Agent-based modelling, simulation and forecasting of epidemics


The research will involve theoretical work in information theory and network science, as well as computer agent-based simulations. It will aim to reveal fundamental theoretical connections between (i) established methods of computational epidemiology (based on percolation theory and critical thresholds) and (ii) novel crisis forecasting methods which employ quantitative information dynamics on complex networks.The PhD will be supervised by Prof Philippa Pattison and Prof Mikhail Prokopenko. The applicant will join the team working on ARC Discovery Project "Large-Scale Computational Modelling of Epidemics in Australia" in The University of Sydney.  The project has strong connections with the Research Cluster on Complex Systems: 


Professor Mikhail Prokopenko

Research Location

Research Cluster on Complex Systems

Program Type



There is a growing need to better understand multiple epidemiological, socio-economic, and socio-ecological implications of emerging threats posed by infectious diseases, epidemics and pandemics. The accuracy of modern epidemiological models can be considerably improved by the integration of large-scale datasets and the explicit agent-based simulation of entire populations down to the scale of a single individuals, coupled with complex network-based modelling.  This should enable a more precise forecasting of critical phenomena typically emerging in complex health systems (including phase transitions, tipping points, epidemic peaks, and so on).  This PhD study will aim at developing (i) novel modelling framework for forecasting of critical phenomena during epidemic crises; and (ii) novel computational methods for large-scale agent-based simulation of disease diffusion based on complex networks, information theory and percolation theory.  

Additional Information

A full-time scholarship will be offered for exceptional students wishing to undertake a research doctorate degree. This Scholarship is awarded to students with a degree equivalent to an Australian Bachelor’s Degree with 1st Class Honours in Engineering or Science which included an independent research project and who ranked in the top 10% of the cohort.   Funding is valued at the APA rate ($26,288 in 2016 per annum, tax exempt), up to three years, subject to satisfactory progress.Applicants need to satisfy the eligibility criteria for PhD enrolment at The University of Sydney.  Backgrounds in applied mathematics, physics, computer science, and specifically in information theory, complex networks and statistical mechanics will be beneficial.   The successful applicant will demonstrate a strong commitment to academic research in the proposed field. He/she will have excellent written and oral communication skills, as well as demonstrated ability to program in MATLAB, Java, or C++, and will be willing to create and develop original approaches to tackle open questions.   Applications should be sent by email to Prof. Mikhail Prokopenko:   They should include a Resume and a Cover Letter. In their Cover Letter, applicants are invited to include a short (about 250 words) research statement explaining how they understand the issues related to the topic of research.  

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complex systems, computational epidemiology, agent-based simulation, complex networks, information cascade, crisis forecasting, critical phenomena, Phase transitions, percolation

Opportunity ID

The opportunity ID for this research opportunity is: 1944

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