Modelling of Virus Spread Using Information from Biased Testing

Dr Fanel Donea1

1CSIRO – Scientific Computing, Melbourne, Australia

This work aims to contribute to the fight against viruses by understanding how they may spread in reality, considering the limited information available from biased testing.

Non-medical methods (mathematical, computational, simulational) are a necessary supplement to direct medical approaches, especially for new viruses, such as covid-19, for which the medical research isstill incipient.

A combination of practical methods is used, including Monte Carlo simulations, agent-based modelling and analytic techniques, based on the classical SIR (susceptible-infected-removed) model. An immediate goal is to use establish an estimate of the real number of cases, in the situation where testing has not been randomised. The same techniques can then be applied in the future for conceptual proofs or disproofs for various claims that have been circulating in the media (effects of ignoring quarantine, the possibility of achieving herd immunity, the effectiveness of mass testing and others).


Biography:

Fanel Donea is a scientific software engineer in the Modelling and Data group of CSIRO’s Scientific Computing department. His current interests are in the fields of computational modelling and quantum computing. In a previous life, he worked in astrophysics, and  he obtained a PhD in Physics, studying accretion discs around black holes.

Modelling optimisation problems in JuMP using the Julia language

James Foster1, Frederik Geth1, Rahmat Heidarihaei1

1CSIRO – Energy, Newcastle, Australia

In this talk, we present a quick introduction to using the JuMP modelling language for clearly and efficiently expressing mathematical optimisation models.

Optimisation models can describe many problems where we are trying to make decisions while looking for the best option that either minimises costs or maximises benefits.

JuMP models have been written to solve a wide variety of domain-specific problems including those from transport, energy systems, finance and scheduling. Since JuMP is written in the open-source language Julia, research and development workflows can access Julia’s rich ecosystem covering data input/output, transformation and visualisation.

We will discuss concrete examples of problems, their solution via JuMP and its interface to high-performance algorithms, and how to address issues of working at scale for realistic model representations. Case examples will be given from modelling Australian energy systems.


Biography:

Dr James Foster is a member of the Energy Economic Modelling team, within the Energy Systems research program. He is a specialist in mathematical optimisation and data analytics applied to energy systems.

Classdesc: C++ Reflection for scientific computing

Dr Russell Standish1

1High Performance Coders

Reflection is the capability of querying aspects of an object’s type and structure at runtime, that normally is discarded as part of the compilation process. It is the key to automatically supporting object serialisation and automatically binding scripting languages to compiled models. In a typical scientific model, code supporting initialisation/configuration as well as checkpoint/restart can often be a significant chunk of the overall codebase, and creates a constant maintenance burden as the scientific model changes over time.

Whilst a number of programming languages support reflection intrinsically, popular high performance programming environments like C++ and Fortran do not. Classdesc is a system for automatically providing reflection capability to C++ programs that has been in constant use and development since 2000. This presentation covers the concept of a class descriptor, from which the name Classdesc is derived, and the existing descriptors for binary, XDR, JSON and XML serialisation, and automatic bindings for Python, Javascript and a REST API. The binary serialisation capability is leveraged to provide easy-to-program distributed memory parallel programming libraries based on industry standard MPI, called ClassdescMP and Graphcode.


Biography:

With a PhD in theoretical physics, Russell has had a long career in supporting high performance computing, and was the founding director of UNSW’s High Perfomance Computing Support Unit between 1997 and 2005. Since that time, he has established a software engineering company, High Performance Coders, with a range of clients in the public, private and academic sectors, specialising in high performance and scientific computing.

He also has academic research interests in complex systems and artificial life.

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