Development of a transmission model for gastro-intestinal nematode infections in cattle

Gastro-intestinal nematodes (GIN) are one of the great threats for farmed ruminants worldwide. Mathematical models that simulate the dynamics of GIN infections have great potential to provide improved understanding of parasite epidemiology under altered conditions and to underpin the development of alternative parasite control strategies. In chapter 1, first the general epidemiology of GIN in ruminants is discussed to provide insight in the dynamics and underlying drivers of the host-parasite interaction. Host immunity, weather and farm management are shown to be significant drivers of parasite epidemiology. The second part of chapter 1 discusses the evolution of both hosts and parasites during the past half century, the expected trends to come and the underlying drivers of these anticipated changes. Finally, the value of transmission models to improve our understanding of parasite epidemiology under changing conditions and to facilitate the development of control strategies is discussed. Key terms encountered in the field of parasitic disease modelling are explained and the development process of these models is given. An overview of the available models for GIN infections in ruminants provides insights into the needs for this field of research. The overall objective of this PhD project was to develop a generic framework for a mechanistic transmission model that simulates the parasitic phase of the GIN lifecycle in farmed ruminants. Further, facilitation of the collection of pasture larval count data, a key input parameter, was explored. Chapter 2 quantifies the main life history traits of the parasitic phase for O. ostertagi and Cooperia oncophora through systematic review and meta-analysis and assesses the potential influences associated with the effect of immunity on these traits. The main parameters determining parasite density during the parasitic phase are the larval establishment rate or pre-adult mortality, the hypobiosis rate, adult mortality and female fecundity. A systematic review was performed covering studies from 1962 to 2007, in which helminth-naive calves were artificially infected with O. ostertagi and/or C. oncophora. The database was further extended with results of unpublished trials conducted at the Laboratory for Parasitology of Ghent University, Belgium. Overall inverse variance weighted estimates were computed for each of the traits through random effects models. To our knowledge, this systematic review is the first to summarize the available data on the main life history traits of the parasitic phase of O. ostertagi and C. oncophora and provides novel estimates for the parameterization of life cycle-based transmission models. Chapter 3 presents a flexible model framework (GLOWORM-PARA) developed for the parasitic phase of GINs infecting ruminants. The framework can be applied to a range of GIN species and is parameterised and thoroughly validated for first season grazing calves infected by two species that are of major importance in cattle, i.e. O. ostertagi and C. oncophora. To our knowledge, no previous attempt has been made to model C. oncophora. For O. ostertagi, GLOWORM-PARA incorporates important improvements to the existing models such as data-driven parameterisation of the rate of acquisition of immunity based on cumulative exposure and the incorporation of host grazing behaviour. Both the parameterisation and validation of these models were backed by extensive datasets obtained from various sources and acquired over decades of parasitological research. This represents the most comprehensive and thorough validation of GIN models to date. The model was able to generate the general patterns of faecal egg counts seen in first season grazing cattle throughout the grazing season. The estimation of the immune response rate from field observations was preferred over fitting the immune response rate to get meaningful predictions of acquired immunity. Linear regression of predictions against observations showed that incorporating host grazing behaviour resulted in an important improvement of model performance and is therefore likely to be important in the transmission of GIN. Assessing levels of pasture larval contamination is frequently used to study the population dynamics of the free-living stages of parasitic nematodes of livestock and the abundance of infective larvae (L3) on pasture is an important input parameter for GLOWORM-PARA. Direct quantification of L3 on herbage is the most applied method to measure pasture larval contamination, but herbage collection remains labour intensive. Chapter 4 compares two different sampling methods in terms of pasture larval count results and time required to sample, to assess the amount of variation in larval counts at the level of sample plot, pasture and season, respectively and to calculate the required sample size to assess pasture larval contamination with a predefined precision using random plots across pasture. Chapter 5 discusses the results and limitations of this work along with opportunities for future research. The integration of GLOWORM-PARA with a complementary model which simulates the free-living stages of GINs, GLOWORM-FL, should lead to a full life cycle based model in further research. To improve the link between the free-living and the parasitic phase, future research needs to assess the daily faecal production based on easy-to-use predictors such as body weight. The incorporation of a component that models grass growth can provide the needed complexity to account for different farm management situations and to underpin meaningful larval infection rates. Several questions remain concerning the implementation of transmission models as site-specific decision support tools for nematode control. A proposed approach to achieve better and more applied modelling is to gradually refine generic models with the needed amount of biological detail. Obtaining relevant and realistic parameter estimates and integrating these in generic models might be a good step to achieve the right balance between generality and specificity. Efforts to facilitate data quality and collection should be encouraged, as this is fundamental to make progress and underpins the future implementation of models. Future research should also focus on how to improve knowledge transfer to the end-users and to identify user-needs.