Colloquium on Computational Social Science/Computational Data Sciences

Epidemics with Space, Movement, and Asymptomatic Spreading - Blake LeBaron, Abram L. and Thelma Sachar Chair of International Economics at the International Business School, Brandeis University

This paper describes a simple agent-based model for disease spread and epidemic dynamics.  The model is stylized and simple, yet tries to capture

some of the core features that are understood to be part of the COVID-19 progression. The model is strongly spatial, but is built in a basic featureless environment to allow for spatial-temporal patterns to emerge endogenously. 

A key driving force of many of the results is the presence of asymptomatic agents who are contagious and spreading the disease more freely than symptomatic types due to differing levels of mobility.  Simulations over several sets of parameters display richer, more realistic

dynamics than standard benchmark (SIR) models which can be replicated as a special case.  Many of the results show dramatic differences between the fully spatial, and SIR type mechanisms.

Finally, the model is used to explore key policy interventions including movement restrictions,

testing/contact tracing, and vaccination.  It is argued that models of this type should be part of the basic toolkit for exploring policy, and generating critical thought experiments for use in parallel with other more detailed, empirically rich models on the forefront of research on epidemic dynamics.

Speaker Bio: Blake LeBaron is the Abram L. and Thelma Sachar Chair of International Economics at the International Business School, Brandeis University. LeBaron was at the University of Wisconsin from 1988-1998, and also served as director of the Economics Program at The Santa Fe Institute in 1993. He was a Sloan Fellow, and is a recent recipient of the Market Technician’s Association Mike Epstein award. He recently spent two years as a visiting researcher with the Office of Financial Research in the U.S. Treasury Department. He currently directs the Masters of Science in Business Analytics program at Brandeis, and is part of a Brandeis interdisciplinary research and teaching group interested in modeling dynamics in a wide range of fields. LeBaron’s research has concentrated on the issue of nonlinear behavior of financial and macroeconomic time series. He has been influential both in the statistical detection of nonlinearities and in describing their qualitative behavior in many series. LeBaron’s current interests are in understanding the quantitative dynamics of interacting systems of adaptive agents and how these systems replicate observed real world phenomenon. Also, LeBaron is interested in understanding some of the observed behavioral characteristics of traders in financial markets.

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