ALife 2020 Tutorial
Tracing epidemics with agent-based and network based models
EpiAgeNet: 13 July 2020
Tracing epidemics with agent-based and network based models
EpiAgeNet: 13 July 2020
We will consider pros and cons of agent-based and network-based models for studying epidemics, tracing a spatiotemporal spread of epidemics across a nation. We will also explore how high-resolution molecular genotyping data can help to infer weighted genetic networks, tracing emergence and evolution of dominant strains.
Highlights
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Agent-based Modelling (ABM)
In the first part of the tutorial, we will consider an agent-based modeling (ABM) framework for studying epidemics, exemplified by influenza and COVID-19 pandemics. Using the ABM simulator, we will analyse the spatiotemporal spread of contagion and spatial synchrony of infection across a nation. The individual-based epidemiological model will account for population mobility patterns and human interactions, using stochastically generated software agents. It will also trace the dynamics of viral infection and transmission at several scales. Using this approach, we can explore pandemic extent and effects of various interventions, via incidence curves, prevalence choropleths, epidemic synchrony, and phase transitions [1, 3, 4, 9, 11].
Highlights
- introduction to key epidemiological concepts
- large-scale agent-based modeling
- network measures quantifying emergence and evolution of pathogens
Download slides
Agent-based Modelling (ABM)
In the first part of the tutorial, we will consider an agent-based modeling (ABM) framework for studying epidemics, exemplified by influenza and COVID-19 pandemics. Using the ABM simulator, we will analyse the spatiotemporal spread of contagion and spatial synchrony of infection across a nation. The individual-based epidemiological model will account for population mobility patterns and human interactions, using stochastically generated software agents. It will also trace the dynamics of viral infection and transmission at several scales. Using this approach, we can explore pandemic extent and effects of various interventions, via incidence curves, prevalence choropleths, epidemic synchrony, and phase transitions [1, 3, 4, 9, 11].
Network-based measures
In the second part, we will examine foodborne epidemics as complex systems, driven by evolution and interactions of diverse microbial strains, and focus on emergence of successful strains. We will explore how high-resolution molecular genotyping data can help to infer weighted networks based on genetic distances between the strains, depicting epidemics as networks of individual bacterial strains. The network analysis will exemplify the emergence of dominant strains defined by their local network topological properties, such as centrality, while correlating the development of new epidemics with global network features, such as small-world propensity [8].
References
- S. L. Chang, N. Harding, C. Zachreson, O. M. Cliff, M. Prokopenko, Modelling transmission and control of the COVID-19 pandemic in Australia, arXiv: 2003.10218, 2020.
- N. Harding, R. E. Spinney, M. Prokopenko, Population mobility induced phase separation in SIS epidemic and social dynamics, Scientific Reports, 10: 7646, 2020.
- C. Zachreson, K. M. Fair, N. Harding, M. Prokopenko, Interfering with influenza: nonlinear coupling of reactive and static mitigation strategies, Journal of Royal Society Interface, 17(165): 20190728, 2020.
- N. Harding, R. E. Spinney, M. Prokopenko, Phase transitions in spatial connectivity during influenza pandemics, Entropy, 22(2), 133, 2020.
- S. L. Chang, M. Piraveenan, P. Pattison, M. Prokopenko, Game theoretic modelling of infectious disease dynamics and intervention methods: a review, Journal of Biological Dynamics, 14:1, 57-89, 2020.
- K. M. Fair, C. Zachreson, M. Prokopenko, Creating a surrogate commuter network from Australian Bureau of Statistics census data, Scientific Data, 6, 150, 2019.
- S. L. Chang, M. Piraveenan, M. Prokopenko, The Effects of Imitation Dynamics on Vaccination Behaviours in SIR-Network Model, International Journal of Environmental Research and Public Health, 16(14), 2477, 2019.
- O. M. Cliff, V. Sintchenko, T. C. Sorrell, K. Vadlamudi, N. McLean, M. Prokopenko, Network properties of Salmonella epidemics, Scientific Reports, 9, 6159, 2019; see also: Supplementary Information.
- C. Zachreson, K. M. Fair, O. M. Cliff, N. Harding, M. Piraveenan, M. Prokopenko, Urbanization affects peak timing, prevalence, and bimodality of influenza pandemics in Australia: Results of a census-calibrated model, Science Advances, 4(12), eaau5294, 2018.
- N. Harding, R. Nigmatullin, M. Prokopenko, Thermodynamic efficiency of contagions: a statistical mechanical analysis of the SIS epidemic model, Interface Focus, 8 20180036, 2018.
- O. M. Cliff, N. Harding, M. Piraveenan, E. Y. Erten, M. Gambhir, M. Prokopenko, Investigating Spatiotemporal Dynamics and Synchrony of Influenza Epidemics in Australia: An Agent-Based Modelling Approach, Simulation Modelling Practice and Theory, 87, 412-431, 2018 (also on arXiv).
- E. Y. Erten, J. T. Lizier, M. Piraveenan, M. Prokopenko, Criticality and Information dynamics in epidemiological models, Entropy, 19 (5), 194, 2017.