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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - SpiL (Spillover of Leptospira in island populations of the Channel Island fox)

Teaser

Summary

Spillover, or the transmission of pathogens from one species to another, is the major source of emerging infectious disease outbreaks in humans. Infamous examples include Ebola virus, HIV and SARS. Yet despite the relevance of spillover and the need to understand it, the theory behind its causes and mechanisms is currently underdeveloped. The goal of SPIL is to develop a new conceptual framework for spillover through the use of an interdisciplinary approach to analyze an exceptional dataset on the transmission of Leptospira between California sea lions and endangered Channel Island foxes. Leptospira is the most widespread animal infectious disease and the cause of leptospirosis, a potentially severe and fatal renal disease. California sea lions experience annual outbreaks of leptospirosis, which is a major cause of stranding. Sea lions are thought to transmit the pathogen to Channel Island foxes through contacts on beaches, and leptospirosis has been detected in foxes on Santa Rosa Island since their reintroduction following near-extinction in the late 90s. This project combines concepts and methods from human epidemiology, disease ecology and island biology to create a model of Leptospira spillover risk, with the ultimate goal of extracting and developing general concepts on spillover.

Work performed

A crucial part of SPIL is the development of a model describing the course of antibody titers after infection of Channel Island foxes (Aims 1.1 and 1.2). This model will provide estimates of the time of infection, which is crucial information for the Leptospira transmission models. Dr. Borremans is conducting this work in collaboration with Riley Mummah. Data has been processed and selected, the different modeling steps have been planned in detail, and a first analysis has been performed. The main results so far are that there is significant individual variation in peak antibody titer, and that the antibody decay rate changes over time. These results show that more refined models are needed that are able to incorporate both individual effects and changing decay rates.
In February 2018 Dr. Borremans was an invited participant of a workshop on spillover that in Montana (US), organized by Prof. Raina Plowright. This workshop gathered a limited number of international experts for a retreat with the goal of advancing theory on the spillover of pathogens, the topic of SPIL. A consequence of this unplanned opportunity was a change in the timeline of the planned aims in the project, which has led to the publication of two articles.
The first article completes Aim 2.4 and Deliverable 2.3, which is the development of a new conceptual framework of spillover. The article presents new theory on spillover across ecosystem boundaries (the core topic of project SPIL), which is the most important way in which pathogen spillover between species occurs. It reviews the limited existing knowledge on this topic and integrates theory from different related fields (island biology, invasion biology, population ecology, macroecology) to present a new theoretical framework for understanding spillover across ecosystem boundaries.

Excellent progress has been made on a California sea lion (CSL) transmission model, a key part of the model of non-stationary of risk and spillover (Aim 2.2). The transmission model uses the long-term dataset on CSL leptospirosis cases (1984-2012) to analyze the demographic and environmental causes of annual variation in outbreak size. Model performance was high, with key results being: (1) Almost 60% of variation in outbreak size can be explained by a combination of demographic and environmental factors; (2) The model can be used to predict outbreak size ahead of time, which can have significant impacts on the preparedness of stranding centers along the US West Coast; (3) Climate change simulations suggest that the average outbreak size will decrease, with occasionally more extreme outbreak sizes.

Spatiotemporal risk mapping of the transmission of Leptospira in the fox population is another key component of Aim 2.2. This work is being conducted in close collaboration with Riley Mummah, a PhD student at the Lloyd-Smith research group co-mentored by Dr. Borremans. A major challenge has been to integrate spatial data from both GPS collars and radio-telemetry tracking, for which Riley has developed new methods that will be widely applicable in the field of movement ecology, especially given the fact that it is becoming increasingly easy to gather large amounts of data from different sources. These efforts have led to the ability to generate maps of estimated home ranges of all individuals, changing over time. The next stage of this project will be to implement these data into a spatiotemporal model of transmission. In order to do this, an estimate of the time of infection of each individual will be needed.

Final results

So far several advances have been made that move the field beyond the state of the art.
â€¢ Development of a method to reconstruct susceptibles in a population taking into account age- and sex-specific survival. This advance moves this method outside the field of human epidemiology, and opens up applications to time series of wildlife infections, where variation in survival and lifespan are likely to significantly influence the number of susceptible individuals in a population.
â€¢ Integration of GPS and telemetry data to estimate individual home ranges. This approach, developed by collaborator Riley Mummah, advances the field of movement ecology by allowing the combined use of data sources that differ greatly in their spatial resolution and their format (spatial points vs spatial polygons).
â€¢ The quantification of model fit for the sea lion transmission models (described above) implements a new type of statistic that we currently label â€œfeature mismatchingâ€ (FM). Model fit statistics typically provide information about how closely the predicted data match the observed data by measuring the distance between these data points and then providing a measure of residual error. Such statistics however are biased towards large differences that can occur more often in overdispersed datasets where the maximum values are much larger than the smallest values. This is often the case in variable time series such as disease cases, including cases of leptospirosis in sea lions. This can become a problem when the researcher is interested in the underlying reasons behind the observed variation, as there is no explicit way to score the qualitative matching between predicted and observed data. We therefore developed a new method that scores the mismatch between predicted and observed outbreak categories instead of absolute values.
â€¢ One assumption that needs to be tested before we are able to develop the models is that the infecting strain of Leptospira remains the same throughout the study. Unfortunately there are limited culture and genetic sequence data, and the results of antibody assays are not conclusive because of extensive cross-reactivity of antibodies against different Leptospira antigens. We are therefore developing a new approach that aims to use cross-reactivity patterns to identify the infecting strain. We do this through a combination of clustering methods to reduce dimensionality, and custom statistical methods for testing the likelihood of different samples being infected by the same strain.