[LINK] [EFA-Privacy] Victoria COVID decision modeling

[Roger Clarke]

I'll get back to you in maybe a week, by which time I'll have worked my 
way through that little lot.

(I've got a couple of bits of homework to do before I can start on the 
'implications for my modelling' part of the job).

Makes yer think about the talent that's sitting around just waiting to 
be baited into delivering the goods  (:-)}

Seriously, many thanks for this Trent, not to mention for your ongoing 
hard work.

_________________

On 20/4/20 7:16 pm, Trent Yarwood wrote:
> Hi Roger,
> 
> I haven't got the time to give this the attention it probably deserves,
> because you've obviously put quite a lot of thought into it. I'm obviously
> just a little bit busy at the moment.
> 
> In defence of the SEIR model, it's a pretty well-established epidemiologic
> tool that has the advantage that it's a lot easier to model because it
> works on far fewer parameters than your model does. You're correct that it
> does make some assumptions to simply but (although I'm not an
> epidemiologist) it works well enough for most of the models that I use as
> an infection clinician.
> 
> Your third document (CVCT) does have some holes that jump out to even a
> cursory glance by a busy clinician.
> 
> The Doherty group released another model a week later which models (but
> doesn't measure) community transmission:
> https://www.doherty.edu.au/uploads/content_doc/Estimating_changes_in_the_transmission_of_COVID-19_April14-public-release.pdf
> and strongly suggests that it's not that much of a thing.  I wrote an
> explainer here:
> https://theconversation.com/latest-coronavirus-modelling-suggests-australia-on-track-detecting-most-cases-but-we-must-keep-going-136518
> 
> The problems with your suggestions lie with the random sampling.
> 
> (Apologies if this is vastly over-simplifying it and readers already know
> some of it)
> 
> We currently test for viruses in two main ways.
> PCR (essentially: direct detection of the viral DNA/RNA.  Generally
> indicates active infection, but remains positive for a while after
> resolution and suggests but doesn't absolutely imply infectivity)
> Serology (antibody testing.  Generally, but not always indicates past
> infection, although sometimes gives an indication that the infection is
> recent).
> 
> Prevalence surveys (which is what we call your idea of random-sample
> testing) usually rely on serology, because the testing material is
> available (eg: by doing antibody screening on blood donors, or people who
> have blood tests for other reasons). This way we can measure how many
> people *have had* the disease as opposed to the number of people who
> *currently do have* it - which is what we need to know in terms of
> loosening lock-down.
> 
> Prevalence surveys of active infection are difficult because:
>   1) there's a shortage of testing reagents (world wide) and doing lots of
> screens on asymptomatic people will restrict our ability to do PCR testing
> not only on people with suspect coronavirus infection, but all other
> diagnostics done by PCR (because they have a common reagent)
>   2) the testing is more invasive as it involves having a swab stuck up your
> nose until your eyes water and isn't likely to be highly popular  (
> https://youtu.be/DVJNWefmHjE)
>   3) The performance of the PCR test isn't well established in asymptomatic
> people
> 
> Three is the one worth expanding on the most.  PCR based-tests are
> generally very sensitive (unlikely to miss an infection if the DNA is
> there) and specific (unlikely to have false positives if it isn't). But
> when you're testing for a disease which is uncommon (which the epidemiology
> and the second Doherty modelling study strongly suggests it is), even very
> sensitive and specific tests can have low positive predictive values (
> https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) - ie
> the likelihood that a positive test is the real deal, or that the negative
> test is actually legit.
> 
> In addition to the technical performance of the test, there is also the
> issue of biological negative tests providing false reassurance.  For
> example, if someone is in the incubation period of the illness (ie have
> come into contact the disease but have not yet developed symptoms) there
> are three phases.
> 
> The incubation period proper in which they don't have symptoms and tests
> are negative because the virus hasn't manifested yet. (correctly negative
> test, but will develop symptoms and infectivity soon)
> The pre-symptomatic phase of the infection (this is what people worry about
> when they talk about "asymptomatic transmission" - which by the way is very
> unlikely at a population level to lead to large transmission clusters; most
> infections come from symptomatic patients) - this individual if tested
> would have a positive test, but no symptoms (this is what you're looking
> for when you're talking about screening)
> Then there are symptomatic patients (positive test, based on the
> sensitivity, and symptoms).
> 
> There's also talk about screening with CT scans which are a bad idea
> because the findings are very non-specific (ie: quite a lot of things look
> like COVID changes; in a low-COVID prevalence environment like ours, it
> would have a very poor negative predictive value) and because it has
> limited availability and involves radiating people which is probably not
> justified by how good a screening tool it is.   The maths for CT screening
> would probably be quite different in say, New York where there is lots and
> lots and lots of COVID, so the positive predictive value is probably pretty
> good, even though the specificity of the test isn't that high.
> 
> Now I have to get back to work, so I'll leave it there.
> 
> Trent.

-- 
Roger Clarke                            mailto:Roger.Clarke at xamax.com.au
T: +61 2 6288 6916   http://www.xamax.com.au  http://www.rogerclarke.com

Xamax Consultancy Pty Ltd      78 Sidaway St, Chapman ACT 2611 AUSTRALIA 

Visiting Professor in the Faculty of Law            University of N.S.W.
Visiting Professor in Computer Science    Australian National University