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Webinar Series — Reasoning Under Uncertainty (Part 3): Epidemic Modeling with Temporal Bayesian Networks

 

"Flatten the curve" has become the motto of the moment. From policymakers and the media, we hear that it's all about keeping the peak of COVID-19 infections below the maximum capacity of healthcare systems. 

There appears to be a consensus regarding the default shape of the curve and that an intervention, e.g., social distancing, would reduce the peak of infections while extending the duration of the epidemic, i.e., the curve flattens.

Given that the curve does resemble a normal distribution, one may speculate that there is an underlying function with "disease" parameters. As it turns out, the curve is impractical to characterize with a closed-form expression. Instead, the curve is the result of a simulation of a compartmental epidemic model that is defined by a set of differential equations. 

The simplest model of this kind is the so-called SIR model. S stands for the number of susceptible, I for the number of infectious, and R for the number recovered. Infection and recovery rates determine how individuals in a population move between the "compartments," i.e., S->I and I->R, from one time step to the next. 

While the infection and recovery rates for a typical flu season are known, COVID-19 is novel, and these parameters have yet to be estimated from observations. However, health policy decisions may be required long before estimates can be established with any degree of certainty. It is a prototypical situation of reasoning under uncertainty.

In this webinar, we show how to implement the SIR model as a temporal Bayesian network and how to use BayesiaLab for simulating the curves as a function of different infection and recovery rates. In this context, we also demonstrate the Bayesia Expert Knowledge Elicitation Environment for combining experts' assumptions to parameterize the Bayesian network model. 


Please also check out our next webinar in the series about reasoning under uncertainty:

Optimizing Health & Treatment Policies with Bayesian Network on April 23, 2020

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