Planning as Inference in Epidemiological Dynamics Models

Abstract

In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution over controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Among other things, we illustrate the use of a probabilistic programming language that automates inference in existing simulators. Neither the full capabilities of this tool for automating inference nor its utility for planning is widely disseminated at the current time. Timely gains in understanding about how such simulation-based models and inference automation tools applied in support of policy-making could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 31, 2022
Source ID
10.3389/frai.2021.550603

Entities

People

  • Adam Ścibior
  • Andrew Warrington
  • Boyan Beronov
  • Christian Weilbach
  • Duncan Campbell
  • Frank Wood
  • John Grefenstette
  • S. Ali Nasseri
  • Saeid Naderiparizi
  • Vaden Masrani
  • William Harvey

Organizations

  • Defense Advanced Research Projects Agency

Tags

Readers

  • Computer Science.
  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy