC and STAN and neural networks, time series, all
45
:that stuff.
46
:Well, we definitely want to hear for you.
47
:I think what we're doing in Miami is some of the most exciting in the MLB right now.
48
:Let me know if you have any questions, if you're a patron of the show, you're on the
discord with me, feel free to send me any of your comments, questions, recommendations of
49
:people, send that to your friends and maybe see you soon in Miami.
50
:On that note, let's go on with the show.
51
:Adam Kucharski, welcome to Learning Vision Statistics.
52
:Thank you.
53
:Yeah, thank you for taking the time and bearing with me, we've had a few technological
problems to record that episode, folks.
54
:That was difficult.
55
:But, you know, as they say, the obstacle is the way.
56
:we are here.
57
:And a big thank you to Chris Wyman.
58
:to for putting us in contact.
59
:Chris and I met at StunCon 2024 in Oxford.
60
:Chris was actually in a panel discussion that I recorded with him and Lisa Semenova that
was episode 120 for people who are curious and want to hear more about how cool
61
:epidemiological science and computational biology are.
62
:So feel free to
63
:to check that out.
64
:With Adam today, we're actually going to tackle some similar topics, but you have a very
wide and very broad research.
65
:So, yeah, that's why I'm super happy to have you on the show today.
66
:You do so many things and you're also a great science communicator.
67
:You've written several books, as I've said in the introduction.
68
:So, yeah, all of these books will be in the show notes.
69
:So people feel free to check them out.
70
:Definitely recommend them.
71
:are absolutely fascinating.
72
:But before we touch on that, can you tell us with your three actually nowadays, how you
ended up working on these?
73
:Yeah, sure.
74
:So my work kind of bridges really understanding epidemics and getting better at predicting
and responding to them.
75
:So there's this mix of aspects to that.
76
:So some of it is
77
:understanding the drivers of what we see in terms of how things spread.
78
:So for something like dengue fever, might be climate influences, accumulation of immunity
in the population.
79
:For something like, I say flu or COVID, the implications of vaccination over time, how
those viruses evolve.
80
:Alongside that, we're also doing a lot of work to build up the methods and tools that we
need to respond to very quickly.
81
:COVID, lot of these things were developed very quickly.
82
:often just working over weekends.
83
:Can we actually do a lot better, particularly for the predictable questions that we're
going to have to answer?
84
:In terms of how got into it, my background's originally in maths.
85
:I applied maths to my PhD.
86
:But even that was starting to go more in this direction of epidemiology and questions
around how we can understand the process of infectious disease.
87
:And for me, as a field, it kind of sits quite nicely between something where having some
mathematical and statistical understanding
88
:can give you a lot of value very quickly.
89
:But also there's enough unknowns about the underlying rules and processes.
90
:It's not something like physics where we have a lot more mechanistic understanding.
91
:So it means squeezing a lot more out of the data you have available, particularly under
pressure in a situation like an epidemic.
92
:I see.
93
:OK.
94
:That's very interesting.
95
:So it's something that was a long time coming.
96
:You've always kind of
97
:been interested in these topics, at least since your past high school studies, right?
98
:Yeah, I think there's a lot of those questions where it obviously has an enormous impact
on people, but also just from a kind of curiosity point of view, these are quite hard
99
:questions.
100
:And often the methods that are in the textbooks don't quite work for what you need to do.
101
:it's an ongoing, interesting
102
:research area to be in because almost every epidemic you work in, what you thought you
knew and what you thought you'd solved, suddenly you happen.
103
:And so yes, kept me busy and kept me interested along with many of my colleagues.
104
:Yeah.
105
:And what about patient stance?
106
:Do you remember when you were first introduced to Zain?
107
:So I think it was particularly during PhD.
108
:I think my kind of undergrad was a lot more
109
:traditional maths, so relatively little statistics, a lot more kind of theory.
110
:So the sort of thing where you learn measure theory rather than learning the kind of
coming at it from a data point of view.
111
:think during my PhD I started working a lot more with data and especially if you start to
look at processes involved in diseases, processes involved in immunity, the natural next
112
:question is how can we estimate things meaningfully within that?
113
:And then particularly if you have
114
:patchy bits of data or different studies that you want to combine in some way, or if you
have some analysis you've put together and then you want to make statements about
115
:additional data coming in, Bayesian statistics are a natural framework for doing that.
116
:And so really in the mix of my work, in some cases you just get simple probability
problems and Bayes' formula is a nice get out of jail way of solving that problem.
117
:And in other cases, it's more of a framework for thinking about the entire analysis.
118
:You want to be able to come to the best possible explanation or the best possible evidence
at a point in time.
119
:But then you want to update that as you get more data coming in.
120
:And again, it gives you very nice toolkit for doing that.
121
:Yeah, Yeah, that makes sense.
122
:Damn.
123
:That's very fascinating.
124
:That brings me a lot of...
125
:More questions for you, Adam.
126
:Yes, one of these questions that I for you is, especially during COVID, Bayesian modeling
became a crucial tool.
127
:I remember I did some work at my very low level at that time, but you did a bunch of work
on that.
128
:as you were saying, I imagine there were lot of very short nights and very long weeks.
129
:Can you share a specific example of how
130
:these models were able to inform public health decisions?
131
:Yes, I think there's quite a few instances, particularly around scenarios, where models
can be a very useful decision support tool.
132
:Because essentially, if you're going to make a decision about what to do in an epidemic,
everyone has a model in their head.
133
:Because if you think, let's do this, let's try this, you're making some assumptions about
what you think is going to happen.
134
:You make some assumptions about how you think epidemics work.
135
:So models.
136
:a very nice way of allowing us to really write down and formalize what we think those
processes are and what we think the intervention is going to do.
137
:And then we can debate whether that's reasonable, whether that's not.
138
:We can see if that generates any counterintuitive effects.
139
:But in doing that, you really want to capture the extent of information you have about the
problem you're dealing with.
140
:So even, for example, the question of how much effort is it going to take to get
transmission down?
141
:Well, one of the key things that
142
:going influence that is how much transmission there really is.
143
:So a lot of the analysis that we did, being able to pass around uncertainty was really
important.
144
:So very early in the pandemic, for instance, because we were relying on quite uncertain
data from China, quite uncertain data about exported cases, we had some sense of what
145
:transmission would do in a country without any control measures, but there was quite a
large amount of uncertainty on that, probably reproduction number.
