Probability and pandemics

Abstract: In one of the simplest epidemic models, one lets $p_n$ denote the number of new infections during week $n$ and assumes that (during the early stages of the epidemic) $p_{n+1} = R_0 p_n c_n$ where $c_n$ measures the "fraction of usual contact" that takes place between people during the nth week. Within this simplistic model, intermittent strategies (taking $c_n$ small some weeks and large other weeks) lead to lower infection rates than consistent strategies with the same total amount of contact.

But what happens if one considers a more realistic disease model (such as a SEIR model with multiple compartments, or a network-based model, with empirically based distributions for incubation and infection times) and also tries to assign utility to the amount of contact in a more realistic way (accounting for crowding, social networking and other issues)? What factors cause intermittent strategies to outperform constant strategies? I will discuss a health policy paper I recently co-authored with a team of public health researchers that explores this question for a range of simple examples.

mathematical physicsprobability

Audience: researchers in the topic

Probability and the City Seminar

Series comments: The Probability and the City Seminar is organized jointly by the probability groups of Columbia University and New York University.

Video recordings of talks are posted online at www.youtube.com/channel/UC0CXjG-ZSIZHy0S40Px2FEQ .