/ncm

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How to use this knowledge

  • Figure out the time line
  • Using the network of the crisis model it in the world
  • Predict when the crisis breaks

21: Epidemics

Diseases and the Networks That Transmit Them

The pathogen and the network are closely intertwined: enven within the same population, the contact networks, for two different diseases can have very different structures, depending dieseases' respctive modes of transmissions.

  • STDs versus the Cold

Properties of Patterns of Epidemics spread through groups of people

  1. Determined by:

    1. Pathogen

      • contagiousness
      • the length of its infectious period
      • severity

    2. The network structure (Of the population affected)

      1. The social network (who knows who)

      2. The contact network

Connections to the Diffusion of Ideas and Behaviors -and Differences

The assumption is that when two people are directly linked in the contact network, and one of them has the diesease, there is a given probablility that he or she will pass it to the other. We have no simple models - so we abstract away the mechanism by modeling it as random. This is the lack of decision making and modeling it as random odds of infection is the concrete difference between modeling of a biological versus a social contagion.

  • Spread from person to person
  • What about surfaces?
  • No decision making process in how a diesease infects a person
  • Infection process is unobservable

Models based on random process in network/graphs are used to model Epidemics

Insight Basic Probablistic (random infection) model provide on Epidemics

  1. Qualitative Aspect involving Spread of Diesease

    1. Sychronization

    2. Timing

    3. Concurrency in Transmission

      Deeper unknowns at this point

      Randomized models can also sometimes be useful in studing social contagion, particularly in cases where the underlying decision process of individuals are hard to model and hence more usefully abstracted as radom processes. Often the two approaches - decision based and probablistic produce related reults and can sometimes be used in connection.

      Understanding the relationship between these methodlogies at a deeper level is an interesting direction for further research.

      MB Idea: This probably where the concepts from Judea Pearls Causality start to connect. Causal Contact Networks (CCN) could be constructed to better understand how different populations behaviour (actions taken not taken) affected the outcome of the diesease.

      Use dowhy module to model and provided "attributions" to the vertices(nodes) and edges Use git to model various branching scenarios.