Hypothetical spread of infection through complex social networks
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Hypothetical spread of infection through complex social networks

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Presentation of a project done by a group students of the "Science of Complex Systems 2012" course at University of Kent, UK.

Presentation of a project done by a group students of the "Science of Complex Systems 2012" course at University of Kent, UK.
http://blogs.kent.ac.uk/complex/lectures-ph724/

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Hypothetical spread of infection through complex social networks Hypothetical spread of infection through complex social networks Presentation Transcript

  • HYPOTHETICAL SPREAD OFINFECTION THROUGH COMPLEXSOCIAL NETWORKSDaniel Stokes, Tom Downes and Mathew Seymour
  • OUTLINE Introduction  Why model the spread of disease? Early Models  SIR / SIS theory and model Network Models  Lattice, Random and Scale Free Newman’s Adaptation Scale Free Networks Simulations Future Research / Conclusions
  • INTRODUCTIONWHY MODEL THE SPREAD OF DISEASE Viral strains transmitted across the population by close proximity or contact We live in a highly connected world Determine risk Targeted Vaccination
  • MODELS
  • THE SIR MODEL: INFECTION RATE
  • THE SIR MODEL: EPIDEMIC THRESHOLD
  • NETWORK MODELS Adapted the SIS and SIR models to fit three different networks; a lattice, a random network, and a scale free network A lattice network is a regular grid of vertices In a random network, vertices are connected at random In a scale free network, the degree distribution of the vertices follows a power law The adapted models are stochastic, and designed to mimic the SIS and SIR models
  • NETWORKED SIS MODEL
  • NETWORKED SIR MODEL
  • NEWMANS SIR ADAPTATION SIR models adapted to fit a random graph using the ideal Bond percolation to carry the infection. Use a power law degree distribution to model the network connections β. Produced models with an epidemic threshold corresponding to an exponent > 3. Problem is that some people are still connected to all others.
  • CLUSTERING IN SCALE FREE NETWORKS Eguiluz and Klemm propose scale free networks do have an epidemic threshold! Real networks are more clustered than “random scale free networks” High clustering leads to low connectivity between connectivity between hubs BUT this is also unrealistic
  • SIMULATIONS Simulations use multiple models together Example: Episims
  • FUTURE RESEARCH New variables can be added to take into account more real world effects, for example: variable susceptibility, asymptomatic carriers and seasonality Can also adapt the types of network used, by making them dynamic, or perhaps introducing directed edges Combining these adaptions will improve the fit the model has with the real world