Analysis of factors associated with the first lumpy skin disease outbreaks in naïve cattle herds in different regions of Thailand.

Introduction: Thailand experienced a nationwide outbreak of lumpy skin disease (LSD) in 2021, highlighting the need for effective prevention and control strategies. This study aimed to identify herd-level risk factors associated with LSD outbreaks in beef cattle herds across different regions of Thailand. Methods: A case-control study was conducted in upper northeastern, northeastern, and central regions, where face-to-face interviews were conducted with farmers using a semi-structured questionnaire. Univariable and multivariable mixed effect logistic regression analyses were employed to determine the factors associated with LSD outbreaks. A total of 489 beef herds, including 161 LSD outbreak herds and 328 non-LSD herds, were investigated. Results and discussion: Results showed that 66% of farmers have operated beef herds for more than five years. There were very few animal movements during the outbreak period. None of the cattle had been vaccinated with LSD vaccines. Insects that have the potential to act as vectors for LSD were observed in all herds. Thirty-four percent of farmers have implemented insect control measures. The final mixed effect logistic regression model identified herds operating for more than five years (odds ratio [OR]: 1.62, 95% confidence interval [CI]: 1.04-2.53) and the absence of insect control management on the herd (OR: 2.05, 95% CI: 1.29-3.25) to be associated with LSD outbreaks. The implementation of insect-vector control measures in areas at risk of LSD, especially for herds without vaccination against the disease, should be emphasized. This study provides the first report on risk factors for LSD outbreaks in naïve cattle herds in Thailand and offers useful information for the development of LSD prevention and control programs within the country's context.

Utilizing liposomal encapsulation approach to address nephrotoxic challenges of colistimethate sodium through a preclinical study.

The use of Colistin, a last-resort antimicrobial drug, carries the risk of acute kidney injury. The objective of the study was to assess the effectiveness of colistin-encapsulated liposomes (CL) in reducing nephrotoxicity. Additionally, a liposomal preparation of colistimethate sodium was formulated using the reverse phase evaporation method with a 3:1 ratio of phospholipids to cholesterol. The liposomal properties were evaluated using scanning electron microscopy, photon correlation spectroscopy, and release kinetic assay. The killing kinetics of the formulations on embryonic kidney cells were assessed using in vitro MTT reduction assay. The nephrotoxicity of CL and colistimethate sodium solution (CS) was evaluated in vivo by administering a dose of 20 mg/kg to rats every 12 h for 3 days, with a negative control group receiving a 0.9% saline solution (NSS). The study results revealed that monodisperses of CL showed a smooth surface and distinct boundaries, with an average size of 151.50 ± 0.46 nm and a narrow size distribution of 0.25 ± 0.01. The liposomal particles showed high entrapment efficiency of 96.45% ± 0.41%, with a ζ-potential of -60.80 ± 1.01 mV and a release rate of 50% of colistimethate sodium within the first 480 min. The CL induced nephrocytotoxicity in a concentration- and time-dependent manner. However, CS had notably lower IC50 values compared to its liposome preparations at 48 and 72 h (p < 0.05). In vivo study results show that serum levels of symmetric dimethylarginine (SDMA) and total white blood cell count (WBC) were significantly lower in the CL group (SDMA = 8.33 ± 1.70 µg/dL; WBC = 7.29 ± 0.99 log10 cells/mL) compared to the CS group (SDMA = 15.00 ± 1.63 µg/dL; WBC = 9.73 ± 0.51 log10 cells/mL). Our study findings enhance the understanding of the safety profile of CL and its potential to improve patient outcomes through the use of liposomal colistin medication. Additional clinical studies are necessary to establish the optimal safety regiment in humans.

Identifying the patterns and sizes of the first lumpy skin disease outbreak clusters in Northern Thailand with a high degree of dairy farm aggregation using spatio-temporal models.

Lumpy skin disease (LSD) is one of the most important notifiable transboundary diseases affecting cattle in many parts of the world. In Thailand, LSD outbreaks in cattle farming areas have been reported in 69 out of 77 provinces, indicating a serious nationwide situation. Understanding the dynamics of spatial and temporal LSD epidemic patterns can provide important information on disease transmission and control. This study aims to identify spatial and temporal clusters in the first LSD outbreaks in dairy farming areas with a high degree of aggregation in Northern Thailand using spatio-temporal models. The data were obtained from an official LSD outbreak investigation conducted between June and August 2021 on dairy farms (n = 202). The outbreak of LSD was confirmed by employing clinical observations and laboratory analysis. The spatio-temporal models including space-time permutation (STP), Poisson, and Bernoulli were applied to the outbreak data with the settings of 10%, 25%, and 50%, respectively, for the maximum reported cluster size (MRCS). Overall, the number of most likely and secondary clusters varied depending on the model and MRCS settings. All MRCS settings in the STP model detected the most likely clusters in the same area and the Poisson models in different areas, with the largest being defined by a 50% MRCS. Although the sizes of the most likely clusters identified by the Bernoulli models were different, they all had the same cluster period. Based on the sizes of the detected clusters, strict LSD insect-vector control should be undertaken within one kilometer of the outbreak farm in areas where no LSD vaccination has been administered. This study determines the sizes and patterns of LSD outbreak clusters in the dairy farming area with a high degree of farm aggregation. The spatio-temporal study models used in this study, along with multiple adjusted MRCS, provide critical epidemiological information. These models also expand the options for assisting livestock authorities in facilitating effective LSD prevention and control programs. By prioritizing areas for resource allocation, these models can help improve the efficiency of such programs.

