DSNE’s seminal works on the Antarctic ice sheet and Greenland focus on understanding and forecasting future change in the properties of these lands (see for example the work from Hollaway and colleagues published in Environmental Modelling and Software[1]). Changepoint analyses have been widely applied but not exclusively to environmental problems. The reader of this blog can appreciate that we are ourselves living in a changepoint period started in September/October 2019 with the emergence of COVID19 pandemic.

Statistical approaches to comparing data collected before, during (and throughout) the COVID pandemic ranged for ‘simple’ generalised linear models applied for pre and post COVID data (where the pre and post is an independent variable) or hypothesis testing between equivalent months pre and post pandemic. More (methodologically) advanced studies employed time-series forecasting models such as autoregressive integrated moving average (ARIMA) models and Bayesian Structural Time Series Models to estimate COVID-related differences in a certain outcome from its baseline values of 4-5 years ago. Finally, another common approach belonging to the changepoint analyses, is interrupted time series (ITS) regressions, a segmented regression model that is increasingly being used for the evaluation of publica health interventions as well as for the evaluation of health impacts of large scale events such as the global financial crisis.

And it is a changepoint analysis for the comparison between the pre- and post-COVID19 health and socio-economic conditions in the coastal community of Fleetwood the focus of a new project funded by Healthier Fleetwood and Lancaster University within the Impact Acceleration Account award. This project can be formally considered an extension of DSNE on the health theme.

For communities already tackling persistent and entrenched health and socioeconomic inequities, the pandemic has created additional challenges – but it may also offer opportunities for reform and change. This is another occasion for data science and advanced quantitative methods to translate data to actions and to help Fleetwood town recover from the pandemic. We plan to generate new understanding that can inform recommendations for local partners facilitating post-COVID recovery in Fleetwood.

[1] Evaluating the ability of numerical models to capture important shifts in environmental time series: A fuzzy change point approach. Hollaway, M.J., Henrys, P.A., Killick, R., Leeson, A., Watkins, J. 31/05/2021 In: Environmental Modelling and Software. 139, 10 p.

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