Edinburgh 14-18 May 2007

`Spatio-temporal and Network Modelling of Diseases'

Slides and/or Abstracts of Talks

• Tommi Asikainen: ECDC and Modeling of Infectious Diseases (3.9 MB)

• Frank Ball: From Individuals to Populations (1.8 MB)

• Shweta Bansal: Does Individual Behavior Matter? Network and Homogeneous Models

• Tom Britton: Networks, epidemics and vaccination: a review

• Damian Clancy: E. coli 0157 in a Dairy Herd

• Derek Cummings: Strategies for Looking for Patterns in Spatio-temporal Data

• Peter Dodd: Abstract

  We argue that the large-dimensional dynamical systems which frequently occur in biological models can sometimes be effectively reduced to much smaller ones. We illustrate this by applying projection operator techniques to a mean-field model of an infectious disease spreading through a population of households. In this way, we are able to accurately approximate the dynamics of the system in terms of a few key quantities greatly reducing the number of equations required. We investigate linear stability in this framework and find a new way of calculating the familiar threshold criterion for household systems.

  Reference DOI: 10.1098/rsif.2007.0231

• Neil Ferguson: Disease Challenges of the 21st Century: Challenges for Modelling

• Luca Giuggioli: Beyond the Diffusion Approximation

• Katie Hampson: Reconstructing networks of rabies transmission (1.9 MB)

• Rowland Kao: The Council of Elrond: A Data-Based Commentary on R₀ on Social Networks

• Theo Kypraios: Avian Influenza, and Foot and Mouth Disease (part of Phil O'Neill's talk).

• Glenn Marion: Relating Contact Networks to Underlying Biological processes.

  Abstract: Although often studied in their own right, contact networks are generated by social and biological processes. In contrast to static network representations the contact structure arising from such processes will be highly dynamic and connections between individuals will be directed and vary in strength. Although potentially complicating any analyses, an understanding of the impacts of such processes on disease dynamics may provide deeper insights and suggest alternative control strategies. Although in principle one could model behavioural and epidemic processes jointly, here we suggest a simplifying approach in which the underlying (dynamic) network is first derived from a behavioural model. As an applied example we study the network of indirect contacts between individuals induced (on a daily and a weekly basis) by an individual-based spatially explicit model of foraging and avoidance behaviour in grazing animals. The derived contact structures are relevant to faecally mediated infections, but only initial results describing the contact network are presented here: a high turnover in the daily (and weekly) numbers of individual contacts reveals the dynamic nature of the contact network; and there is considerably greater variation in individual exposure than in numbers of individual contacts.

• Denis Mollison: Small worlds and giant epidemics: an overview of s-t and network models (3.8 MB)

• Phil O'Neill: Statistical Inference for Spatial and Structured Population Epidemic Models

• Lorenzo Pellis: Threshold Parameters for a Model With Households and Workplaces: Mathematical Models for Emerging Infections in Socially Structured Populations

• Steven Riley: Spatial Networks of Individuals, Households and Workplaces

• Mick Roberts: Spatio-temporal and Network Modelling of Diseases (9.1 MB)

• Lisa Sattenspiel: Spanish Influenza on the Island of Newfoundland (2.6 MB)

• Gianpaolo Scalia Tomba: The Statistics of Generation Times in Epidemic Spread

• Christian Steglich: Statistical Inference for Jointly Evolving Networks and Actor Characteristics

• Pieter Trapman: Construction of Random Graphs With Given Clustering

Top of page

Workshop home page

Maintained by Denis Mollison denis@ma.hw.ac.uk

31st July 2007