Spotlight on producing projections for the human population

DyME, quantifying the environmental impacts

The Dynamic Modelling for Environment - Climate, Heat and Health project (DyME-CHH) has set out to build a microsimulation model (DyME) applied to population-level exposure to extreme heat (CHH). The project aims to predict the health consequences of climate change and rising temperatures on various populations within the UK. The DyME model was first developed in collaboration with academic and industry partners by the Turing’s Urban Analytics programme at the start of the COVID-19 pandemic - where ‘E’ in DyME initially stood for ‘Epidemics’.

In 2020, the Royal Society established the Rapid Assistance in Modelling the Pandemic initiative (RAMP). It brought together epidemiologists and those with skills in computer modelling in fields such as urban transportation planning, financial markets and individualised marketing on social media to help model non-pharmaceutical interventions to stop the spread of the coronavirus. The Turing researchers participated in the initiative and developed DyME by integrating two previous open source projects affiliated with the Turing: QUANT (Quantitative Urban Analytics), a land-use transportation model; and SPENSER (Synthetic Population Estimation and Scenario Projection), a microsimulation model that produces high-resolution geographical projections of human populations. The work was done in collaboration with universities in the UK and Denmark, as well as with data-science companies in the UK.

Using DyME, the researchers demonstrated that in 2020, going into lockdown in response to COVID-19 a week earlier would have significantly reduced the rate of infection and transmission in England’s Devon County (Spooner et al., 2021). The success of DyME led to the subsequent adaptation of microsimulation models and tools for use in environmental research. The DyME model was released as open source software and was later expanded to a national level in England under the name Agent-based Simulation of Epidemics at Country Scale (ASPICS) by the ASG programme’s Urban Analytics team. Under the name ‘Dynamic Microsimulation for Environment - Air Quality’ or DyME-AQ, the model was repurposed for use in predicting air quality and associated health hazards. The initial DyME-CHH model will be calibrated for different trial areas, with future work planned with the Turing REG team to upscale for the rest of the UK, developing more efficient R code that the DyME model will be written in initially.

DyME-CHH is building off this body of work and experience created throughout the programme to model the interaction of activity, rising temperatures and vulnerabilities on the health of different populations within the UK, in collaboration with the University of Exeter and the Cornwall Council. Using a synthetic population provided by the Synthetic Population Catalyst (SPC) project, researchers can already estimate how different individuals might move through the world, and predict how these conditions along with their age, health and socioeconomic factors affect their risks in the face of environmental breakdown. DyME-CHH outputs will allow policymakers to identify the specific risk to an area based on what the population and physical aspects (such as housing or urban design) of the area looks like. An exciting impact of the project is the ability for decision makers to model different scenarios of local adaptation to support developing policy in an accessible, but data-driven way. The goal is to enable local authorities to use this information for identifying and prioritising key mitigation and adaptation strategies, limiting the adverse effects on the health of the most vulnerable people in a population.