[Population Modeling] Soliciting contributions for a population modeling paper

[Jacob Barhak]

Greetings to all the newcomers to the list,

Since many of you joined after my initial request, I am repeating my
request for contributions.

We are trying to write a paper that will define our work by examples. I
previously asked from every person in the list to write one paragraph that
describes their population modeling work and add a few references to their
own work.

Each person knows their work best and it should not take more than a few
minutes to write a paragraph and point to previous work done. And do write
fresh new text rather than copy existing text to avoid copyright issues
when I assemble all those contributions together to a paper under the name
of all contributors.

The current target for the paper is one of the SCS conferences: WinterSim,
SpringSim, SummerSim, Yet other venues are possible and I am open to
suggestions.

So Please after receiving this email, please take a few minutes write a
short blurb about what you do, and reply to this email.

I look forward to see more contributions.

            Jacob



On Mon, Dec 15, 2014 at 2:49 PM, Shweta Bansal <shweta at sbansal.com> wrote:
>
> Dear Jacob and colleagues,
>
> Please find below a blurb on network-based epidemic population modeling:
>
> Epidemics caused by the transmission of infectious agents are marked by
> variation. Heterogeneities in pathogens, host populations, and the
> interactions between them profoundly affect the dynamics of infection.
> There are several epidemiologically important sources of variability,
> including disease-independent host parameters—age, sex, contact rate, and
> compliance to public health recommendations—and disease-dependent host
> parameters—susceptibility to disease, transmission rate, mode of
> transmission, and recovery rate. The field of network epidemiology is a
> branch of infectious disease modeling which focuses on disease-independent
> heterogeneity in host contact rates [1]. That is, the number of potentially
> disease-causing interactions can vary widely across a host population.
> Although this is only one aspect of host heterogeneity, it is an important
> one.  Variability in contact patterns can stem from social structure, age,
> sex, spatial structure and behavioral differences. This heterogeneity is
> ubiquitous at many scales, can cause variability in fundamental disease
> parameters such as infectivity and susceptibility, and profoundly shape
> population-level disease dynamics. Incorporation of individual-level
> contact heterogeneity in population modeling of infectious disease spread
> has led to an understanding of super-spreading phenomenon [e.g. 2], of the
> preferential impact of past epidemics on future disease dynamics [e.g. 3],
> and the design of targeted intervention studies that can effectively
> control disease outbreaks [e.g. 4]. The field of network epidemiology has
> seen many advances in recent years, spurred by the availability of data and
> by the maturation of network theory; however, many challenges still remain
> [5].
>
> [1]  When individual behavior matters: homogeneous and network models in
> epidemiology
> Shweta Bansal, Bryan Grenfell, Lauren Ancel Meyers
> Journal of Royal Society Interface, doi: : 10.1098/rsif.2007.1100
>
> [2] Superspreading and the effect of individual variation on disease
> emergence
> JO Lloyd-Smith, SJ Schreiber, PE Kopp, WM Getz
> Nature, doi:10.1038/nature04153
>
> [3] The impact of past epidemics on future disease dynamics
> Shweta Bansal, Lauren Ancel Meyers
> Journal of Theoretical Biology, doi:10.1016/j.jtbi.2012.06.012
>
> [4]  The shifting demographic landscape of influenza
> Shweta Bansal, Babak Pourbohloul, Nathaniel Hupert, Bryan Grenfell, Lauren
> Ancel Meyers
> PLoS One, dii: 10.1371/journal.pone.0009360
>
> [5] Eight challenges for network epidemic models
> Lorenzo Pellis, Frank Ball, Shweta Bansal, Ken Eames, Thomas House,
> Valerie Isham, Pieter Trapman Epidemics, doi:10.1016/j.epidem.2014.07.003
>
> Many thanks to Jacob for compiling these.
>
> Best,
> Shweta Bansal
> Assistant Professor, Department of Biology
> Georgetown University
> http://bansallab.com
>
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>
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