[Vp-reproduce-subgroup] [Vp-integration-subgroup] White paper revision

[Jacob Barhak]

Well William,

Jonathan and you criticize some work done. However,  in a larger
perspective,  let us remember that although disease models existed for
about a century, this is still emerging technology.

I have been working in the field for over a decade and I am a great critic
of our current state and believe we can do better.

Our technologies are still not good enough for prediction.  We really
cannot predict,  moreover we still cannot fully explain the phenomena we
see computationally. So every technique will have difficulties in
forecasting.

And when I write we,  I also include myself. I really wish we can do
better.

This does not mean people should stop trying. So instead of trying to
dismiss other methods,  perhaps we should try to suggest how to learn from
mistakes and improve things for the future.

We have the collective responsibility of preparing tools for the future.
And I think a positive tone in our message of what to do to improve things
will do better than pointing fingers in this situation.

I think we were able to do it ok in the paper so far,  for each deficiency
we were able to show a potential solution.  Perhaps we should keep that
concept.

The credibility section in the paper attempts to address the issue you
discuss in a subtle way. It hints that we should do better as modelers so
that regulators will trust us. I think it serves the purpose you are both
aiming for. Yet if you think a stronger message is needed,  the paper is
now open for revisions and for discussions and you are welcome to make
those.

          Jacob






On Mon, May 17, 2021, 03:13 William Waites <wwaites at ieee.org> wrote:

> > Also, to motivate the focus on credibility, it could be helpful to cite
> an instance where the pandemic models were wrong or where lack of
> credibility (trust) inhibited use of a model. For the former, one example
> that comes to mind is the widely cited IHME model which was substantially
> off in the Spring.
>
> Another good example is Friston’s dynamic causal model which is
> interesting in itself and apparently a useful technique in neuroscience but
> does badly for infectious disease, famously leading to the assertion that
> the reason the model results’ divergence from reality must be do to
> mysterious “epidemiological dark matter”. The debunking of this sucked up a
> lot of time from people who know better…
>
>
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