Shift, Scale and Restart Smaller Models to Estimate Larger Ones: Agent-based Simulators in Epidemiology


Agent-based simulators are a popular epidemiological modelling tool to study the impact of various non-pharmaceutical interventions in managing an evolving pandemic. They provide the flexibility to accurately model a heterogeneous population with time and location varying, person specific interactions. To accurately model detailed behaviour, typically each person is separately modelled. This however, may make computational time prohibitive when the region population is large and when time horizons involved are large. We observe that simply considering a smaller aggregate model and scaling up the output leads to inaccuracies. In this talk we primarily focus on the COVID-19 pandemic and dig deeper into the underlying probabilistic structure of an associated agent based simulator (ABS) to arrive at modifications that allow smaller models to give accurate statistics for larger models. We exploit the observations that in the initial disease spread phase, the starting infections behave like a branching process. Further, later once enough people have been infected, the infected population closely follows its mean field approximation. We build upon these insights to develop a shifted, scaled and restart version of the simulator that accurately evaluates the ABS’s performance using a much smaller model while essentially eliminating the bias that otherwise arises from smaller models.

About the speaker

Sandeep Juneja Sandeep Juneja is a Senior Professor in the School of Technology and Computer Science at the Tata Institute of Fundamental Research, in Mumbai. He received his B. Tech. in Mechanical Engineering from IIT Delhi (1989) and his M. S. in Statistics and Ph.D. in Operations Research from Stanford University (1993). He is a recipient of IBM faculty partnership award in the year 2001- 02 and he co-authored papers that received best paper awards at 4th as well as 6th International ICST Conference on Performance Evaluation Methodologies and Tools (in 2009 and 2012). His research interests lie in applied probability including in sequential learning, mathematical finance, Monte Carlo methods, and game theoretic analysis of queues. Lately, he has been involved in modelling Covid-19 spread in Mumbai, and in mathematics of certain epidemiological models.