146
:somewhere between two and four.
147
:And so what we wanted to do is when we generate UK scenarios to present them, we didn't
want to give one number.
148
:We wanted to say, look, we don't know actually it's more the upper end or lower end of
this, but we want to kind of define that uncertainty in what we simulate.
149
:And then after that wave comes through, we had lockdowns like many other countries, there
was this push to reopen.
150
:And then the question is, okay, so we've had that wave.
151
:what's the reopening going to look like?
152
:And again, that's where these kind of basic methods will be very helpful because we can
take the uncertainty and perhaps we've got more confidence now about what we're dealing
153
:with, but then we want to pass that level of confidence into what's going on in future.
154
:And then, you know, as variants started emerging, that became even more important because
often for Alpha, we would have some degree of confidence about how much more transmissible
155
:it was, how much more severe it was.
156
:And in any analysis, for example, we did a lot of work early in 2021 of how quickly you're
to have to vaccinate.
157
:And I think there's a lot of pressure to lift lockdown and try to understand that
trade-off.
158
:If you're vaccinating at this rate and you're lifting lockdown this quickly, what's that
going to look like?
159
:And again, none of those values you had very precisely and you had more epidemic data
coming in all the time.
160
:particularly that process of reopening in 2021, there was a very tight relationship
between models and policy because
161
:In the UK, was one of the few times where the policy was actually very informed by what
was useful from a technical point of view.
162
:So what they did was they had these series of steps in the roadmap.
163
:It was designed to give enough signal in the data post each step that if something was
going very wrong, they wouldn't have implemented another step before you had that signal.
164
:So they were kind of deliberately spaced out so the models and the epidemiologists would
have enough time to work out what that's.
165
:relaxation had done.
166
:So again, from a basic point of view, that's really nice because it gives you enough time
to kind of update your posterior sensibly before you do the next step and work out what
167
:effect that's going to have.
168
:Yeah.
169
:Yeah.
170
:Yeah.
171
:Okay.
172
:That's fascinating.
173
:I didn't know there was that level of coordination where you could actually do that.
174
:There are many examples where it didn't work in such a coordinated fashion, but think that
roadmap reopening was one where there was a much tighter relationship between the
175
:questions coming down from policy and what models needed to say something sensible about
the implications.
176
:Yeah.
177
:Yeah.
178
:And I guess, I mean, I was wondering how much do you think the pendulum has swung back
from then?
179
:Do you think we'll be faster?
180
:to implement these workflows and improve them next time there is a pandemic?
181
:Or will we have to work from a blank slate because politics is so short sighted with very
short cycles?
182
:Yeah, I think that's a really good question.
183
:I think there's...
184
:There's some things that have been positive in terms of progress.
185
:So I think in terms of what we're doing in others in consolidating a lot of the tools that
are available.
186
:So some things now that we, know, looking at quick questions that H5N1, questions that I
just wouldn't have bothered doing previously just as a curiosity, because it would have
187
:been three, four hours just for a maybe question.
188
:And now in 20 minutes, you can get a rough example.
189
:So think that's quite nice is bringing things into reach.
190
:But I think in terms of just
191
:staff capacity and people to do this work.
192
:I think there are a of people who, you know, put a huge amount of time in often as around
the world, getting pulled off different roles and off other projects.
193
:And so I think in a way in that sense, we probably don't have the same workforce who could
undergo that amount of pressure for that amount of time.
194
:in a way that creates the necessity of we need to get better tools because we don't have,
I think that.
195
:volume of people, also just all our projects.
196
:we basically took people off a lot of other funded projects.
197
:And I think we wouldn't have the resource to do that in the same way.
198
:So for me, think particularly what we're interested in at moment is if you imagine the set
of tasks that you might have to do in an epidemic, some are tasks that a lot of other
199
:people have to do.
200
:And we can really predict ahead of time you're going to need to do that.
201
:So stuff like working out the severity of infection or working out the transmission.
202
:We know we're going to need that.
203
:that done.
204
:We know lots of people are going to do that.
205
:So that's a really good task for automation and getting really leading edge, know, basic
methods standardized so people can ever want to do that.
206
:We don't all have to duplicate effort.
207
:There's other things that could be really specific and maybe within the country, there's a
kind of subgroup that's particularly important or there's a certain variant or something
208
:you might want to deal with.
209
:And that's going to require more domain knowledge.
210
:And I think at the moment, even if talk to people
211
:at different outbreak organizations around the world, they probably spend six to 70 % of
their time on quite low level, predictable, data wrangling type questions rather than the
212
:sort of 30 % of expertise led questions they want.
213
:I don't necessarily think in a pandemic people would end up working less hard because
everyone wants to contribute as much as they can.
214
:But I think what I'd like to see is a lot more time on that 30 % and going much deeper.
215
:into what we can understand rather than spending a lot of time even just trying to get
ahead, a sense of the data and the basic tasks to answer very simple questions.
216
:Yeah, yeah, yeah.
217
:That makes sense.
218
:Not sure I'm really reassured by that answer.
219
:But yeah, that's actually a realistic answer.
220
:Something I'm also really curious about, because I see that a lot as a modeler myself, is
where the common misconceptions that you've seen the public or elected officials or even
221
:professionals have about epidemiological data.
222
:and what the scientific community can do to clarify these misunderstandings.
223
:Yeah, that's a really good question.
224
:I think one common misconception is even just what data are as a thing.
225
:I think often when people talk about raw data, they're talking about inferred estimates.
226
:So think one example of this is excess stats that I the media often treat as a measured
thing.
227
:rather than actually a counterfactual.
228
:And I think I remember talking to journalists about the comparison with flu and I think a
lot of them discovered that the flu mortality statistics they've used every year actually
229
:involved some quite big modeling assumptions about seasonal patterns behind them.
230
:And there isn't just this magic number that we measure.
231
:I think similarly with lot of the estimates, for example, in the UK they ran randomized
testing surveys.
232
:they randomly tested people in the community each week and reported it.
233
:And that number, that wasn't a raw proportion because, of course, you needed to do some
standardization across groups and weighting.
234
:And in some cases, because it was quite noisy across multiple panels, they would have a
kind of smooth underlying proportion and then infer that.
235
:So it was sensible modeling, but there was modeling behind that.
236
:And I think the misconception often is that these things are raw data.
