[Population Modeling] Soliciting contributions for a population modeling paper

[Shweta Bansal]

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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