Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure

Biology – Quantitative Biology – Populations and Evolution

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

We consider the edge-based compartmental models for infectious disease spread introduced in Part I. These models allow us to consider standard SIR diseases spreading in random populations. In this paper we show how to handle deviations of the disease or population from the simplistic assumptions of Part I. We allow the population to have structure due to effects such as demographic detail or multiple types of risk behavior the disease to have more complicated natural history. We introduce these modifications in the static network context, though it is straightforward to incorporate them into dynamic networks. We also consider serosorting, which requires using the dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Edge-Based Compartmental Modeling for Infectious Disease Spread Part III: Disease and Population Structure will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-35876

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.