237
:often, even a very simple thing, if you calculate
238
:If you run a survey and calculate proportion, you're making some modeling assumptions.
239
:Or if you know, oh, we're going to adjust for age.
240
:Why are you adjusting for age?
241
:Why are you not adjusting for something else?
242
:You're making assumptions about what you think are important drivers of that thing you're
measuring.
243
:So I think that's probably one thing we can get better at communicating is that actually
this distinction between models and data isn't that simple.
244
:That a lot of things we think of data are actually modeled estimates.
245
:And actually a lot of the things that sometimes people treat as
246
:super complex models are actually just quite straight, simple steps for thinking about
data.
247
:So even one of, if you've got a bunch of hospitalizations over time and you want to know
when the infections happened, you can use quite simple, like a decomposition model just to
248
:take that and work out when the infections are.
249
:And I think often in public perception, when people talk about models, what they mean is
very complicated scenario models that people are assuming are used to make forecasts.
250
:So I think it's often the of a crystal ball of use this big complex model to say what's
going to happen.
251
:And I think in reality, what happens is often the more complex models you use for
scenarios and like what if, because we can't, we can't make forecasts often in pandemics
252
:as well, because you'll be forecasting what policymakers are going to do.
253
:And if your models are used as tools to support their decisions, it becomes quite odd to
use that as a forecast.
254
:I mean, I think the example I sometimes get
255
:is that imagine you're playing a game of poker and at any point in time you want to
understand the implications of your decisions.
256
:What you don't really want is someone on your shoulder saying, I bet you're going to fold
later this round.
257
:What you want is someone who can say, look, if you raise, this is likely the situation
you're going to face.
258
:This is the risk you're taking on.
259
:If you fold, is potentially what the outcomes are going to be.
260
:So that's really how a lot of these models are used in decision support.
261
:But I think in public consciousness, they're often seen as this is
262
:we're saying this is going to happen in the future.
263
:And I think part of it is communication and particularly, I think, getting people to able
to play with those simple models can be very helpful.
264
:So they realize it's not this super complex thing.
265
:It's probably what they're doing in their head, but just writing it down in a bit more
structured way.
266
:Yeah.
267
:I find also, indeed, walking through scenarios
268
:is something that's really helpful to people because, well, they can imagine the scenarios
and that's much more tangible and concrete than numbers and possible distributions.
269
:can see that a lot.
270
:mean, sports is a lot like that, especially baseball, lots of different discrete
scenarios.
271
:Yeah, and I think often people, particularly with epidemics,
272
:are kind doing it in their head all the time.
273
:So the epidemic is going up, and you say there's going be a problem, and then you get a
few data points that seem to be tailing off a bit.
274
:And basically, everyone's doing that updating in their head of where they think it's
going.
275
:I would have loved to have seen more politicians and journalists actually write down
their, you could get them to actually just write down their prediction and their kind of
276
:distribution of what they think is going to happen and then see how there's update.
277
:And then you could actually
278
:give them a better understanding of what's going on in their head relative to actually
what's plausible given the data that's coming in.
279
:Yeah.
280
:But so I completely agree with that.
281
:And I really love that.
282
:But I think here the incentives are really bad for politicians, right?
283
:then if you like if you publicly and privately, think I that's a lot.
284
:I think that's a really good point.
285
:I think that's a really good point.
286
:I think getting politicians to write down what it's going to happen publicly is very
difficult.
287
:But I even privately, think there were a lot of people probably not doing that.
288
:So I mean, amongst colleagues and stuff, we sometimes just had just probably more things
that when you think this study comes out, what do you think it's going to show?
289
:And I think it's sometimes quite, we like to kid ourselves.
290
:So even if it's just writing it down for yourself, I think it's just quite useful to have
that to effect on.
291
:Oh, yeah.
292
:No, I mean, I
293
:I'm like completely on board in helping people develop a more probabilistic thinking.
294
:You I think you doing the work you're doing and also the communication work you're doing
is very important.
295
:All your books.
296
:That's also why I have these podcasts.
297
:People like Nate Silver writing books is very important too.
298
:I don't know if you read his last book On the Edge, but that's very important too for
that.
299
:Right.
300
:And I think that'd be awesome if we were gearing towards that direction.
301
:But yeah, the problem is like the incentives in the public incentives in politics are so
bad when it comes to that, that you actually have a much better standing if you just, you
302
:say anything and everything and just are not
303
:held accountable for that, then actually betting on something that would happen and then
changing course if actually what you said would happen did not.
304
:Yeah, I think it's partly from the communication.
305
:There's also, yeah, what are the things that we can do meaningfully and what are the
things that you can encourage people to do versus just wants to be feasible.
306
:And I think also just from a modeling point of view, sometimes there's this idea that
307
:for political decisions, you should have this big model with absolutely everything in and
all the kind of the weights of how you do everything.
308
:And I can't see any political party wanting to do it because ultimately those things are
going to be weighed, not in a kind of written down, you know, we're going to put 10 % on
309
:this and 15 % on this.
310
:And so I think it's it's working out where the science can be really informative and where
actually there's more of that kind of human political element and that's not.
311
:the best possible to be fighting at this point.
312
:Yeah, yeah.
313
:Yeah, it's a great point.
314
:actually, your talking about your books in the rules of contagion, you explore why things
spread.
315
:And I really love that.
316
:So can you tell us a bit more about that?
317
:And how does patient thinking help in understanding these patterns?
318
:especially for diseases.
319
:Yeah.
320
:So I think that's something striking writing the book is I set out thinking that there
would be these analogies in other fields.
321
:And I wasn't sure necessarily how strong they would be.
322
:But the more I dug into that, I realized actually they were in many cases very explicit
and very directly informative.
323
:For example, after the 2008 financial crisis, there was very
324
:clearly described epidemic thinking that drove a lot of the interventions we saw, things
like ring fencing, things like capital requirements for banks at risk in network.
325
:It's really about thinking about it like a contagion problem.
326
:Similarly, if you dig into the history of companies like Buzzfeed that were very good at
generating viral content, they were actually writing research reports on how you evaluate
327
:the reproduction number of marketing campaigns.
328
:And actually,
329
:I subsequently discovered that Buzzfeed journalists would have a equivalent to the
reproduction number as a metric for their articles.
330
:So it wasn't just this quite fuzzy comparison.
