Predicting seasonal fluctuation in ovine haemonchosis in contrasting eco-climatic zones using time series analysis
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1
The Institute for Global Food Security, Faculty of Medicine, Health and Life Sciences, Queen's University Belfast, United Kingdom
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2
National Veterinary Research Institute (NVRI), Nigeria
Infection with H. contortus remains one of the most pervasive challenges to sheep production worldwide, particularly in the tropics and sub-tropical regions. Development and survival of the free-living stages of H. contortus depend strongly on the prevailing temperature and water availability. Hypothetically, seasonal patterns and incidence of haemonchosis should be predictable from information on key climatic variables both in time and space. Statistical models are useful to determine the seasonal influence of climate in the pattern of disease incidence without full understanding of the mechanism behind them. These models have been used to successfully deduce the relationships between climatic variables and disease incidence to predict disease incidence in space and time. Predictive models for haemonchosis are needed to underpin an integrated-sustainable control.
This study sets forth to determine if variations in climatic variables are able to consistently predict incidence of haemonchosis in different eco-climatic areas, with contrasting prevailing climate. Our ultimate aim was to develop reliable and stable regional models capable of predicting haemonchosis occurrence from climatic conditions.
We used time series analyses to assess the seasonal forcing influence of climate (mainly rainfall and temperature) on the pattern and incidence of haemonchosis in UK (2004-2011), North Yorkshire (2001-2006) and in a South African farm (1997-2000). This was to find out if statistical approach can be used to predict the pattern and risk of haemonchosis across different eco-climatic zones.
Our results indicate that time series analysis was able to identify predictors of seasonal pattern of haemonchosis incidence for each of the three locations. However, the main predictors and the relationships between them and haemonchosis incidence varied from zone to zone; thus one model could not be used in all locations. This suggests that if this statistical approach is to be used on a previously unstudied farm, a new time series analysis has to be performed, which critically depends on the availability of quality data on both haemonchosis incidence and current climatic situation. If this is the case, time series modelling appears to be a promising starting-point approach to examine local or regional climate-haemonchosis links. Another implication of our study is that predicting patterns and incidence of haemonchosis may benefit from the inclusion of outputs of climate-based mechanistic models of Haemonchus contortus and non-climatic factors such as husbandry systems.
Keywords:
climate,
Haemonchosis,
Model-validation,
Time-series-analysis,
South Africa,
United Kindgom
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Regular oral presentation
Topic:
Real-time field data collection and visualization platforms
Citation:
Morgan
E and
Bolajoko
M
(2019). Predicting seasonal fluctuation in ovine haemonchosis in contrasting eco-climatic zones using time series analysis.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00091
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Received:
10 Jun 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Muhammad-Bashir Bolajoko, National Veterinary Research Institute (NVRI), Vom, Nigeria, mbbolajoko@gmail.com