Fully-funded PhD opportunity available! I’m looking for someone interested in working on agent-based modelling for healthcare applications. No fees and £20K stipend. These are four-year positions and you will be asked to contribute up to six hours per week of teaching (tutorials/demonstration only, no lectures), which is more work but also good for the CV. Click here and filter under ‘Computer Science’ to see my project. For more about me, check out the various pages on this blog or my staff profile at Teesside.
Project description: This research will focus on the application of Agent-Based Modelling techniques to human social systems, with particular emphasis on digital health applications. In the context of public health, agent-based models can help us understand the complexities of health policy implementation and service delivery by modelling the multiple interacting processes underlying the health system. These models will investigate challenges in health and social care service delivery across a variety of spatial and temporal scales – from short-term studies of demands on accident and emergency services, to longer-term explorations of the pressures facing social care over the next several decades. Our multi-disciplinary team will work with members of the School of Health and Social Care here at Teesside, along with external collaborators and stakeholders. The project would be suitable for a graduate with a background in Computer Science, Artificial Intelligence, Statistics or Complexity Science with an interest in Public Health/Healthcare applications.
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Some of you may have seen my paper with Jason Noble, Jason Hilton and Jakub Bijak from 2013 called Simulating the cost of social care in an ageing population. The paper presents an agent-based model of informal social care in the United Kingdom. Our virtual agents live in a simulated UK, and try to live out their lives — moving around, working, starting families, etc. — and when their family members need help due to illness, they try to contribute their time to help out.
Model results showed that, surprisingly, retirement age has a strong impact on social care costs across the population. When the retirement age was raised, there was a net increase in tax revenues up to a certain point, but beyond that critical limit social care costs began to rise. The model seemed to indicate that an unexpectedly large number of elderly people were providing informal care to their spouses or other loved ones, and so putting them back into the workforce actually led to increased demand for state-funded formal care for those left at home, increasing the cost to society overall.
I’ve just noticed a news posting from Age UK from last month which is pretty relevant to this:
New figures released this morning by Age UK show there is an army of carers amongst the oldest in our society, who are between them saving the health and care system a massive £5.9bn a year by providing unpaid care.
Over the past 7 years the number of carers aged 80 and over has rocketed from 301,000 to 417,000, an increase of nearly 39%. Now 1 in 7 people aged 80 and over provide some form of care to family or friends.
Furthermore, over half (144,000) of carers in this age group who are caring for someone in their home are doing so for more than 35 hours a week, while a further 156,000 are caring for more than 20 hours a week. As our population continues to age it is estimated that there will be more than 760,000 carers aged 80 and beyond by 2030.
I’m the first to admit that I’m a bit of an outsider when it comes to gerontology and the study of social care in detail, so it’s possible that this study isn’t telling us much that’s new. It’s news to me, however, and I’m glad to see that our model showed us some interesting results that turned out to be reflective of reality, despite the necessarily simplified nature of the model’s systems.
Now that there’s some solid data out there about this ‘invisible army’ of older carers, I think it may be time to revisit this model and investigate this aspect more fully. Caring for someone 35 hours a week or more is exhausting work for anyone, let alone someone over 80 years old who should be enjoying a dignified retirement. Perhaps we can use agent-based models to investigate policies that could take some of this burden away from our older population.
Our paper for the journal Demographic Research just came out today! After months of hard work it’s so satisfying to see it out there, and we love Demographic Research — they’re open-access too, so feel free to download to your heart’s content and spread it widely.
Next up will be our paper for the Journal of Artificial Societies and Social Simulation; the final version has been submitted to the editor so it won’t be long now. Always good to have these things done; now we’re able to move on to the next paper!
Got the news recently that a paper by Jakub Bijak, Jason Hilton and myself has been accepted for publication in Demographic Research, an open-access journal for demography and population sciences. We’re very pleased to see our work accepted, particularly as we’re offering up a relatively unconventional approach.
We’ve just sent in the final version for editing/formatting, so it won’t be online for a little while. In the meantime here’s the abstract:
Reforging the Wedding Ring: Exploring a Semi-Artificial Model of Population for the United Kingdom with Gaussian Process Emulators
Background We extend the ‘Wedding Ring’ agent-based model of marriage formation to include some empirical information on the natural population change for the United Kingdom together with behavioural explanations that drive the observed nuptiality trends.
Objective We propose a method to explore statistical properties of agent-based demographic models. By coupling rule-based explanations driving the agent-based model with observed data we wish to bring agent-based modelling and demographic analysis closer together.
Methods We present a Semi-Artificial Model of Population, which aims to bridge demographic micro-simulation and agent-based traditions. We then utilise a Gaussian process emulator – a statistical model of the base model – to analyse the impact of selected model parameters on two key model outputs: population size and share of married agents. A sensitivity analysis is attempted, aiming to assess the relative importance of different inputs.
Results The resulting multi-state model of population dynamics has enhanced predictive capacity as compared to the original specification of the Wedding Ring, but there are some trade-offs between the outputs considered. The sensitivity analysis allows identification of the most important parameters in the modelled marriage formation process.
Conclusions The proposed methods allow for generating coherent, multi-level agent-based scenarios aligned with some aspects of empirical demographic reality. Emulators permit a statistical analysis of their properties and help select plausible parameter values.
Comments Given non-linearities in agent-based models such as the Wedding Ring, and the presence of feedback loops, the uncertainty of the model may not be directly computable by using traditional statistical methods. The use of statistical emulators offers a way forward.