331
:Actually, this was the same bits of theory that were appearing between these two fields.
332
:I think one thing I find quite interesting on the Bayesian angle in how things spread is
particularly some of the debates around how you convince people and how
333
:people adopt beliefs and they take off.
334
:Because there was quite a popular idea for a while known as the backfire effect, which is
where if you try and convince someone, you can end up basically just strengthening their
335
:pre-existing belief.
336
:And this idea that attempts to change people's beliefs can kind of backfire, which
obviously doesn't bode well for any kind of social progress, because it's this idea if you
337
:try and convince someone that marriage equality or something is good idea, it's just going
to lead to them being entrenched.
338
:But what subsequently happened is a lot of the work, both on the applied side of actually
people trying to get these, get support for these kind of this progress, but also on some
339
:of the scientific side of people studying them, suggested it's actually much closer to a
Bayesian problem, that it's not that you're leaving people to entrench their beliefs,
340
:rather, if you give people weak evidence, you're not going to shift their distribution
much.
341
:Which I thought was kind of really
342
:Interesting.
343
:And I hadn't actually thought about it quite much in that way.
344
:And it kind of makes sense that even if you've got like a quite strong prior for
something, if someone gives you evidence that agrees with that prior, it's going to look
345
:pretty similar afterwards.
346
:And especially because we're not doing all those calculations in our head.
347
:We're just sort of seeing the feeling we come away with.
348
:And it really struck me that actually in those situations, we're probably much better
evaluating the effects of evidence that we disagree with because the posterior, you know,
349
:just
350
:Mathematically, you expect the posterior to move more.
351
:And so perhaps the situations where what people were thinking was a backlash effect is
more just we're better at critiquing evidence we disagree with rather than the ones that
352
:kind of line up.
353
:Because if it agrees with us, we're going to walk away with the same opinion afterwards
anyway.
354
:So yeah, I think now it's obviously a much harder problem to study in terms of spread of
beliefs and so many factors that can play into that.
355
:But yeah, there's still a lot of ongoing debate on the extent to which people's
356
:adoption of beliefs and behaviors of these Bayesian versus some other factors that kind of
explain how those are updated over time.
357
:Yeah, I see.
358
:That's really amazing.
359
:I love that.
360
:I didn't know that example about...
361
:What's the website you were saying?
362
:Oh, BuzzFeed.
363
:Yes, thank you.
364
:It's remarkable.
365
:Yeah.
366
:And it was coming to it because they did campaigns about Hurricane Katrina.
367
:None of these were viral.
368
:And this is one of their key findings that this wasn't like COVID where it spread and
spread and spread.
369
:But for 10 shares, they might get an extra seven or eight.
370
:So if you get it, if you spark lots of little clusters of sharing, you might.
371
:actually get quite considerable additional uptake as a result.
372
:I think there was one that was marketing campaign for detergent and it just wasn't
transmissible at all basically.
373
:So there was quite a nice quantifying that there's certain things that people obviously
want to tell people about and others that even if you got the biggest marketing budget in
374
:the world, you're going to struggle to make washing up liquid contagious.
375
:I see, see.
376
:Yeah, yeah.
377
:You know, something I'm curious about and I ask this question to Chris at StancOn is,
concretely, what does it look like for you to work on an epidemiological model?
378
:Like, who are you talking to and what's your workflow and technical stack?
379
:Yeah, that's a really good question.
380
:So I think generally,
381
:anything we build starts with a problem and often that problem either comes from someone,
if it's a policy related question, it comes from someone policy side, maybe scientific
382
:advisors to certain agencies in the case of like an applied organization like MSF or WHO,
it will come from a representative who we're working on maybe that outbreak or that
383
:situation and there'll often be quite specific things that people are interested in.
384
:So it might be a forecast of actually what are we dealing with?
385
:It might be that there's a plan to implement vaccination or control measure.
386
:A lot of the work we did in Ebola, for example, different control measures being proposed
and people wanted some idea of the relative impact that that would have.
387
:There's also, of course, just on the scientific side, so the work we've done around
effects of behaviour and immunity, it might be that you've got lab colleagues who've
388
:noticed some interesting features and want to work out how can I get
389
:sufficient estimates out of my data.
390
:mean, I mean, this is, I think another example where Bayesian thinking is very helpful
that you might have, say, lots and lots of antibody responses.
391
:And you don't want to analyze them all as like individual data sets, because there's going
to be some commonalities in just how the biology works between individuals.
392
:So having this kind of hierarchical models can be very powerful, because you can have
shared information just on the underlying dynamics across the population, but you can also
393
:have individual features of
394
:what we looked at previously and this sort of thing.
395
:In terms of developing the kind of model and the stack, it depends a bit on whether it's
similar to another problem we've seen.
396
:So in case of a policy question, often what we'll do is, in the many, many models and
things we've dealt with previously, you'll find the thing that's most similar.
397
:And I think increasingly, we're seeing progress in libraries.
398
:So a lot of the work we do is in R or in some cases, using kind of Stan.
399
:backends.
400
:And so you'll take something that maybe is from the library that you think is most
appropriate for that and then a model that's templated up that's closest to that.
401
:And then ideally you'll just plug in and work through it away.
402
:Often you might either adapt some features of the model process in terms of say how
transmission is happening or what groups are affected or you might bring in data.
403
:So you might have a model set up from UK contact structure and you'll just encode data
from some of us so you can adapt it.
404
:In other cases, you might get something that's a very specific, quite niche question.
405
:you know, for example, I don't know, like the ones we're doing during COVID, testing
people at certain types of gathering or something.
406
:And that's not something necessarily you have a model off the shelf, but in some cases,
the equation is quite simple to write down.
407
:You if you've got this many people and you're testing this many at this point in time.
408
:And in that case, we might just build something more bespoke.
409
:And I think that...
410
:The challenge always, mean, like with any kind of software development problem is to what
extent do you do something quick for a very specific problem versus take on coding that if
411
:you've got to that problem repeatedly.
412
:And so I think we're getting a better sense now.
413
:There's certain things that are sufficiently complex.
414
:There's large enough scope for bugs.
415
:It's gonna be useful to enough people where actually that makes sense to package it up
more because this is a library.
416
:And there's other things that actually are simple enough and transparent enough and kind
of
417
:bespoke enough that you don't want to build a software tool for every single one of those.
