[Population Modeling] PopModWkGrpIMAG-news Digest, Vol 27, Issue 1

Sun Jan 8 16:32:28 PST 2017

Welcome Nathan.  Great short introduction!!

Typed with two thumbs on my iPhone.  (757) 318-0671 
>> “Where there is much desire to learn, here of necessity will be much arguing, 
>> much writing, many opinions; for opinion in good men is but knowledge in the making.”
>> John Milton (1608 – 1674)
>> 
>> 

> On Jan 8, 2017, at 15:00, popmodwkgrpimag-news-request at simtk.org wrote:
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> Today's Topics:
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>   1. Description of research (Nathan Geffen)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Sun, 8 Jan 2017 16:48:22 +0200
> From: Nathan Geffen <nathangeffen at gmail.com>
> To: popmodwkgrpimag-news at simtk.org
> Subject: [Population Modeling] Description of research
> Message-ID:
>    <CAB1iTxAfC1ATOgH8XVL1aey-ABuamRdnQBmu=6vbVBQF8082XA at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
> 
> Dear all
> 
> Jacob has asked me to describe the population modelling work I'm doing with
> Stefan.
> 
> In simulations of sexually transmitted infections how do we match agents
> with each other in sexual partnerships, and how does this affect the
> outcomes of simulations? In the simplest differential equation models, it
> is implied that all members of the population are equally likely to pair
> with each other (see for example perhaps the most cited HIV population --
> and maybe any STI  -- model ever, Granich et al 2009
> <http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(08)61697-9/fulltext>
> which
> implicitly makes this assumption - its simplicity is part of its appeal).
> This is similar to the simplest simulations where any member of the
> population is equally likely to pair with any other. But if we have
> information (or assumptions) about who people are actually more likely to
> partner with, perhaps this will give us more accurate or realistic models
> and better insights into how infections spread.
> 
> The problem can be stated like this: given a set of agents, each
> representing a person or animal seeking a (sexual) partnership, find a set
> of partnerships such that every agent is paired with one and only one other
> agent. We need this assumption too: for any two agents a and b we have a
> distance function that calculates how realistic it is that a and b can
> become sexual partners. This distance function creates an ordering such
> that for any two potential partners, b and c, of a, we can calculate
> whether b or c is the more likely partner (perhaps with some arbitrary
> tie-breaking method). For example, the distance function might calculate
> that a 25 year-old heterosexual male living in Berlin is more likely to
> partner with a 25-year-old heterosexual female living in Berlin than a 45
> year-old gay male living in Munich. but the latter in turn is more likely
> to partner with a 40-year-old gay male living in Munich than either of the
> first two individuals.
> 
> Now there is actually an algorithm called Blossom V
> <http://pub.ist.ac.at/~vnk/papers/BLOSSOM5.html> which given a set of such
> agents will calculate optimally all the sexual partnerships, such that the
> average distance for all the partnerships is minimised (It's called a
> minimum cost perfect matching algorithm). It has two disqualifying
> practical problems however. 1. It is very, very slow, far too slow for a
> useful role in agent based models except for perhaps one that only needs to
> be run occasionally on a small population on fast hardware. 2. It is
> deterministic which is actually a poor feature for most stochastic
> agent-based models. (It's also complicated.)
> 
> Stefan and I have developed matching algorithms that try to balance speed
> and quality. We are currently exploring how this affects the outcomes of a
> simulation. If we find the effect is substantial on say such bottom-line
> outputs as overall prevalence, it might mean we have to increase our
> scepticism/caution of simple models like Granich et al. However, if it
> doesn't have a big effect then perhaps our confidence in their outputs
> might be increased.
> 
> Here is preliminary work I did on this topic:
> http://nathangeffen.webfactional.com/partnermatching/partnermatching.html
> 
> And this paper has interesting findings on the different outcomes modelling
> various STIs  in South Africa using equation-based (Frequency-dependent)
> models versus microsimulations (network models).
> https://www.ncbi.nlm.nih.gov/pubmed/26859800
> 
> Stefan and I are working on at least two additional papers on this topic,
> one of which is under peer-review.
> 
> Regards
> Nathan Geffen
> PhD student
> University of Cape Town
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