Pandemic Flu

[Phil Henshaw-2]

When the interactions are complex it's fairly rare for people to look
carefully to see if the intended outcome actually occurs, for example
whether not your aid of people in trouble is actually competently
aiding them, or perhaps multiplying the people and their troubles.  
It's really great that you're asking the question this way, and about
one of the areas where there do seem to be a lot of resources put into
sophisticated growth system modeling.  

It seems to me that the key to this one is recognizing that all
interventions will necessarily be experimental and that the two main
modes of failure would be guessing wrong in the fist place and not
noticing very quickly in the second place.  Certainly using simpler
models that are easier to put though a variety of trials can help with
failure type 1, but failure type 2 is largely benefited by skillful
watching of what is happening and picking up on and correctly
identifying the divergences.  For that perhaps the need for better
observation technique takes precedence, rather than models and
planning.



> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA512
>
> Colleagues,
>
> Two questions re: pandemic flu simulation development. Thoughts?
>
> 1. Many of the models of pandemic flu are complex and model reality
to a
>  relatively high-level of granularity. I was thinking that a more
> simplistic model (SD with some ABM) might be suitable for creating a
> simulation that could be fed real-time data in the event of an actual
> pandemic. Data would come from surveillance with a goal of trying to
> determine (a) transmissibility and (b) antiviral resistance. Are
others
> working on more simplistic models of pandemic flu that can be linked
to
> surveillance data? What sort of surveillance data would be
appropriate
> to flow into such a simple model for trying to ascertain probable
values
> on the above factors?
>
> 2. I am struck by the fact that many simulations of pandemic flu
focus
> only on key performance indicators related to influenza-related
> outcomes, while ignoring the broader costs that would clearly result
> from targeted layered containment strategies. That is, could the
costs
> associated with trying to 'contain' a pandemic exceed the costs of
just
> letting it run it's course? So, for example, we close the schools to
> reduce the number of infected, but this results in many children
> becoming malnourished because they rely on free school lunches for a
key
> portion of their daily calories? How do we simulate the impact of
> well-meaning policies that decrease the spread of the disease (a
> benefit) but that also have tremendous costs when viewed outside the
> perspective of the spread of the disease? That is, how do we create a
> simulation to test policy decisions to ensure that well-meaning
policies

> don't actually make a tough situation even worse.
>
>
> - --
> Best regards,
>
> Justin Lyon
> M: [phone] (London, UK)
> E: justin at simudyne.com
> W: http://www.simudyne.com
> -----BEGIN PGP SIGNATURE-----
> Version: GnuPG v1.4.5 (MingW32)
> Comment: Using GnuPG with Mozilla - http://enigmail.mozdev.org
>
> iD8DBQFFvgNQhfoqghrmIrARCpduAJ9ybJDsZwCs5sD9xWJ/1fKyAHGqcwCgtX/Q
> jQGgLZDN1IdcA/YMeMyHlOw=
> =jwfl
> -----END PGP SIGNATURE-----
>
> ============================================================
> FRIAM Applied Complexity Group listserv
> Meets Fridays 9a-11:30 at cafe at St. John's College
> lectures, archives, unsubscribe, maps at http://www.friam.org
>
>

--
Phil Henshaw                       ????.?? ? `?.????
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~        
tel: [phone]                
e-mail: sy at synapse9.com          
explorations: www.synapse9.com