Forecasting Rabies in Dogs in Thailand: Time Series Analysis
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Abstract
Background: Rabies is a dangerous infectious disease spread from dogs to humans by bites from rabid dogs. There is a lack of studies investigating trends of rabies in dogs in Thailand. The objectives of this study were to investigate trends in rabies in dogs in Thailand and use this to develop a model to predict the number of dogs with rabies in Thailand.
Methods: The number of dogs with rabies between 2013 and 2017 were retrieved from the rabies surveillance report system of the Bureau of Disease Control and Veterinary Services in Thailand. Time series analysis using Holt-Winter and Box-Jenkins methods were performed to create a predictive model. Akaike information criterion (AIC) and Root Mean Square Error (RMSE) were used to choose an appropriated model for predicting the number of dogs with rabies in Thailand in 2018.
Results: During 2013-2017 there was a significant increase in the number of rabid dogs in northeastern Thailand. Using the Seasonal Autoregressive Integrated Moving Average (SARIMA (1, 1, 0) (0, 1, 1)12) predictive model, the number of rabid dogs in Thailand was predicted to be highest in December 2018.
Conclusion: The number of dogs with rabies in Thailand is increasing and the SARIMA predictive model was the most suitable for forecasting the number of rabid dogs that might be found in Thailand at national and regional levels.
Keywords: Forecasting, Rabies dogs, Modelling, Time series
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