A mechanistic hydro-epidemiological model of liver fluke risk. Issue 145 (29th August 2018)

• Record Type: Journal Article
• Authors: Beltrame, Ludovica; Dunne, Toby; Vineer, Hannah Rose; Walker, Josephine G.; Morgan, Eric R.; Vickerman, Peter; McCann, Catherine M.; Williams, Diana J. L.; Wagener, Thorsten
• Is Part Of: Journal of the Royal Society interface. Volume 15:Issue 145(2018)
• Publication Date: 2018-08-29
• Subjects: water-based disease -- environmental drivers -- mechanistic modelling -- spatio-temporal dynamics -- fasciolosis; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2018.0072
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : The majority of existing models for predicting disease risk in response to climate change are empirical. These models exploit correlations between historical data, rather than explicitly describing relationships between cause and response variables. Therefore, they are unsuitable for capturing impacts beyond historically observed variability and have limited ability to guide interventions. In this study, we integrate environmental and epidemiological processes into a new mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model simulates environmental suitability for disease transmission at a daily time step and 25 m resolution, explicitly linking the parasite life cycle to key weather–water–environment conditions. Using epidemiological data, we show that the model can reproduce observed infection levels in time and space for two case studies in the UK. To overcome data limitations, we propose a calibration approach combining Monte Carlo sampling and expert opinion, which allows constraint of the model in a process-based way, including a quantification of uncertainty. The simulated disease dynamics agree with information from the literature, and comparison with a widely used empirical risk index shows that the new model provides better insight into the time–space patterns of infection, which will be valuable for decision support.