Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Machine learning for predicting climate change impacts on Pseudopithomyces chartarum spore counts: a risk indicator of facial eczema(Taylor and Francis Group, 2025-11-09) Wada M; Sagarasaeranee O; Cogger N; Marshall J; Cuttance E; Macara G; Sood A; Vallee EAims To determine the importance of 11 climate variables on pasture spore count of Pseudopithomyces chartarum, a risk indicator of facial eczema (FE), and to forecast spore counts in New Zealand until 2100, using longitudinal P. chartarum pasture spore count data. Methods Between 2010 and 2017, spore counts (n = 6,975) were collected from 862 paddocks spread over 102 farms in the North Island of New Zealand. Historical and projected climate data were obtained from the National Institute of Water and Atmospheric Research. The spore count dataset was merged with climate data from corresponding locations, incorporating time lags of 1–53 weeks. Linear regression models were fitted for describing crude associations, while random forest models were fitted for determining variable importance and predicting future spore counts. Results Mixed-effect linear regression models explained up to 11% of the variance of log-transformed spore counts by a single lagged climate covariate. The best-fit random forest model had a testing accuracy of 80% in classifying low or high FE risk (> 20,000 spores) with an R2 value of 43%. The random forest models suggested time-dependent importance of soil temperature at 10 cm depth, solar radiation, potential evapotranspiration, vapour pressure, soil moisture and minimum temperature, while no or weak evidence of variable importance was found for maximum temperature, rainfall, mean sea level atmospheric pressure, relative humidity and wind speed. Over the next 80 years, our model predicted an increase in the seasonal mean spore counts in the study farms by a mean of 17% (min 6, max 30%) under the high-end greenhouse gas emission scenario (representative concentration pathways (RCP) 8.5). Every decade was associated with an increase in the probability of high-risk spore counts (> 20,000) by 14–22% for the moderate to high emission scenarios (RCP 4.5–8.5). The model indicated increased peak spore counts across most regions over the next 80 years. Specifically, the entire North Island and three districts in the South Island were projected to have high mean peak spore counts by 2100. Conclusions and clinical relevance These findings could be used to target high-risk areas to implement mitigation or adaptation measures for FE. In addition, the study highlights the value of ecological data for forecasting environmental disease risks to enhance preparedness for climate change.Item The Effect of Climate Change on Emergence and Evolution of Zoonotic Diseases in Asia(Wiley-VCH GmbH, 2025-09-01) Morris RS; Wada MAs the climate of Asia changes under the influence of global warming, the incidence and spatial distribution of known zoonoses will evolve, and new zoonoses are expected to emerge as a result of greater exposure to organisms which currently occur only in wildlife. In order to evaluate the risks attached to different transmission methods and organism maintenance mechanisms, a classification system is provided which allocates diseases into nine epitypes. All animal diseases and zoonoses recognised as globally important can be categorised into an epitype, or in a few cases more than one epidemiologically distinct epitype. Within each epitype, evidence available on the effects of climatic factors is provided for selected diseases of zoonotic importance to illustrate likely future evolution of these diseases and the extent of currently available evidence for different diseases. Factors which are likely to influence the emergence of novel zoonotic pathogens in Asia are outlined. The range of methods available for analysis, prediction, and evaluation of likely changes in disease occurrence under the influence of climate change has grown rapidly; an introduction is given to the types of tools now available. These methods will need to be integrated into a surveillance and response strategy for Asia, and an approach to achieve this is outlined.Item The effects of rain and flooding on leptospirosis incidence in sheep and cattle in New Zealand(Taylor and Francis Group on behalf of the New Zealand Veterinary Association, 2025-08-12) Sadler E; Vallee E; Watts J; Wada MAims To describe the spatio-temporal patterns of leptospirosis case counts in sheep and cattle in New Zealand, and to assess their association with climate variables indicative of flooding and surface runoff. As livestock are a major reservoir of Leptospira spp. and an important source of zoonotic transmission, understanding these patterns is critical for informing livestock and public health interventions in the context of climate change. Methods Confirmed cases of bovine and ovine leptospirosis from January 2011 to December 2023 were extracted from the Ministry for Primary Industries’ Animal Health Surveillance programme. Climate data was sourced from the National Institute of Water and Atmospheric Research. Using the χ2 test and Poisson regression models, the association between district-level case counts and four climate indices were examined: seasonal mean rainfall, seasonal frequency of extreme rainfall, seasonal mean soil moisture, and seasonal frequency of estimated surface runoff. Results Findings indicated an average of 13 confirmed cases for sheep annually, with notable surges in 2017 (34 cases) and 2023 (36 cases), aligning with extreme climate events. Poisson regression models for sheep leptospirosis identified significant associations with extreme rainfall (incidence risk ratio (IRR) = 5.03; 95% CI = 1.18–21.45), mean rainfall (IRR = 1.25; 95% CI = 1.15–1.36), surface runoff (IRR = 1.09; 95% CI = 1.04–1.15), and soil moisture (IRR = 1.03; 95% CI = 1.02–1.03). Cattle leptospirosis was positively associated with surface runoff (IRR = 1.06; 95% CI = 1.02–1.10) and soil moisture (IRR = 1.01; 95% CI = 1.00–1.01). Associations with extreme rainfall (IRR = 1.46; 95% CI = 0.49–4.31) and mean rainfall (IRR = 1.07; 95% CI = 1.00–1.14) were not statistically significant. Conclusions The outcomes of this study provide new evidence linking extreme rainfall, surface runoff, and other climate variables with increased leptospirosis case counts in sheep, with less pronounced but notable associations in cattle. These findings highlight the vulnerability of livestock to climate-driven disease pressures and suggest that future extreme weather events may increase the risk of leptospirosis outbreaks. This has important implications for targeted vaccination, surveillance, and public health preparedness in flood-prone rural regions of New Zealand.
