Computational Epidemiology

Tue, 26 Apr 2022 00:00:00 +0000

Global Pervasive Computational Epidemiology

Overview

NSF Expeditions Project

Organization

Sponsor: NSF
Institutions: University of Virginia
Period: 2020-2025

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Wed, 11 May 2022 00:00:00 +0000

https://www.osti.gov/servlets/purl/1855247

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

Sun, 01 Aug 2021 00:00:00 +0000

https://link.springer.com/article/10.1007/s41745-021-00253-1

COVID-relevant Scalable Computational Research Directions and Tools

Thu, 09 Apr 2020 00:00:00 +0000

This seminar conveys some directions and technical ideas being pursued by the Discrete Computing Systems Group of the Computer Science and Mathematics Division, in collaboration with others at the lab. Some of the scalable computational tools ready for applying to challenging computational problems are presented. Actual working codes ready for customization to COVID-related efforts are described, which are built for scaling to supercomputing platforms such as Summit.

Topics that are covered include:

A high resolution simulator, ExaCorona, that scales from laptops to leadership class supercomputers, is outlined that uses a discrete event model of virus spread, with probabilistically timed state transitions at the individual level across millions of individuals represented with arbitray geography and mobility characteristics.
A clonable simulation framework, CloneX, is introduced that enables millions of “what-if” scenarios to be executed rapidly on thousands of GPUs of Summit and similar supercomputers. An SEIR-based epidemiological model is outlined for numerous what-if simulations of disease spread that can be executed for country-scale populations like India’s, with ‘what-if’ scenarios, each varying in the outbreak points (hotspots), quarantines, vaccinations and hospitalizations.
A machine learning pipeline for the prediction of material structure properties directly from their neutron scattering profiles (development as part of the ExaLearn ECP co-design project). A brief overview of this system is provided, which is being applied for studies of new therapeutic targets and viral protein-structure-assisted drug design studies related to the COVID outbreak.
Network science methods are outlined for detecting information cascades in time varying large-scale social communication networks. We discuss its implications for detecting occurrence/response or epidemic related events from Twitter and similar global interaction systems.

Epidemiology | Kalyan Perumalla

Computational Epidemiology

Overview

Organization

Computer Science Research Needs for Parallel Discrete Event Simulation (PDES)

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

COVID-relevant Scalable Computational Research Directions and Tools