Using mathematical models to understand the impact of climate change on tick-borne infections across Scotland - PhDData

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Using mathematical models to understand the impact of climate change on tick-borne infections across Scotland

The thesis was published by Worton, Adrian J, in September 2022, University of Stirling.


Ticks are of global interest as the pathogens they spread can cause diseases that are of importance to both human health and economies. In Scotland, the most populous tick species is the sheep tick Ixodes ricinus, which is the vector of pathogens causing diseases such as Lyme borreliosis and Louping-ill. Recently, both the density and spread of I. ricinus ticks have grown across much of Europe, including Scotland, increasing disease risk. Due to the nature of the tick lifecycle they are particularly dependent on environmental factors, including temperature and habitat type. Because of this, the recent increase in tick-borne disease risk is believed to be linked to climate change. Many mathematical models have been used to explore the interactions between ticks and factors within their environments; this thesis begins by presenting a thorough review of previous modelling of tick and tick-borne pathogen dynamics, identifying current knowledge gaps. The main body of this thesis introduces an original mathematical modelling framework with the aim to further our understanding of the impact of climate change on tick-borne disease risk. This modelling framework takes into account how key environmental factors influence the I. ricinus lifecycle, and is used to create predictions of how I. ricinus density and disease risk will change across Scotland under future climate warming scenarios. These predictions are mapped using Geographical Information System software to give a clear spatial representation of the model predictions. It was found that as temperatures increase, so to do I. ricinus densities, as well as Louping-ill and Lyme borreliosis risk. These results give a strong indication of the disease risk implications of any changes to the Scottish environment, and so have the potential to inform policy-making. Additionally, the models identify areas of possible future research.

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