No abstract

Climate change and emerging drug resistance make the control of many infectious diseases increasingly challenging and diminish the exclusive reliance on drug treatment as sole solution to the problem. As disease transmission often depends on environmental conditions that can be modified, such modifications may become crucial to risk reduction if we can assess their potential benefit at policy-relevant scales. However, so far, the value of environmental management for this purpose has received little attention. Here, using the parasitic disease of fasciolosis in livestock in the UK as a case study, we demonstrate how mechanistic hydro-epidemiological modelling can be applied to understand disease risk drivers and the efficacy of environmental management across a large heterogeneous domain. Our results show how weather and other environmental characteristics interact to define disease transmission potential and reveal that environmental interventions such as risk avoidance management strategies can provide a valuable alternative or complement to current treatment-based control practice.

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.

17The majority of existing models for predicting disease risk in response to climate change are 18 empirical. These models exploit correlations between historical data, rather than explicitly 19 describing relationships between cause and response variables. Therefore, they are unsuitable for 20 capturing impacts beyond historically observed variability and cannot be employed to assess 21interventions. In this study, we integrate environmental and epidemiological processes into a new 22 mechanistic model, taking the widespread parasitic disease of fasciolosis as an example. The model 23 simulates environmental suitability for disease transmission, explicitly linking the parasite life-cycle 24to key weather-water-environment conditions. First, using epidemiological data, we show that the 25 model can reproduce observed infection levels in time and space over two case studies in the UK. 26Second, to overcome data limitations, we propose a calibration approach based on Monte Carlo 27 sampling and expert opinion, which allows constraint of the model in a process-based way, including 28 a quantification of uncertainty. Finally, comparison with information from the literature and a 29widely-used empirical risk index shows that the simulated disease dynamics agree with what has 30 been traditionally observed, and that the new model gives better insight into the time-space 31 patterns of infection, which will be valuable for decision support. 32 33