Center of Excellence in Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai 50100, Thailand.
Excellence Center in Veterinary Bioscience, Chiang Mai University, Chiang Mai 50100, Thailand.
Viruses. 2022 Jun 23;14(7):1367. doi: 10.3390/v14071367.
Thailand is one of the countries where foot and mouth disease outbreaks have resulted in considerable economic losses. Forecasting is an important warning technique that can allow authorities to establish an FMD surveillance and control program. This study aimed to model and forecast the monthly number of FMD outbreak episodes (n-FMD episodes) in Thailand using the time-series methods, including seasonal autoregressive integrated moving average (SARIMA), error trend seasonality (ETS), neural network autoregression (NNAR), and Trigonometric Exponential smoothing state−space model with Box−Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and hybrid methods. These methods were applied to monthly n-FMD episodes (n = 1209) from January 2010 to December 2020. Results showed that the n-FMD episodes had a stable trend from 2010 to 2020, but they appeared to increase from 2014 to 2020. The outbreak episodes followed a seasonal pattern, with a predominant peak occurring from September to November annually. The single-technique methods yielded the best-fitting time-series models, including SARIMA(1,0,1)(0,1,1)12, NNAR(3,1,2)12,ETS(A,N,A), and TBATS(1,{0,0},0.8,{<12,5>}. Moreover, SARIMA-NNAR and NNAR-TBATS were the hybrid models that performed the best on the validation datasets. The models that incorporate seasonality and a non-linear trend performed better than others. The forecasts highlighted the rising trend of n-FMD episodes in Thailand, which shares borders with several FMD endemic countries in which cross-border trading of cattle is found common. Thus, control strategies and effective measures to prevent FMD outbreaks should be strengthened not only in Thailand but also in neighboring countries.
泰国是口蹄疫疫情导致重大经济损失的国家之一。预测是一种重要的预警技术，可以使当局能够建立口蹄疫监测和控制计划。本研究旨在使用时间序列方法，包括季节性自回归综合移动平均 (SARIMA)、误差趋势季节性 (ETS)、神经网络自回归 (NNAR) 和带 Box-Cox 变换、ARMA 误差、趋势和季节性成分的三角指数平滑状态空间模型 (TBATS)，以及混合方法，对口蹄疫每月爆发次数 (n-FMD 爆发次数) 进行建模和预测。这些方法应用于 2010 年 1 月至 2020 年 12 月的每月 n-FMD 爆发次数 (n = 1209)。结果表明，n-FMD 爆发次数从 2010 年到 2020 年呈稳定趋势，但从 2014 年到 2020 年呈上升趋势。爆发次数呈季节性模式，每年 9 月至 11 月期间出现明显高峰。单一技术方法产生了最佳拟合的时间序列模型，包括 SARIMA(1,0,1)(0,1,1)12、NNAR(3,1,2)12、ETS(A,N,A)和 TBATS(1,{0,0},0.8,{<12,5>})。此外，SARIMA-NNAR 和 NNAR-TBATS 是在验证数据集中表现最好的混合模型。纳入季节性和非线性趋势的模型比其他模型表现更好。预测结果突出了泰国 n-FMD 爆发次数的上升趋势，泰国与几个口蹄疫流行国家接壤，这些国家之间经常进行牛的跨境贸易。因此，不仅在泰国，而且在邻国都应加强口蹄疫爆发的控制策略和有效措施。
