Outbreak analytics pipelines

  • Our vision is to have pipelines of R packages for outbreak analytics.
  • Our strategy is to create interconnected tasks to get relevant outputs for public health questions.
  • We plan to introduce package solutions and theory bits for each of the tasks in the outbreak analytics pipeline.

Read delays

  • Use {epiparameter} to access the systematic review catalogue of epidemiological delay distributions.
  • Use epidist_db() to select single delay distributions.
  • Use list_distributions() for an overview of multiple delay distributions.
  • Use discretise() to convert continuous to discrete delay distributions.
  • Use {epiparameter} probability functions for any delay distributions.

Quantifying transmission

  • Transmission metrics can be estimated from case data after accounting for delays
  • Uncertainty can be accounted for in delay distributions

Create a short-term forecast

  • We can create short-term forecasts by making assumptions about the future behaviour of the reproduction number
  • Incomplete case reporting can be accounted for in estimates

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 transmissibility of infection
  • Vaccination can be modelled by assuming individuals move to a different disease state \(V\)

Comparing public health outcomes of interventions

  • The counter factual scenario must be defined to make comparisons