A crucial part of SPIL is the development of a model describing the course of antibody levels after infection of Channel Island foxes (Aims 1.1 and 1.2). This model provides estimates of the time of infection, which is crucial information for the Leptospira transmission models. This model provides the basis for estimating time series of the incidence of Leptospira infection in the fox population.
In February 2018 Dr. Borremans was an invited participant of a workshop on spillover 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.
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. Additionally, using a Cox Proportional Hazards model to analyze infection risk factors, we found a highly significant effect of the amount of rainfall in the preceding season on the risk of becoming infected.
The final stage of this part of the project, which is to integrate the spatial and non-spatial models of transmission into a complete spatiotemporal model of transmission, builds on a compartmental model of transmission in which individuals are classified into different states depending on their infection status. We developed an SEICR model with compartments S (susceptibles), E (exposed but not yet infectious), I (infectious), C (chronically infectious), R (recovered/immune to reinfection). This model incorporates mortality, seasonal birth pulses, and variation in transmission linked to precipitation. The model is able to reconstruct temporal changes in the proportion of antibody- and PCR-positive individuals over time. We are currently in the process of expanding this model into a patch-based spatial model of transmission on the island.