418
:Some of those you can just do quickly as the problem arises.
419
:Yeah, yeah, yeah.
420
:That makes tons of sense.
421
:once you're like, I'm wondering also that the size of the teams in these cases, because
obviously each time I talk to a modeler in your field, it sounds like the models are
422
:really
423
:big and huge and take a lot of time to work on because they are so complex.
424
:So yeah, I'm wondering how many people does it take to work on a model and how do you
actually do that?
425
:Because like, yeah, is everybody working on the model at the same time?
426
:Do you have some team for that part of the model, another team for the other part?
427
:How does that work?
428
:Yeah, so I think, I mean, to give the example of some of the big purpose scenario models
that are used by our group in the UK,
429
:I haven't got the github we've off from ever.
430
:It's probably at least 10, 15 people who've made substantial contributions to that co-base
at the various points in time.
431
:And in some cases, can be, mean, ideally we'd make these things as modular as possible.
432
:So early on, for instance, I had some colleagues who were focusing very much on the
transmission dynamics and kind of how people interact, what interventions were going to
433
:be.
434
:And I worked a lot more on the disease burden module.
435
:So once you have transmission infections,
436
:you can convert those infections into an estimate of how many are going to be
hospitalized, how long are they going to be hospitalized for.
437
:So then you have that kind basic model structure.
438
:But then over time, that got expanded because variants came in, vaccines came in, and you
end up with multiple versions of those models.
439
:And there's always this kind of challenge of, do you make
440
:kind of one core model that has sufficient flexibility to all those problems, or do you
kind of fork a model and use it for a special case and you're not going to use it again?
441
:So that model's actually been used in multiple countries.
442
:We adapted it to a whole range of different settings, some cases with very unusual
patterns of immunity and infection.
443
:And in that case, it didn't make sense just to build that into the original model because
it was just such a specific example.
444
:But I think that's one thing where, as well, if we had to go back now, because there's so
many versions of the model and so many applications, because it's real time, we have to
445
:deliver that very quickly, it's obviously harder to now say, how would we do that for flu
epidemic?
446
:And so I think what we're trying to move more towards is these very modular examples that
you can plug in all the bits you need, but also have that capacity to adapt it.
447
:And I think that's just that's kind of an ongoing challenge that you can have these things
that are very stable and structured, but very hard to adapt.
448
:Or you can have these things that are very maybe flexible and easy to adapt, but not
necessarily as kind of efficiently structured as you'd like.
449
:mean, there are examples as well of modeling where it might be one or two people
developing something quite quickly or just making use of a library.
450
:mean, some of the popular methods, for example, for estimating reproduction numbers.
451
:that tool would have been used by a huge amount of people, but obviously the active
contributors to the development might be a lot smaller.
452
:Okay, okay.
453
:Yeah, I see.
454
:Yeah, so big diversity in the size of the projects.
455
:And do you have a favorite type of models, actually, that you'd like to share with us?
456
:So I one, well, I think one of the models that
457
:I think it delivered a lot of value in various things that we've worked on over the years.
458
:actually it was both on some of the H7N9 analysis we did about 10 years ago, the outbreaks
in China where you had infections coming from poultry and potentially human to human
459
:transmission as well.
460
:It a big question of looking at that human data, how much was coming from spillover, how
much was coming from human human infections.
461
:And we actually got an analogous version of the problem with some of the COVID variants.
462
:So for Delta, how much of this was imported cases from India versus transmission
establishing in countries?
463
:And in both those situations, so first of all, it's a bit of a modeling headache because a
lot of traditional models are structured in a way that basically says, if you've got your
464
:cases over time, the new cases that appear, it has to be one of those past ones that
infected them.
465
:So if you look at a lot of the common calculations for how you do reproduction numbers,
it's known as a generative model.
466
:So in other words, or renewal equation is the kind of specific version.
467
:So the new infections are the products of the infections that come before.
468
:Someone has caused that infection.
469
:If you've got importations or spillover, that's no longer the case.
470
:You've actually got this additional term coming into your equation.
471
:So it makes it a trickier inference problem if you've got two routes just to infecting
someone.
472
:But it can also be a more powerful one because
473
:In the case of avian flu, we knew when the wild lipultary markets were closed.
474
:And in the case of Delta, we knew when the travel ban against India was implemented.
475
:So what you've got as an estimation problem, you've got two things that are influencing
your infections, but you know the kind of shape of one of these because you know when the
476
:markets close, you know when the flight patterns came in.
477
:And suddenly that gives you a lot more estimation power on the thing you care about, which
is human-to-human transmission.
478
:So I think for me that
479
:That's just a really nice example of a model that is not super complicated.
480
:It's relatively easy to explain.
481
:like there's two things that can affect someone, which is it?
482
:But actually, you can squeeze a remarkable amount out of your data as soon as you know the
shape of one of those processes.
483
:OK.
484
:does that?
485
:I mean, it obviously comes with.
486
:significant challenges of using these models.
487
:Can you detail a bit these challenges that you face when creating such models?
488
:Yeah, I think there's...
489
:whole mix.
490
:mean, in some cases, there's just understanding the process you define in model that for a
lot of outbreaks, there's a lot of things you'd like to know the role of in a process.
491
:So, for example, how different age groups are interacting or affected by certain things.
492
:But in some cases, you might not have the data that you need to actually
493
:So a good example in COVID was a lot of people, know, the epidemic would come down, a lot
of people would argue about why that was.
494
:And actually, if you just look at case data or just look at deaths, it could be immunity,
it could be changes in behavior, it could be something to do with the climate, it could
495
:be, you know, a whole range of different things that could influence that transmission.
496
:And just looking at one time series of outcomes, you can't distinguish what those are.
497
:You really need some data on antibodies.
498
:You need some data on social mixing to tell you which of those is the most likely
explanation.
499
:So I think that's often a big challenge is where you have lots of potential explanations
and you can't actually untangle them.
500
:mean, the other, to use H5N1 as a current example, unlike the H5N9 and Delta where we knew
how that shape of the introductions was changing, we don't know that for H5.
501
:So now you get cases popping up and they they haven't had a...
502
:contact with poultry, maybe it's a wild bird, maybe it's a human, we've got no idea.
503
:And I think that's kind of a big challenge.
504
:Almost a model can't really tell you anything at this point because the data is so
uninformative about the process.
