v2 wiki outline

Stochastic Network Model Implementation

• Compartment model
  • Abstracting a customizable compartment model
    • Compartments (States)
    • Transitions
    • Transmissibilities
    • Susceptibilities
    • ...
  • [Separate page?] Specifying custom compartment models
    • JSON
    • Compartment model builder
    • (pre-builts)
• Contact networks
• Individual heterogeneity (in params)
• Stochastic dynamics
  • (Prime equation, then build up)
  • Transmission model
    • Global
    • Local (and mixedness)
      • Connectivity Correction Factor (loc?)
    • Exogenous (and openness)
    • Isolation ("active" in networks)
  • Gillespie implementation(s)

Interventions

• Simulation loop (logic/structure of when/how interventions applied)
  • Simulation/intervention time
  • Introductions (eg timing)
  • Interventions (eg timing)
• Compliances (loc)
• Implementations of interventions
  • Isolation
    • (intro conditions for entering isolation (onset, testing, etc))
    • (recall how isolation works from model description)
    • (conditions for exiting isolation)
  • Testing
    • How a test works
      • Delays
      • Sensitivity (temporal)
      • ...
    • Onset
    • Proactive (random)
    • [Tracing]
    • Groupmates
    • De-isolation
    • (others?)
  • [Tracing]
  • Social distancing
  • Masking
  • Vaccines

COVID

• Pre-built models
• Pre-built scenarios

Networks

• Generic (FARZ)
• Community
• Primary school
• Secondary school

Model

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Compartment model

Consider a single locus with a set of alleles in a population of haploid organisms that reproduce asexually in discrete non-overlapping generations. Let the number of offspring possessing allele (A_i) in generation (t) be (n_i^{t}), and let the frequency of allele (A_i) in generation (t) be (p_i^t = n_i^t / N^t), where (N^t = \sum_{i} n_i^t) is the total size of the population in generation (t).

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