TY - JOUR
T1 - A mechanistic hydro-epidemiological model of liver fluke risk
AU - Beltrame, Ludovica
AU - Dunne, Toby
AU - Vineer, Hannah Rose
AU - Walker, Josephine G.
AU - Morgan, Eric R.
AU - Vickerman, Peter
AU - McCann, Catherine M.
AU - Williams, Diana J. L.
AU - Wagener, Thorsten
PY - 2018/8/29
Y1 - 2018/8/29
N2 - 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.
AB - 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.
UR - https://doi.org/10.1098/rsif.2018.0072
U2 - 10.1098/rsif.2018.0072
DO - 10.1098/rsif.2018.0072
M3 - Article
VL - 15
JO - Journal of the Royal Society, Interface
JF - Journal of the Royal Society, Interface
SN - 1742-5689
IS - 145
M1 - 20180072
ER -