Simulation modelling of COVID-19 spread

The ongoing global Covid-19 pandemic has led to an explosion in the use of agent-based modelling to attempt to predict the spread of the virus through the population.  The use of agents allows modellers to examine the impact of individual behaviours on disease spread, and the effectiveness of related public health restrictions.

Most of the current ABM work on Covid-19 has focussed on epidemiological questions, with the main outputs of interest being the number of cases, hospitalisations and deaths resulting from Covid-19 infections.  Our project takes a wider view and provides detailed models of individual health behaviour that can inform the development of more effective policies to mitigate the social and economic effects of the virus.

The use of ABM allows us to illuminate unexpected consequences of Covid-related policies which can emerge from individual behavioural responses to the pandemic.  Our work on simulating the effectiveness of contact-tracing apps has shown that even low levels of app adoption can reduce spread of the virus by prompting self-isolation, but the usefulness of the app depends strongly on the presence of accessible and efficient testing.  We have also constructed a novel behavioural model of Covid-19, which is the first to examine the impact of individual households' perception of infection risk and propensity to adhere to restrictions on the resulting effectiveness of policies designed to mitigate viral spread.

Currently we are extending these models to enable us to simulate the evolution of Covid-19 itself in response to changes in selection pressure resulting from greater vaccine prevalence and shifting behavioural patterns.  This model will use a genetic algorithm to simulate the evolution of the virus and enable it to adapt to changing conditions and alter its properties. This model will serve as a decision-support tool for health policy-makers, who will be able to examine how changing public health policies may affect the emergence of new Covid-19 variants.




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