Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods.

Lumpy skin disease (LSD) is an important transboundary disease affecting cattle in numerous countries in various continents. In Thailand, LSD is regarded as a serious threat to the cattle industry. Disease forecasting can assist authorities in formulating prevention and control policies. Therefore, the objective of this study was to compare the performance of time series models in forecasting a potential LSD epidemic in Thailand using nationwide data. For the forecasting of daily new cases, fuzzy time series (FTS), neural network auto-regressive (NNAR), and auto-regressive integrated moving average (ARIMA) models were applied to various datasets representing the different stages of the epidemic. Non-overlapping sliding and expanding window approaches were also employed to train the forecasting models. The results showed that the FTS outperformed other models in five of the seven validation datasets based on various error metrics. The predictive performance of the NNAR and ARIMA models was comparable, with NNAR outperforming ARIMA in some datasets and vice versa. Furthermore, the performance of models built from sliding and expanding window techniques was different. This is the first study to compare the forecasting abilities of the FTS, NNAR, and ARIMA models across multiple phases of the LSD epidemic. Livestock authorities and decision-makers may incorporate the forecasting techniques demonstrated herein into the LSD surveillance system to enhance its functionality and utility.

Exploring the predictive capability of machine learning models in identifying foot and mouth disease outbreak occurrences in cattle farms in an endemic setting of Thailand.

Occurrences of foot and mouth disease (FMD) outbreaks in cattle farms in Thailand have been significantly harmful to the cattle industry for the past decade. A prediction of FMD outbreaks based on relevant risk factors with a high prediction accuracy is important for authorities to develop a plan for preventing the outbreaks. Data-driven tools are widely accepted for their prediction abilities, but an application of these techniques to FMD outbreak prediction is very limited. The objectives of this study were to develop prediction models of FMD outbreaks among cattle farms using machine learning (ML) classification algorithms including classification tree (CT), random forests (RF), and Chi-squared automatic interaction detection (CHAID) and to compare the predictive performance of the developed models. Data from 225 FMD and 608 non-FMD outbreak farms from an endemic setting were analyzed using ML methods. The CT, RF, and CHAID methods were utilized to develop predictive models, and their prediction capabilities were compared. The results showed that models developed using ML methods have an acceptable to excellent ability to predict the occurrence of FMD outbreaks. The RF model had the highest accuracy and the value of area under the operating characteristic curve in predicting the occurrence of an FMD outbreak. Meanwhile, the CT and CHAID models delivered comparable results. In this study, we demonstrated the capability of machine learning algorithms to predict FMD outbreaks using actual FMD outbreak data from the endemic setting and provided a new insight into the prediction of FMD outbreaks. The ML techniques demonstrated herein may be used as a prediction tool by the relevant authorities to predict the occurrence of FMD outbreaks in cattle farms.

Bacterial contamination of chicken meat in slaughterhouses and the associated risk factors: A nationwide study in Thailand.

Slaughterhouses are a key source of bacterial contamination in poultry meat and products, which is a major health and economic concern for several public authorities. This study aimed to quantify the non-compliance of bacterial contamination on chicken meat sampled from slaughterhouses and identify risk factors associated with the contamination. A questionnaire survey of 569 chicken slaughterhouses was undertaken and 1,707 meat samples were collected to determine the level of bacterial contamination. The proportion of the non-compliance associated with aerobic plate count [APC] (24.6%), Staphylococcus aureus (6.3%), Enterococcus spp. (24.7%), coliforms (13.5%), Escherichia coli (33.3%), and Salmonella spp. (33.4%) based on the livestock authorities' criteria was determined. Our results highlighted that the scalding process without scalding water temperature control or improper scalding increased the risk of APC (odds ratio, OR = 4.84, 95% CI: 2.72-8.61), S. aureus (OR = 2.68, 95% CI: 1.29-5.55), Enterococcus spp. (OR = 3.38, 95% CI: 2.01-5.69), coliforms (OR = 3.01, 95% CI: 1.47-6.15), and E. coli (OR = 2.69, 95% CI: 1.58-4.56) contamination on meat samples. Meat from eviscerated carcasses was more likely to be non-compliance due to contamination by E. coli (OR = 1.96, 95% CI: 1.14-3.38). Furthermore, open or semi-closed system slaughterhouses (OR = 1.79, 95% CI: 1.23-2.60) and lack of equipment for specific slaughtering areas (OR = 1.65, 95% CI: 1.04-2.61) increased the likelihood of Salmonella spp. occurrence. This is the first study of factors influencing the non-compliance of meat samples across Thailand. Authorities can use the study findings to enhance food safety strategies at the national level.

First study on assessments of farmers' benefits under a payment program based on dairy milk quality in Thailand.

Background and Aim: To improve overall milk quality in Thailand, dairy farmers and milk collection centers employ a payment program based on milk quality (PPBMQ) for milk trade. This study aimed to determine and compare the proportion of dairy farmers receiving benefits from the PPBMQ using data from selected dairy cooperatives located in northern and central regions in Thailand. Materials and Methods: Monthly data on milk components (n=37,077), including fat, solids not fat (SNF), and somatic cell counts (SCC) were collected from the two regions in 2018 and 2019. Based on the PPBMQ, farmers were classified into benefit-gain, benefit-loss, and no-benefit groups. A mixed-effects logistic regression model was used to compare the number of farmers in northern and central regions who received monthly benefits from the PPBMQ. Results: More than 70% of dairy farmers benefited from the PPBMQ. The proportion of dairy farmers in the benefit-gain group was higher in the northern region (88.7%) than in the central region (57.1%). A high percentage of dairy farmers in the central region lost their benefits mainly due to SCC (40%) and SNF (44%). Conclusion: The PPBMQ benefited the vast majority of dairy producers in the northern region and approximately two-thirds of those in the central region. Thus, the efforts of authorities and stakeholders should be enhanced to support dairy farmers in the central region in improving milk quality.

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models.

Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.