Abstract
Livestock are commonly infected by many different species of parasite (termed coinfections), and yet parasite control generally focuses on one species or a limited group of related species. The core goal of COADAPT is to achieve impact by improving ability to predict, measure and target coinfections in the field, and supply tools for exploitation and application – to enhance the competitiveness and sustainability of the UK farming industry and to reinforce internationally leading capabilities in predictive biology and digital decision support tools for parasite control. We focus on parasitic and gastrointestinal coinfections in grazing ruminants because they are universally common, frequently production-limiting, and often inappropriately targeted. Changes in seasonal patterns of infection and increasing antiparasitic drug resistance impose major limitations on future ability to manage impacts on production, emissions and animal welfare. The key challenge is to be able to manage coinfections adaptively, calibrating interventions accurately to infection status and impact, using limited chemical inputs to obtain the best response. This will require alignment with prevailing risks, and effective support of on-farm decisions.
Technical Summary
The core goal of COADAPT is to achieve impact by improving ability to predict, measure and target coinfections in the field, focusing on parasitic infections in the gut, and supply tools for exploitation and application. The key challenge is to be able to manage coinfections adaptively, calibrating interventions accurately to infection status and impact, using limited chemical inputs to obtain the best response. This will require alignment with prevailing risks, and effective support of on-farm decisions.
The proposal addresses this challenge in linked work packages (WP), enhancing ability to: (1) predict patterns, (2) measure coinfections and outcomes, and (4) target interventions.
Patterns of coinfections will be evaluated using historical data from laboratory diagnoses as well as longitudinal monitoring at farm level and cross-sectional data on individual animals from faecal and post mortem data. Mechanistic models of climatic and age-related drivers of infection will be run to predict regional and temporal trends in coinfection overlap, validated using these datasets, and extended to climate change projections to explore likely future patterns.
Improved ability to detect and respond to coinfections will be achieved by developing simple, accessible and affordable molecular diagnostic tests for multi-species parasite infections and comparing them to reference tests. Further, to assess the effect of targeted interventions on animal performance and map outcomes to coinfections and treatment effects on them. Finally, to screen for positive or adverse consequences of antiparasitic treatments on bacterial coinfections, focusing on known pathogens, zoonoses and antimicrobial resistance.
Tools and understanding developed will be applied through dissemination and also new digital decision support tools, capitalising on strong collaborations with industry.
Contact details:
Professor Eric Morgan
School of Biological Sciences
Queens University Belfast