Webinar: Temporal Epidemic Modeling with Bayesian Networks
Recorded on April 2, 2020.
Overview
Compartmental models represent the most common approach for characterizing the development of an epidemic. In an earlier webinar, we introduced a compartmental S-I-R-D model and created a highly-simplified Bayesian network to illustrate the principles. Given its great relevance, we believe the topic warrants a more detailed explanation beyond the initial "toy model."
For the purpose of this BayesiaLab Tech Talk, we present a more comprehensive S-E-I-R-D model. Each letter denotes a compartment (or state) of individuals in a population:
- S: number of susceptible
- E: number of exposed
- I: number of infected
- R: number recovered
- D: number of dead
Additionally, we further differentiate within the states of exposed and infected to account for contagiousness and disease severity.
In standard models, a set of differential equations describes how individuals move between the compartments/states. In this Tech Talk, we implement the differential equations as probabilistic, temporal relationships between nodes in a Bayesian network.
While we often use fictional values in webinars to emphasize methodology over the subject matter, we take a different approach here: The numerical values and parameters presented in this Tech Talk are derived from current COVID-19 observations in France. As a result, the model attempts to represent the actual pandemic situation in France and forecast the pandemic progression.
Presentation Video
Presentation Materials
S2E3IRD.pdf (opens in a new tab)
S2E3IRD_France_h2_Scenarios.xbl (opens in a new tab)
SEIRD_d.xbl (opens in a new tab)
SEIRD_h.xbl (opens in a new tab)
OptimizationSEIRD_OptADR.py