Related Experiment Video
Updated: Jul 17, 2026

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus
Published on: November 30, 2014
Predicting the unpredictable? A climate-based model of the timing of peak pasture infectivity for Dictyocaulus
C McCarthy1, H Rose Vineer2, E R Morgan3
1University of Liverpool, Department of Epidemiology and Population Health, Institute of Infection and Global Health, Liverpool CH64 7TE, UK.
Abstract:
Outbreaks of cattle lungworm disease (Dictyocaulus viviparus) are explosive and costly. The unpredictability of the disease often encourages farmers to apply blanket anthelmintic treatments to the herd, which impede the acquisition of immunity, increase the risk of drug resistance, and interfere with efforts to reduce anthelmintic use against ubiquitous gastrointestinal nematodes. Improving our understanding of the factors which lead to a high risk of infection with lungworm, (including climatic pressure), would support a more targeted management. We present GLOWORM-FL-DV, the first mathematical model of the free-living stages of D. viviparus. The ecology of D. viviparus is unique compared with other strongylid nematodes due to its relationship with Pilobilus spp. fungi, which enhance the transmission potential. The role of the fungi was therefore incorporated into the model framework, informed by laboratory observations of Pilobolus spp. development and sporulation. The thermal niche of D. viviparus was characterised based on published and laboratory observations. Mortality of parasitic larvae increased significantly below 0oC, and larval development occurred above 1.4oC, whereas the estimated minimum temperature for migration via Pilobolus spp. was 8.8oC. Model predictions were compared with antibody levels in bulk milk tank samples collected at two-weekly intervals from eight dairy herds across Great Britain over two grazing seasons. The model predicted high levels of larval abundance on pasture 46 days (38-52 days) before a rise in antibody levels and 22-26 days before the onset of clinical signs. The model assesses the impact of climate and weather on lungworm larval availability at pasture and provides a framework for the development of a risk forecasting system. This could help to focus vigilance for clinical signs at high-risk times and facilitate the targeted use of anthelmintics to prevent outbreaks, in support of sustainable parasite control.
Related Concept Videos
What is Climate?
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
Causality in Epidemiology
Steps in Outbreak Investigation
Microbes and Climate Change
Infectious Diseases and Their Occurrence