505
:There's lots of questions at the moment, get journalists who ask, you're building models
of H5.
506
:Actually, outbreak data is just too bad to say much.
507
:We can design some what-if hypothetical models, but I think that's much harder.
508
:I think there's also just the technical challenge that
509
:you know, in terms of just making sure that the models you build are without bugs.
510
:And then also edge cases is another classic one that there's, there can be sometimes some
slight counter-intuitive things, particularly if you're doing with kind of, know, delay
511
:processes.
512
:So one example, which I think it was sort of a communication one, but even, you know, if
you have an epidemic that's going up and you suddenly stop it and you have a delayed
513
:outcome like that.
514
:the peak in when the epidemic stops isn't the same as the peak in death because you're
doing a kind of delayed convolution.
515
:We're doing a smooth out of lay distribution.
516
:And so features like that, that making sure that you've actually got that relationship
defined properly.
517
:And again, this is, think, why the move to a lot more established libraries rather than
trying to make sure these things are bug free in real time.
518
:OK.
519
:Yeah.
520
:Yeah.
521
:And a question I often get, you
522
:personally, and I'm guessing you are getting a lot too, is, okay, cool.
523
:I get uncertainty estimation, you know, with the Bayesian models.
524
:Why do I care?
525
:So I think a lot of what you want to do, particularly decision-making, comes down to
confidence and evidence.
526
:So particularly in the middle of an epidemic, there's a lot of things we don't know with
any confidence.
527
:So I guess on the one hand, you could just ignore it and just go for a point estimate.
528
:Is it going up?
529
:Is it going down?
530
:But particularly if you want to ask, is the epidemic under control, it's not very helpful
to have a yes, no always.
531
:That you might want to say,
532
:Yes, it's under control.
533
:How confident are you that it is?
534
:And again, having that uncertainty in a reproduction number, if you like, well, 100 % of
our density is below one.
535
:And that's what we had post lockdowns and social mixing data, for example, in the UK, that
there was uncertainty in that distribution, but all of that density was below one.
536
:So the conclusion was we're very confident that transmission is coming down.
537
:I think similarly for some of the variants, we found that
538
:you'd get uncertainty in the estimates.
539
:So maybe it's 30 % more transmissible, maybe it's 40%, maybe it's 50.
540
:You can be very confident it is more transmissible.
541
:And I think it's a difficult one because policymakers sometimes love single answers.
542
:They don't want a kind of vague.
543
:But I think particularly if your uncertainty lands either side of a particular threshold
that matters,
544
:that can in a way give you more confidence in communicating and saying, yeah, look, this
is definitely going up or this is definitely, almost definitely under control.
545
:It's similar, know, the clinical trial, you look at the confidence interval, I think is
the equivalent of that.
546
:You want to know, you know, how much can you rely on this estimate?
547
:And I think that's where the uncertainty really comes in.
548
:Yeah, yeah, so basically, making sure
549
:that you're not fooled by the variance of your processes.
550
:Yeah, and think especially when you're dealing with exponential processes, errors can
accumulate.
551
:There's something that might feel quite small of, oh, the transmission rates this.
552
:And actually, if it's slightly higher or slightly lower, you get a rate of an outcome.
553
:Even something, say, as a very simple example, each person affects two others, and you cut
transmission in half.
554
:So each person affects one other, just another one other.
555
:a tiny amount of uncertainty there after two months could be the difference between a
massive epidemic and a handful of cases.
556
:And if you don't communicate that, people will be asking, well, why is there a massive
epidemic when you said it would fit it out based on your best estimate?
557
:yeah, yeah.
558
:That's definitely a good point.
559
:I'm curious also to have your
560
:to hear your thoughts about the latest advancements that we've seen in the last year in
artificial intelligence and large language models, because I'm guessing this is going to
561
:have also an impact, hopefully for the best, on epidemiology.
562
:So yeah, with these advancements, how do you see the future of the field of epidemiology
and
563
:and the role of patients stance in it.
564
:Yeah, I think there's a huge amount of really promising development.
565
:I think a lot of it at the moment that we're working on and most of us is trying to find
where do these solutions work.
566
:I think essentially we've been given this increasingly amazing toolkit, but in many cases
it wasn't necessarily developed, these are problems we're working on.
567
:And so finding where the applications works can be very powerful, where are the ones that
it's going to struggle.
568
:And so even, if you look at AI or some more traditional machine learning approaches that
there might be situations where if we're very interested in the mechanism and we've got
569
:some process we can find our model, perhaps that estimation or that prediction approach
isn't optimal for actually what we're trying to solve.
570
:But think equally, the pandemic we showed, there was a huge amount of data out there that
in many cases we weren't.
571
:able to interpret in a meaningful way.
572
:you might have some like a social contact and if it drops by a certain amount, you can put
that in a model with a very meaningful as meaningful change.
573
:But you might have quite a lot of very noisy data, which is giving some indication about
how people are behaving.
574
:But you can't extract those features and weigh them in a useful way in the same sense that
an AI model could do.
575
:So I think it's in a way finding a range of problems.
576
:think
577
:broadly the challenge we have for outbreaks often they're quite rare events.
578
:So even near what you validate against and what's your input and what's your kind of
outcome that you're trying to predict.
579
:But I think within a epidemic, especially you get larger clinical data sets, behavioral
data sets, a lot of value there.
580
:I think also some of the methods like universal differential equations and other things
coming through where it's taking that transmission model structure, but then incorporating
581
:things like neural networks to allow for more complexity
582
:in understanding the patterns that are kind going on alongside that.
583
:think there's, yeah, there's a lot of really interesting progress there.
584
:think just also just more generally in the field, there's some of the AI models that
essentially are learning features that can, mean, weather forecasting is one example where
585
:it's many, many times faster and less energy intensive for simulations.
586
:think again, that kind of, you can find ways of of approximating more complex models.
587
:I think just also in terms of the data that comes in, mean, lot of outbreaks often
described in narrative reports where it's kind of like a few paragraphs or so and so did
588
:this and went here and did that.
589
:And one of my colleagues did some nice prototyping, but he brought in this, that's all
local models that can take those quite difficult to interpret narrative reports and
590
:convert them into structured data, which you can then perform standard analysis on.
591
:And then you've got the issue of accuracy and validation.
592
:But I think there's a lot of
593
:work really across the spectrum, both in the models themselves, but also how you're more
efficient in what you're doing.
