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Time-space trajectories

This project brings together a group of UK and US scholars to push forward a cutting-edge research agenda in the interdisciplinary modelling of complex epidemiological trajectories of well-being and place. It is based on the realization that a major methodological challenge in epidemiology and community health today is modelling the complex inter-dynamics of time, place and well-being.


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From managing the spread of disease to reducing the burden of chronic disease to improving sluggish or resistant to change health networks, one of the most important and timely methodological challenges in epidemiology and community health today is modelling the inter-dynamics of time, place and well-being. Of particular concern is how to:

  • bring together mathematical, epidemiological and sociological approaches to model trajectories;

  • connect these models to social data, context and human agency; and

  • do so in an applied setting to inform health policy and planning.

In response to these points, the current project seeks to develop two interconnected, innovative, interdisciplinary approaches, already in development by team members. In terms of modelling temporal/spatial trajectories mathematically, two main approaches tend to be used: a stochastic (probabilistic) or deterministic (differential equation) approach. Research in network science and health, for example, has focused on stochastic models, although recent work by Liu, Slotine and Barabasi has explored a deterministic, control theory approach. Research by team-member House (Warwick Mathematics Institute) has explored the utility of a stochastic, epidemiological, network science approach for modelling epidemics and disease outbreaks. Research by team-members Rajaram and Castellani (Kent State University, USA) has explored the utility of a deterministic, control theory, density-based approach for modelling the temporal and spatial dynamics of aggregate (place-based) health trajectories. To develop these approaches and their connections, we ground them in the idea that nodes, agents, networks and aggregate (place-based) densities are types of complex, case-based systems—which is where the case-based, mixed-methods research of Uprichard, Byrne and Griffiths comes into our project. The sociological and practice-based health expertise of Byrne, Castellani, Griffiths and Williams will ensure that the project remains focused on the global health research agenda.

If successful, the methodological innovations of this project will advance existing research in two ways: Firstly, we will advance three key areas in the complexities of health and place literature:

  • modelling community health and epidemiological trajectories as complex networks of cases;

  • conceptualizing places (i.e., networks, communities, etc.) as complex systems; and

  • modelling complex temporal-spatial trajectories to inform health policy and planning.

By bringing these areas together, we aim not only to extend each area, but also to develop an innovative and cutting-edge agenda which is resolutely interdisciplinary. From a complex case-based perspective, places (neighbourhoods, social networks, cities, etc.) are nested complex systems comprised of a set of clinical (i.e., diseases), compositional (i.e., household income) and contextual (i.e., job growth) factors and cases. In turn, the health trajectories of a place’s cases (even if a network) are a function of their different health profiles—measurements on a place’s clinical, compositional and contextual factors. Understanding the complexities of place as a set of cases is clinically appealing because, at the end of the day, medicine is ultimately about the case. Therefore, cases constituted a complex web of clinical, psychological, social and environmental characteristics can be modelled to present emergent problems, which, when assembled as a clinical profile, explain health differences and similarities. This is why we aim to conceptualise place, communities, networks and health all together as complex cases in which each element/entity emerges within and out of the interactions with each other.

Secondly, most approaches to modelling complex social trajectories tend to prioritise either time or space. There is good reason for this, as the feedback loops involved in modelling complex health systems (in networks or other places, for example) is problematic, methodologically. However, our view is that there is much value in tackling the methodological challenge of modelling complex dynamics over both time and space head-on, as the complex epidemiological trajectories of well-being and place are, by definition, temporally and spatially intertwined. In sum, then, the collaborative work that we are doing is: methodologically ambitious; theoretically synthetic; and empirically innovative. In short, this is high-risk/high-payoff science that has the potential to be ground breaking.