Cattle and Nematodes Under Global Change: Transmission Models as an Ally. Issue 9 (September 2016)

• Record Type: Journal Article
• Authors: Verschave, Sien H.; Charlier, Johannes; Rose, Hannah; Claerebout, Edwin; Morgan, Eric R.
• Is Part Of: Trends in parasitology. Volume 32:Issue 9(2016:Sep.)
• Page Start: 724
• Page End: 738
• Publication Date: 2016-09
• Subjects: model -- gastrointestinal nematode -- ruminant -- climate -- farm management -- epidemiology; Parasitology -- Periodicals; Parasitology -- Periodicals; Biology -- Periodicals; Parasitology; Biology; Parasitologie -- Périodiques; Online resources; 571.999
• DOI: 10.1016/j.pt.2016.04.018
• Languages: English
• ISSNs: 1471-4922

Abstract

Abstract : Nematode infections are an important economic constraint to cattle farming. Future risk levels and transmission dynamics will be affected by changes in climate and farm management. The prospect of altered parasite epidemiology in combination with anthelmintic resistance requires the adaptation of current control approaches. Mathematical models that simulate disease dynamics under changing climate and farm management can help to guide the optimization of helminth control strategies. Recent efforts have increasingly employed such models to assess the impact of predicted climate scenarios on future infection pressure for gastrointestinal nematodes (GINs) in cattle, and to evaluate possible adaptive control measures. This review aims to consolidate progress in this field to facilitate further modeling and application. Trends: Mathematical and computer models are now available to simulate climate- and management-driven transmission dynamics in a range of host and gastrointestinal parasite species. Key recent advances include the ability to formally incorporate uncertainty into parameter estimates, and into future climate and management projections, as a result of increased computer power. The advent of high-throughput diagnostics for parasite infections in farmed ruminants brings opportunities for model validation using large datasets, which were not realistically possible when the model frameworks were originally conceived. These models provide a useful contribution to our understanding of parasite epidemiology in alternative conditions, and should be further applied and refined.