594
:Yeah, yeah, OK, I see.
595
:So basically, like in a lot of other fields, you would like to see a better communication
between these models, DLM models, in
596
:the humans in the loop, but clearly you don't see these kind of models being made entirely
by artificial intelligence.
597
:No, we've tried it actually.
598
:even things like co-pilot workspace, which look, you try them out on a simple problem and
they look really, really cool.
599
:So the idea that you can just give it a code base and tell it to do things.
600
:But often for quite specific things, if you say, build me a model with these features to
do this, I think because just the
601
:the training set just doesn't include anything resembling or very little resembling that
kind of problem.
602
:So if you want an LLM to build you some JavaScript, it's pretty good because there's just
so much that's trained on it.
603
:If you want quite an image, a practical model of an epidemic, it sort of struggles in some
ways.
604
:So think finding the shape, it's great for, I've got all these functions, can you package
them up and add the documentation and this kind of stuff?
605
:It makes a lot of tasks faster.
606
:But I think,
607
:Yeah, the idea is just going to do science.
608
:think it's mainly put forward by people who've only worked on a narrow set of scientific
problems.
609
:And there's probably some science is going to do well.
610
:And then there's some that's going to really struggle with, hopefully, not just the boring
bits.
611
:I think we need to map out where those gaps are.
612
:Yeah, yeah.
613
:mean, it's the same in my field, whether that's sports modeling or just
614
:statistical modeling in general, where it's really funny because if you ask an LLM what a
hierarchical model is, it can explain it extremely well.
615
:That's exactly what I would explain my students.
616
:But then if you ask for a pimsy code of that, the model is not at all hierarchical.
617
:It's just a classic model.
618
:But the LLM will be like, well, this is a hierarchical model.
619
:But it's not.
620
:Are you sure?
621
:Yeah.
622
:So I definitely have that back and forth, but it makes you more efficient.
623
:Also, it helps me, for instance, to kill my darlings maybe a bit faster, which is very
important in modeling.
624
:That's always better to do it, in my experience, with a human.
625
:But sometimes you don't have someone at the same stat level as you in your organization or
project.
626
:then you have to do that on your own and that can be quite hard.
627
:And there's something we're using actually for things like reviewing training materials.
628
:Because sometimes you want to throw a human view, but sometimes you want a first pass of
if an applied field epidemiologist is reading this, is there anything in there that just
629
:is going to not make sense or really obvious things you want to fix?
630
:It can just help accelerate a lot of that kind of production process.
631
:OK, OK.
632
:Yeah.
633
:That's actually, yeah.
634
:That sounds very useful.
635
:So you've been already very generous with your time.
636
:I'm going to play us out here.
637
:But I'm also curious to hear your thoughts about more educational perspective, because you
do a lot, as we've heard, lot of public communication.
638
:So given your experience, what
639
:educational initiatives would you recommend to better prepare the next generations of
epidemiologists?
640
:Yes, but also policymakers and citizens in general.
641
:Yeah, I think there's probably some specific angle, then it's to also become more general
ones.
642
:think it's not realistic to try and get people to have a deeper understanding of epidemics
any more than it's
643
:really sit to have a very, very deep understanding of kind of the nuclear threats or
something.
644
:But I think there are certain features of epidemics which are very important and very
often misunderstood.
645
:So think one aspect is exponential growth is a concept that people find very, very
difficult.
646
:You look at who makes and loses money in finance, and there's a definite divide in terms
of understanding that's a concept.
647
:And so I think having more intuition of that across more people is very helpful.
648
:Similarly,
649
:things like having lagged outcomes, but if you have an event you care about and then the
fact that happens later, that's something that's kind of widely misinterpreted during the
650
:pandemic.
651
:yeah, think it wasn't increasingly as COVID went on, it wasn't just people who hold
academic posts in epidemiology that were making a lot of useful contributions.
652
:You had a lot of people who were in adjacent industries who are maybe actuaries or in
finance or in other bits of, you
653
:academic work or even just, you know, math teachers, whatever, quite useful stuff because
they hadn't understand those concepts.
654
:You get a feel for some of those data problems that just needed more eyes on them.
655
:So I think for me, that's where a lot of value is.
656
:It's like, how do we have more people who can just not get very basic things wrong and
just have useful eyes on the problem, even if they don't know the ins and outs of exactly
657
:how you've captured that specific parameter?
658
:I think more generally, though, we're also just seeing, I think it's a challenge for a lot
of fields that with the emergence of AI, with the emergence of climate, sort of more
659
:complex understanding of epidemics.
660
:I think we are moving into a world that's much harder to sort teach yourself everything
from scratch.
661
:I if you think about even statistics as a field, the sort of statistics that practitioners
do relative to what's taught in schools is now very different.
662
:You know, it's the sort of doing kind of regression lines and the things that I did at
A-level.
663
:And I think we've seen that in a lot of fields now where actually the cutting edge is so
far detached that I think that can create partly challenges in communication because even
664
:if you – a very interesting climate, you can't I can't go and run a climate model in the
way that you might be able to sort of do a simple mathematical proof or a simple kind of
665
:statistical problem.
666
:So think there's that kind of relationship of how do we build
667
:trust and enough understanding of how those fields work that people can engage with them,
even though actually the kind of cutting edge is now so computationally intensive, in some
668
:cases just so difficult to explain in terms of the algorithms.
669
:Yeah, I think that AI is a prominent example.
670
:think there's a lot of those situations where it feels like there's a bit of a growing gap
that we need to bridge.
671
:Yeah, yeah, yeah.
672
:Yeah, definitely.
673
:I mean, completely agree with everything you just said.
674
:That's a topic that's dear to me, obviously.
675
:We've talked about that several times on the podcast.
676
:I think one of the best episodes we've done about that was episode 50 with Sir David
Spiegelhalter.
677
:Only sir yet to have been on the podcast.
678
:But yeah, that was a great episode.
679
:David is an awesome communicator also.
680
:I'll put that episode in the show notes for
681
:people who want to dig deeper because, yeah, definitely one of the episodes I recommend a
lot.
682
:And then the next one, episode 51 with Aubrey Clayton about his book, Bernoulli's Fallacy
and the Crisis of Modern Science, really recommend the book and the episode also because
683
:these two together make a great combination if you're interested in epidemiology.
684
:Okay, well, I am.
685
:That was awesome.
