A mechanistic hydro-epidemiological model of liver fluke risk

Ludovica Beltrame*, Toby Dunne, Hannah Rose Vineer, Josephine G Walker, Eric R Morgan, Peter Vickerman, Catherine M McCann, Diana J L Williams, Thorsten Wagener

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)
    5 Downloads (Pure)

    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.

    Original languageEnglish
    Number of pages14
    JournalJournal of the Royal Society Interface
    Volume15
    Issue number145
    Early online date29 Aug 2018
    DOIs
    Publication statusPrint publication - 31 Aug 2018

    Keywords

    • Environmental drivers
    • Water-based disease
    • Spatio-temporal dynamics
    • Mechanistic modelling
    • Fasciolosis

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