Modelling leptospirosis at the animal-human interface in island populations

Leptospirosis is a bacterial infectious disease which affects both animals and humans. There are an estimated one million human cases a year and 58,000 deaths which occur all over the world. The majority of cases occur in tropical and sub-tropical regions, with high incidence reported in Pacific Islands. The bacteria are passed into the environment in the urine of infected animals, and humans can become infected either by direct contact with animals, or through contaminated water and soil. Understanding the transmission pathways by which leptospirosis is passed between hosts is a challenge, as there are multiple animals that can be infected (e.g. rodents, livestock and dogs), as well as environmental and social drivers of transmission (such as flooding, temperature and poverty). This project aims to combine a new transmission model with historical disease surveillance and environmental data to characterise how leptospirosis is passed between hosts, using Fiji as a case study. I will use a mathematical simulation model to explain how different animals and humans become infected, how they pass the disease to each other, and the risk factors that give rise to outbreaks. I will then fit this to historical data so it is useful for real world decision-making. Subsequently, this model will be further developed to explore how best to control leptospirosis, as well as make a tool to predict future outbreaks. Building on this approach in Fiji, it may be possible to extend this tool and model to other settings, such as other islands in the Pacific and even other affected countries. This project will allow me to gain quantitative skills in mathematical and statistical modelling. I will learn how to manipulate big data and combine new models with historical health surveillance and remote sensing data. In addition, I will have the opportunity to interact with international researchers and stakeholders in the field and at conference.

It is thought that the number of cases of leptospirosis will increase globally in the future. Climate change is predicted to increase the likelihood of extreme weather events, such as flooding, whilst increasing population sizes will require more intensive farming, and so affected populations are going to be living closer to their animals. Understanding how leptospirosis is passed between hosts is key to planning and implementing effective health interventions. The hope is that this project will begin to allow the governments of countries affected by leptospirosis to predict when outbreaks will occur and, in understanding how it is transmitted, take steps to prevent these outbreaks.

Key words: Mathematical modelling, infectious disease, leptospirosis, quantitative analysis, environmental change, global health, planetary health.