686
:Really, thank you so much for taking so much time.
687
:I knew that was going to be fascinating and it was.
688
:Thanks a lot to Chris Wyman again.
689
:But before letting you go, of course, I'm going to ask you the last two questions I ask
every guest at the end of the show.
690
:One, if you had unlimited time and resources, which problem?
691
:would you try to solve?
692
:That's a big one.
693
:And I think one of the things coming out of COVID that really struck me is with a lot of
infections, we're actually quite unambitious, I think.
694
:You know, we put up with a lot of disease in daily life.
695
:And even pre-COVID, if you look at lot of adverts for medicine when you're ill, it's, just
keep going, keep going.
696
:And I think we saw a lot of indications
697
:during COVID for some of the technologies and approaches that weren't lockdowns but were
actually much more efficient and even just like the work that Oxford have done on things
698
:like digital tools.
699
:So I think we've got these little signals that we could be much, much better in how we
tackle these problems.
700
:And I think that's something that would require quite a lot of resource and time to do
effectively.
701
:But even though I've got young children and
702
:Yeah, there's a lot of kids that kind of hospitalized with a lot of infections that we
could understand, we could test about.
703
:I think we rely a lot on we wait for a vaccine to be developed.
704
:But I think I've always just wondered, could we do something a bit cleverer?
705
:Can we go all this technology?
706
:I mean, there's a US colleague who during COVID said, this is our Apollo mission.
707
:Can we actually do something extremely innovative and ambitious?
708
:So I think vaccines were amazing.
709
:some of the treatments coming down, but we didn't, I think, solve that much earlier
problem of what to do about infection.
710
:So yeah, I'd love us to live in a different world where we can actually build on all the
tools and technology where we've got emerging.
711
:Yeah, yeah, definitely share that passion and objective.
712
:And if you could have dinner with a great scientific mind, dead, alive, or fictional, who
would it be?
713
:So I think I've recently for a project been reading up a lot about William Gossett, a
student who developed T-Tests and worked at Guinness.
714
:But actually digging more into his work, he was a really interesting character because
there was a lot of conflict with Fisher in terms of outlook.
715
:And Fisher was very much from the academic focus of, you accrue knowledge and you want
very high confidence in that knowledge.
716
:And that's why you have the sort of thresholds and experiments of science he's been
brought up.
717
:But Gossett was much more of a pragmatist.
718
:He was working for a big business.
719
:And I mean, there's one situation where he had a p-value of 0.13.
720
:And he said, you know, that's fairly good evidence.
721
:If it's a business decision that doesn't cost much and
722
:we can explore further, it's worth going forward.
723
:Whereas Fisher would have had that, okay, if it's not hitting the 5%, we're not
interested.
724
:so I think for me, that's maybe a part of statistics that got suppressed, I think,
probably lot of the 20th century.
725
:I think Fisher and Cobe probably won out in many ways in terms of imposing those criteria
and in some cases throwing away a lot of evidence.
726
:I mean, although it wasn't all explicitly baked in, was very anti-Basian, but I think that
making use of limited information, the gossip was very adamant.
727
:In some cases, I think he had budget to get two data points or something.
728
:And was still that I want to do something with that.
729
:So I think that would be a very interesting person to dig into the strategy.
730
:It also just sounds like he was just a very nice guy relative to Fisher and some of the
others at the time.
731
:So yeah, I think that.
732
:the kind of different outlook of where do you set the bar and actually, you know, what are
you trying to do with statistics?
733
:Are you trying to kind of get perfect knowledge or are you actually just trying to make
better decisions in whatever job or industry you're in?
734
:Yeah, yeah, I love that.
735
:So yeah, I think from what I understood, wasn't that hard to be a nicer guy than Fisher,
apparently.
736
:But yeah, no, I mean,
737
:I definitely resonate with what you were saying because all my models, most of my models,
I do them not for academic use, but for companies.
738
:yeah, there is a lot of things you have to be able to prioritize.
739
:I think that's one of the most important skills to have as a modeler, especially
740
:especially if you're in a small team, right?
741
:If you're in a big project with a lot of modelers, you can explore a lot of paths at the
same time.
742
:But if you're in a small team, then you have to really be able to set the priorities and
see what path you want to explore first.
743
:That's often a metaphor I use to explain what the modeling process is.
744
:It's like being lost in a desert and you're trying to find your way out.
745
:And the most efficient way is usually to explore a lot of paths and see which ones are
successful.
746
:There can be several of them.
747
:There can be just one of them.
748
:There can be zero sometimes.
749
:But also exploring a path that ends up not being successful is actually very informative.
750
:because then that means the people behind you won't make the same mistakes and they won't
go down that path.
751
:So to explore these paths, you can do it alone.
752
:If you don't have any more people, that's just going to take you more time, but you have
to do it or you can do it with several people simultaneously.
753
:But that's where the idea of priorities also very important because your priorities are
going to dictate which path you go down first.
754
:Yeah, and I think it's that, the priorities and the focus that I think is a kind of angle
which, know, maybe in the pursuit of perfection, we don't always get that balance right,
755
:particularly when it's kind of academic research interacting with very fast problems.
756
:Right, yeah.
757
:Awesome.
758
:Well, Edem, let's call you to show that this was an absolute pleasure to...
759
:have you here on the show.
760
:As usual, I will link to your website and your socials and your books in the show notes
for people who want to dig deeper.
761
:Thank you again, Adam, for taking the time and being on this show.
762
:Yeah, that's not me.
763
:Good job.
764
:This has been another episode of Learning Bayesian Statistics.
765
:Be sure to rate, review, and follow the show on your favorite podcatcher, and visit
learnbaystats.com for more resources about today's topics, as well as access to more
766
:episodes to help you reach true Bayesian state of mind.
767
:That's learnbaystats.com.
768
:Our theme music is Good Bayesian by Baba Brinkman, fit MC Lass and Meghiraan.
769
:Check out his awesome work at bababrinkman.com.
770
:I'm your host.
771
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772
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773
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774
:Thank you so much for listening and for your support.
775
:You're truly a good Bayesian.
776
:Change your predictions after taking information in and if you're thinking I'll be less
than amazing.
777
:Let's adjust those expectations.
778
:Let me show you how to be a good Bayesian Change calculations after taking fresh baiting
Those predictions that your brain is making Let's get them on a solid foundation