Contact matrices

  • Contact matrices quantify the mixing patterns between different population groups
  • socialmixr provides tools to estimate contact matrices from survey data
  • Contact matrices can be used in various epidemiological analyses, from calculating \(R_0\) to modeling interventions
  • Proper normalization is crucial when using contact matrices in transmission models

Simulating transmission

  • Disease trajectories can be generated using the R package epidemics
  • Uncertainty should be included in model trajectories using a range of model parameter values

Choosing an appropriate model

  • Existing mathematical models should be selected according to the research question
  • It is important to check that a model has appropriate assumptions about transmission, outbreak potential, outcomes and interventions

Modelling interventions

  • The effect of NPIs can be modelled as reducing contact rates between age groups or reducing the transmission rate of infection
  • Vaccination can be modelled by assuming individuals move to a different disease state \(V\)

Comparing public health outcomes of interventions

  • A counterfactual (baseline) scenario must be clearly defined for meaningful comparisons
  • Scenarios can be compared using both visualizations and quantitative measures
  • The outcomes_averted() function helps quantify intervention effects
  • Parameter uncertainty should be considered in intervention analysis

Comparing vaccination strategies

  • Herd immunity is a indirect effect of vaccination programs
  • Targeted vaccination programs have benefits when there is heterogeneity in contacts
  • The timing of implementation of vaccination programs and NPIs can result in very different disease trajectories

Modelling disease burden

  • Transmission models should include disease burden when it affects onward transmission
  • Outputs of transmission models can be used as inputs to models of burden
  • The Gamma distribution is commonly used to model delays in disease progression
  • Convolution is a powerful tool for estimating disease burden from transmission model outputs