Stochastic Models of the Spread of Disease and Information on Networks

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Stochastic Models of the Spread of Disease and Information on Networks

 04 - 08 Jul 2016

ICMS, 15 South College Street Edinburgh
  • Rick Durrett, Duke University
  • Thomas House, University of Manchester
  • Malwina Luczak, Queen Mary University of London
  • Pieter Trapman, Stockholm University

About:

Despite major advances due to vaccination, hygiene and pharmaceutical interventions, infectious diseases continue to pose a serious threat to public health. Notable examples of epidemics during recent decades include the HIV epidemic, the SARS outbreak in 2002-04, the 2009 H1N1 influenza pandemic, and more recently Ebola. It is of utmost importance to understand how infectious diseases spread through populations, how the population structure influences the spread, and what disease control measures are effective.

 

The main themes were:

  • Spread of epidemics on dynamic networks

  • Near-critical epidemics

  • Persistence of epidemics

  • Spread of information and opinions

  • Connecting mathematical models to real-life epidemics through data fitting

Mathematically, models of disease and information spread have a wealth of interesting features, and understanding them better will advance the field. Currently, health systems in developed nations are struggling under pressure caused by ageing populations and resource limitations. It is therefore imperative to reduce the economic and human burden of infectious diseases as efficiently as possible, and modelling can play a key role in this optimisation

Speakers

  • Frank Ball, University of Nottingham - Inference for Emerging Epidemics on Networks with Household Structure

  • Philip O'Neill, University of Nottingham - Bayesian Inference for Epidemic Models via Likelihood Approximation 

  • Peter Neal, Lancaster University - A Household SIR Epidemic Model Incorporating Time of Day Effects

  • Rosalind Eggo, London School of Hygiene & Tropical Medicine - Epidemic Size and the Role of Population Immune History in Influenza

  • Pierre-Andre Maugis, University College London - Network Analysis and Non-Parametric Statistics

  • David Sirl, University of Nottingham - Vaccine Allocation in Network Epidemic Models

  • Tom Britton, Stockholm University - Inferring R_0 in Emerging Epidemics - the Effect of Common Population Structure is Small 

  • Viet Chi Tran, Université Lille 1 - Nonparametric Adaptive Estimation of Order 1 Sobol Indices in Stochastic Models, with an Application to Epidemiology

  • Mick Roberts, Massey University - An Epidemic Model with Noisy Parameters

  • Odo Diekmann, University of Utrecht - The Renewal Equation for the Volz Variable

  • Joel Miller, Institute for Disease Modeling - Modeling Disease Spread with Birth, Death and Concurrency

  • Elizabeth Buckingham-Jeffrey, University of Warwick - Gaussian Process Approximations of the Stochastic SIR Model

  • TJ McKinley, University of Exeter - Combining Gaussian Processes and ABC for Inference in Complex Infectious Disease Models: with Application to HIV in Uganda

  • Nakul Chitnis, Swiss Tropical and Public Health Institute - Modelling Rabies Elimination in an African City

  • Mirjam Kretzschmar, University Medical Centre Utrecht - Use of Network Models for Assessing the Impact of Interventions for Chlamydia Infections

  • Ian Hall, Public Health England - Developing an Emerging Disease Analysis Toolbox

  • Iain Barrass, Public Health England - Modelling to Support a UK Pandemic Influenza Exercise

  • David Aldous, University of California - A General SI Epidemic and a Framework for Imperfectly Observed Networks

  • Fabio Lopes, Universidad de Chile - Extinction Time for the Weaker of Two Competing SIS Epidemics

  • Damien Clancy, Heriot-Watt University - Approximating the Time to Endemic Fade-Out

  • Graham Brightwell, London School of Economics - The SIS Logistic Epidemic

  • Ayalvaid Ganesh, University of Bristol - Optimal Control of a Contact Process

  • Christel Kamp, Paul-Ehrlich-Institut - Epidemic Spread on Weighted Networks

  • Tobias Mueller, University of Utrecht - A Hyperbolic Model of Complex Networks

  • Joshua Ross, University of Adelaide - Characterising Pandemic Impact from Data Collected During First Few Hundred Studies

  • Istvan Kiss, University of Sussex - Generation and Analysis of Networks with a Prescribed Degree Sequence and Subgraph Family: Higher-Order Structure Matters

  • Kieran Sharkey, University of Liverpool - Defining Prevalence and Invasion Probability for SIS Dynamics on Finite Networks

  • Edward Hill, University of Warwick - Spreading of Healthy Mood in Adolescent Friendship Networks 

  • Nicolas Rivera, King's College London - The Linear Voting Model

  • Eric Foxall, Arizona State University - Dynamics of the Naming Game on the Complete Graph

  • Denis Mollison, Heriot-Watt University - Challenges in Representing Spatial Structure

  • Tatyana Turova, Lund University - Random Geometric Graphs