Conférence
Notice
Lieu de réalisation
Centre Inria d'Université Côte d'Azur
Langue :
Anglais
Crédits
Jonathan Ozik (Intervention)
Crédit image : Centre Inria d'Université Côte d'Azur
Détenteur des droits
Centre Inria d'Université Côte d'Azur
Conditions d'utilisation
CC BY-NC-ND (Attribution - Pas d’Utilisation Commerciale - Pas de Modification)
DOI : 10.60527/ytq1-0m54
Citer cette ressource :
Jonathan Ozik. Inria. (2023, 30 mars). Integrating Simulation, Machine Learning, and High-performance Computing to Support Public Health Decision Making. [Vidéo]. Canal-U. https://doi.org/10.60527/ytq1-0m54. (Consultée le 7 janvier 2026)
RF-Inria-logo-RVB
Inria - Institut national de recherche en sciences et technologies du numérique
S'abonner
Contacter

Integrating Simulation, Machine Learning, and High-performance Computing to Support Public Health Decision Making

Réalisation : 30 mars 2023 - Mise en ligne : 11 avril 2023
  • document 1 document 2 document 3
  • niveau 1 niveau 2 niveau 3
Descriptif

The COVID-19 pandemic has highlighted the need for detailed modeling approaches that can capture the many complexities of emerging infectious diseases. In response, our group developed CityCOVID, a distributed agent-based model capable of tracking COVID-19 transmission in large, urban areas. Through partnerships between Argonne National Laboratory, the University of Chicago, the Chicago Department of Public Health, and the Illinois COVID-19 Modeling Task Force we combined multiple data sources to develop a locally informed, realistic, and statistically representative synthetic agent population, with attributes and processes that reflect real-world social and biomedical aspects of transmission. We model all 2.7 million individual residents of Chicago, as they go to and from 1.2 million different places according to their individual hourly schedules. In this presentation I will describe how we integrated agent-based modeling (Repast HPC, ChiSIM), machine learning (IMABC, MOBO), and high-performance computing (HPC) technologies (Swift/T, EMEWS) in support of public health stakeholders. I will describe our efforts in translating the outputs of our HPC-generated analyses to support public health decision making in understanding, responding to and planning for the current and future population health emergencies. I will also describe other areas of application where our integrated approaches have and are continuing to be applied. I will conclude with thoughts on future areas of interest.

Intervention / Responsable scientifique
Thème
Discipline :

Sur le même thème