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Predicting the periodic risk of anthrax in livestock in Victoria, Australia, using meteorological data

  • T. BrownlieEmail author
  • T. Bishop
  • M. Parry
  • S. E. Salmon
  • J. C. Hunnam
Original Paper
  • 5 Downloads

Abstract

Cases of anthrax in livestock are infrequently and irregularly reported in the state of Victoria, Australia; however, their impact on individual livestock, farming communities and the government agencies tasked with containing these outbreaks is high. This infrequency has been anecdotally associated with differences in annual and local weather patterns. In this study, we used historical anthrax cases and meteorological data from weather stations throughout Victoria to train a generalized linear mixed effects model to predict the daily odds of a case of anthrax occurring in each shire in the coming 30 days. Meteorological variables were transformed to deviations from the mean values for temperature or cumulative values for rainfall in the shire across all years. Shire was incorporated as a random effect to account for meteorological variation between shires. The model incorporated a post hoc weighting for the frequency of historic cases within each shire and the spatial contribution of each shire to the recently redefined Australian Anthrax Belt. Our model reveals that anthrax cases were associated with drier summer conditions (OR 0.96 (95% CI 0.95–0.97) and OR 0.98 (95% CI 0.97–0.99) for every mm increase in rainfall during September and December, respectively) and cooler than average spring (OR 0.20 (95% CI 0.11–0.52) for every °C increase in minimum daily temperature during November and warmer than average summer temperatures (OR 1.45 (95% CI 1.29–1.61) for every °C increase in maximum daily temperature during January. Cases were also preceded by a 40-day period of cooler, drier temperatures (OR 0.5 (95% CI 0.27–0.74) for every °C increase in maximum daily temperature and OR 0.96 (95% CI 0.95–0.97) for every mm increase in rainfall followed by a warmer than average minimum (or nightly) temperature 10 days immediately before the case (OR 1.46 (95% CI 1.35–1.58) for every °C increase in maximum daily temperature). These coefficients of this training model were then applied daily to meteorological data for each shire, and output of these models was presented as a choropleth and timeline plot in a Shiny web application. The application builds on previous spatial modelling and provides Victorian agencies with a tool to engage at-risk farmers and guide discussions towards anthrax control. This application can contribute to the wider rejuvenation of anthrax knowledge and control in Victoria and corroborates the anecdote that increased odds of disease can be linked to meteorological events.

Keywords

Anthrax Livestock Predictive model Meteorology 

Notes

Funding information

This study was funded by Agriculture Victoria, Department of Economic Development, Jobs, Transport and Resources, Attwood, Victoria, Australia.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Barro AS, Fegan M, Moloney B, Porter K, Muller J, Warner S, Blackburn JK (2016) Redefining the Australian Anthrax Belt: modeling the ecological niche and predicting the geographic distribution of bacillus anthracis. PLoS Negl Trop Dis 10(6):e0004689.  https://doi.org/10.1371/journal.pntd.0004689 CrossRefGoogle Scholar
  2. Bennett C (1975) Up hierarchy. Journal of extension 13 (MAR-A):7-12Google Scholar
  3. Blackburn JK, Goodin DG (2013) Differentiation of springtime vegetation indices associated with summer Anthrax epizootics in West Texas, USA, deer. J Wildl Dis 49(3):699–703.  https://doi.org/10.7589/2012-10-253 CrossRefGoogle Scholar
  4. Brownlie TS, Holmes I, Delahunty H, Salmon S, Hunnam JC (2019) Perceptions of anthrax in livestock from Victorian dairy farmers in the Goulburn-Murray region of Victoria, Australia. Aust Vet J.  https://doi.org/10.1111/avj.12844 CrossRefGoogle Scholar
  5. Chang W, Cheng J, Allaire J, Xie Y, McPherson J (2017) Shiny: web application framework for R. R package version 1.0.3. Edn.Google Scholar
  6. De Vos A, Cumming GS, Cumming DHM, Ament JM, Baum J, Clements HS, Grewar JD, Maciejewski K, Moore C (2016) Pathogens, disease, and the social-ecological resilience of protected areas. Ecol Soc 21 (1). doi: https://doi.org/10.5751/es-07984-210120
  7. Fasanella A, Galante D, Garofolo G, Jones MH (2010) Anthrax undervalued zoonosis. Vet Microbiol 140(3–4):318–331.  https://doi.org/10.1016/j.vetmic.2009.08.016 CrossRefGoogle Scholar
  8. Hugh-Jones ME, de Vos V (2002) Anthrax and wildlife. Revue Scientifique Et Technique De L Office International Des Epizooties 21 (2):359–383CrossRefGoogle Scholar
  9. Jubb TF (2007) A review of anthrax in Victoria 2007. Department of Primary Industries, VictoriaGoogle Scholar
  10. Mock M, Fouet A (2001) Anthrax. Annu Rev Microbiol 55:647–671.  https://doi.org/10.1146/annurev.micro.55.1.647 CrossRefGoogle Scholar
  11. Pepe MS, Cai TX, Longton G (2006) Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62(1):221–229.  https://doi.org/10.1111/j.1541-0420.2005.00420.x CrossRefGoogle Scholar
  12. Porter K, Macfarlane-Berry L, Mohammad I, Warner S, Wilks C, Firestone S, Fegan M (2016) Serological surveillance for anthrax in healthy Victorian cattle. Epidemiological Chapter Proceedings of the Science Week Scientific Meeting of the Australian and New Zealand College of Veterinary ScientistsGoogle Scholar
  13. R Core Team (2017) R: A language and environment for statistical computingGoogle Scholar
  14. Turnbull PCB (2002) Introduction: anthrax history, disease and ecology. Anthrax 271:1–19CrossRefGoogle Scholar
  15. Turner AJ, Galvin JW, Rubira RJ, Miller GT (1999) Anthrax explodes in an Australian summer. J Appl Microbiol 87(2):196–199.  https://doi.org/10.1046/j.1365-2672.1999.00869.x CrossRefGoogle Scholar
  16. Wang YJ, Chen HH, Li RZ, Duan NH, Lewis-Fernandez R (2011) Prediction-based structured variable selection through the receiver operating characteristic curves. Biometrics 67(3):896–905.  https://doi.org/10.1111/j.1541-0420.2010.01533.x CrossRefGoogle Scholar

Copyright information

© ISB 2020

Authors and Affiliations

  1. 1.Working Formula LtdDunedinNew Zealand
  2. 2.Department of Mathematics and StatisticsUniversity of OtagoDunedinNew Zealand
  3. 3.Agriculture Victoria, Department of Economic Development, Jobs, Transport and ResourcesAttwoodAustralia

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