Job: Research Associate
Employer: University of Bristol
PhD Thesis: Simulating the risk of liver fluke infection in the UK through mechanistic hydro-epidemiological modelling
PhD Supervisors: Professor Thorsten Wagener and Dr Eric Morgan
Climate change and direct human activities, such as land use change, are making our environment increasingly non-stationary, with implications on hydrological and connected processes. Significant effects are expected on parasitic diseases, as many parasites carry out part of their life-cycle outside of the host, and thus are directly susceptible to changes in the environment. Evidence of climate-driven changes in the phenology of parasites and timing of infection already exists, with consequences for human and animal health. Therefore, it is crucial to have mechanistic models, which can be used to reliably simulate impacts of potential future conditions on disease risk.
Liver Fluke (Fasciola hepatica) is a widespread parasite of livestock, which in the UK mainly infects sheep and cattle, causing disease and production losses, e.g. reduced growth rates and lower milk yield, with consequent large economic costs. Risk of infection is strongly controlled by climatic and hydrological conditions, which characterise the habitat for parasite development and transmission. Despite on-going control measures, increases in prevalence have been reported in recent years in the UK, and have been often attributed to climate change. Currently used fluke risk models have been available since the 1950s, however, they are based on empirical relationships derived between historical climate and disease incidence data, which makes them unsuitable for simulating future risk.
The first aim of this research is to develop a mechanistic hydrological-epidemiological model for Liver Fluke, which explicitly represents the parasite life-cycle in connection with key environmental conditions, thus enabling predictions of system behaviour that are beyond the range of historical variability. The model will simulate risk of infection in time and space, at scales useful for disease management at the farm level, and will be tested employing various datasets, including data from regional veterinary laboratories. Our second goal is to use the model on different catchments in the UK to understand and predict how the controls identified within the coupled system may change under non-stationary conditions, assessing the implications of potential future climate on risk of transmission. Finally, the model will allow investigating the impacts of disease management scenarios, analysing the sensitivity of infection rates to parasite control strategies such as flukicidal treatments or environmental interventions. This will be essential for supporting decision-making and guiding interventions to mitigate risk.
Keywords: Hydrology, infectious disease, integrated mechanistic modelling.
Ludovica graduated in Environmental Engineering from Politecnico di Milano, Italy. In 2013 she was a visiting student at the Singapore University of Technology and Design, working at her MSc thesis. Her dissertation was part of a wider project concerning the integrated water management of the Red River system in Vietnam and focussed on investigating the effects of El Niño Southern Oscillation on hydrological processes at the catchment scale, to develop seasonal streamflow forecasts. Part of this work was presented at the International Congress on Environmental Modelling and Software (iEMSs) 2014, in a conference paper entitled “Quantifying ENSO impacts at the basin scale using the Iterative Input variable Selection algorithm”. Following an internship at a management consultancy in Milan, she joined WISE in September 2014 and is now based at the University of Bristol. Her research interests include modelling hydrologic and connected processes to understand how coupled systems will evolve under changes in climate and the environment. Current work lies at the interface between hydrological processes and infectious diseases and involves developing a new mechanistic hydro-epidemiological model for simulating risk of Liver Fluke infection in the UK.