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

[Gilberto Gonzalez-Parra]

Briefly , some researchers are trying to make the distinction between
prediction and forecasting. I think this is a good point related to
modeling, credibility and reproducibility.



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Gilberto C. Gonzalez-Parra, Ph.D in Applied Mathematics.
Faculty of the Mathematics Department
New Mexico Tech, NM, USA.
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On Mon, May 17, 2021 at 4:33 AM Jacob Barhak <jacob.barhak at gmail.com> wrote:

> 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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