Big Data approaches to identifying potential sources of emerging pathogens in humans, domesticated animals and crops

Emerging infectious diseases continue to pose major threats to humans, animals and plants. Recent years have seen significant outbreaks of several emerging diseases, ranging from the well-known (Ebola and Olive quick decline syndrome), to the previously little known (Zika), to the entirely novel (Schmallenberg), to name but a few. It is well established that the ability of a pathogen to infect multiple hosts, particularly hosts in different taxonomic orders or wildlife, is a risk factor for emergence in human and livestock pathogens. Emerging wild-life diseases have also been linked to 'spill-overs' from humans or domesticated animals. Despite the importance of cross-species disease transmission, there has been relatively little attention paid to which species are the most important sources cross communities (e.g., zoonotic, wild-life to domestic, plants to other kingdoms), which are the most prolific vectors, how those species acquired the pathogens, and by what means the diseases entered new species or populations. A major reason for this limited understanding is the lack of comprehensive data on the pathogens in animal and plant populations and, in most cases, poorly documented information on how they are transmitted, including to humans.
In this fellowship, I will improve and exploit a novel bioinformatic resource developed at the University of Liverpool to investigate how humans, their domesticated animals and crops are connected to the pathogen reservoir in other species, and how these pathogens pass from that reservoir to the focus populations. The bioinformatic resource, developed by me with funding from BBSRC, is the Enhanced Infectious Disease Database (EID2). EID2 utilises state-of-the-art, text and data mining procedures to extract information from multiple sources, including millions of metadata records accompanying genetic sequences and scientific publications. After processing, EID2 provides evidence for over 60,000 interactions between species of hosts and pathogens and is the most comprehensive data source on the known pathogens of humans, animals, and plants and their geographical ranges.
During this fellowship, I aim to investigate the factors which lead to emergence of pathogens, asking the following questions:
1. What are the characteristics of the networks that connect species via shared pathogens? How central are humans and their domesticated animals and crops in these networks and which other species are each of those communities most closely connected to?
2. What is the role of different pathogen transmission routes on the nature of these networks? Are the potential species-to-species transmission pathways different for direct, food-borne, water-borne and vector-borne pathogens?
3. What factors determine the host ranges of pathogens? Are host species more likely to become exposed to pathogens that infect a wide range of species? From species that are closer to them genetically? Or from those species with which they often interact?
4. What are we missing? Given the networks, transmission routes and host ranges, what is the risk associated with each pathogen emerging in new species? What are the pathogens that can be prioritised as more-likely to emerge in the future?

Despite the importance of cross-species disease transmission, there has been relatively little attention paid to which species are the most important sources cross communities, and by what means the diseases entered new species or populations. A reason for this is the lack of comprehensive data on pathogens in animals and plants and, often, poorly documented information on how they are transmitted. The objective of this fellowship is to transform host-pathogen interactions extracted from EID2 into multi-host ecological networks. Using network analysis: typology; bridge species; and metrics of networks will be studied. By implementing a centrality index based on a cohort of centrality measures, potential super-spreaders will be identified.
Transmission routes play a role in determining the parts of the population that are infected, the range of species that can be infected, and the measures required to bring an outbreak under control. A transmission route identification system, utilising an ensemble of classifiers, will be developed to categorise scientific publications to identify transmission routes. Consequences of different routes will be assessed, and their effect on centrality will be quantified.
The range of hosts that a pathogen infects influences the dynamics of pathogen transmission and emergence in novel hosts. Statistical models will be developed to rank different traits of hosts in relation to host-specificity. The effects of the traits on centrality will be quantified.
Predicting unobserved or future host-pathogen interactions is imperative for one-health, enabling identification of potential emergence and spill-over events. A link prediction model on host-pathogen networks, with links either positive or negative, will be constructed. A system will be developed to capture host-pathogen negative interactions from scientific literature. The results will be incorporated into the multi-host networks; the effect on centrality will be quantified.