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.

The first study on the impact of lumpy skin disease outbreaks on monthly milk production on dairy farms in Khon Kaen, Thailand.

Background and Aim: Outbreaks of lumpy skin disease (LSD) have resulted in substantial economic losses to the dairy industry in Thailand. This study aimed to determine the influence of LSD outbreaks on monthly milk production levels. Materials and Methods: Milk production for dairy farms located in Khon Kaen Province, Thailand, belonging to the Khon Kaen Dairy Cooperative, was affected by LSD outbreaks from May to August of 2021. The resulting data were analyzed using general linear mixed models. Results: It was estimated that the LSD outbreak caused economic losses totaling 2,413,000 Thai Baht (68,943 USD) over the outbreak period. The monthly farm milk production level in May differed from the levels in June and August. Dairy farmers experienced losses between 8.23 and 9.96 tons of milk each month, which equated to between 4180 and 14,440 Thai Baht (119.43 and 412.57 USD) in monthly income. Conclusion: This study demonstrated that LSD outbreaks on dairy farms resulted in significant farm milk production losses. Our findings will increase awareness among authorities and stakeholders in the dairy industry of Thailand, as well as to assist in the prevention of future LSD outbreaks and minimize the negative impacts of LSD.

Spatio-temporal patterns of lumpy skin disease outbreaks in dairy farms in northeastern Thailand.

In 2021-2022, there were numerous outbreaks of lumpy skin disease (LSD) affecting cattle farms across Thailand. This circumstance was the country's first encounter with an LSD outbreak. Thus, a better understanding of LSD epidemiology is necessary. The aim of this study was to determine the spatio-temporal patterns of the LSD outbreaks in dairy farming areas. Data from LSD outbreak investigations collected from dairy farms in Khon Kean province, northeastern Thailand, were analyzed using spatio-temporal models including space-time permutation, Poisson, and Bernoulli models. LSD outbreaks were found in 133 out of 152 dairy farms from May to July, 2021. The majority of dairy farms (n = 102) were affected by the LSD outbreaks in June. The overall herd attack, morbidity and mortality rates were 87, 31, and 0.9%, respectively. According to the results of all models, the most likely clusters were found in the northern part of the study area. The space-time permutation and Poisson model identified 15 and 6 spatio-temporal outbreak clusters, respectively, while the Bernoulli model detected only one cluster. The most likely clusters from those models cover radii of 1.59, 4.51, and 4.44 km, respectively. All farms included in the cluster identified by the space-time permutation model were also included in the cluster identified by the Poisson model, implying that both models detected the same outbreak area. Furthermore, the study results suggested that farmers who own farms within a one km radius of the LSD outbreak farm should be advised to implement more stringent insect vector control measures to prevent disease spread. This study provides better insights into the spatio-temporal pattern of clusters of LSD in the outbreak area. The findings of this study can support authorities in formulating strategies to prevent and control future outbreaks as well as prioritizing resource allocation to high-risk areas.

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.

Time-Series Analysis for the Number of Foot and Mouth Disease Outbreak Episodes in Cattle Farms in Thailand Using Data from 2010-2020.

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.

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.

First study on diversity and antimicrobial-resistant profile of staphylococci in sports animals of Southern Thailand.

Background and Aim: Staphylococci are commensal bacteria and opportunistic pathogens found on the skin and mucosa. Sports animals are more prone to injury and illness, and we believe that antimicrobial agents might be extensively used for the treatment and cause the existence of antimicrobial-resistant (AMR) bacteria. This study aimed to investigate the diversity and AMR profile of staphylococci in sports animals (riding horses, fighting bulls, and fighting cocks) in South Thailand. Materials and Methods: Nasal (57 fighting bulls and 33 riding horses) and skin swabs (32 fighting cocks) were taken from 122 animals. Staphylococci were cultured in Mannitol Salt Agar and then identified species by biochemical tests using the VITEK® 2 card for Gram-positive organisms in conjunction with the VITEK® 2 COMPACT machine and genotypic identification by polymerase chain reaction (PCR). Antimicrobial susceptibility tests were performed with VITEK® 2 AST-GN80 test kit cards and VITEK® 2 COMPACT machine. Detection of AMR genes (mecA, mecC, and blaZ) and staphylococcal chromosomal mec (SCCmec) type was evaluated by PCR. Results: Forty-one colonies of staphylococci were isolated, and six species were identified, including Staphylococcus sciuri (61%), Staphylococcus pasteuri (15%), Staphylococcus cohnii (10%), Staphylococcus aureus (7%), Staphylococcus warneri (5%), and Staphylococcus haemolyticus (2%). Staphylococci were highly resistant to two drug classes, penicillin (93%) and cephalosporin (51%). About 56% of the isolates were methicillin-resistant staphylococci (MRS), and the majority was S. sciuri (82%), which is primarily found in horses. Most MRS (82%) were multidrug-resistant. Almost all (96%) of the mecA-positive MRS harbored the blaZ gene. Almost all MRS isolates possessed an unknown type of SCCmec. Interestingly, the AMR rate was notably lower in fighting bulls and cocks than in riding horses, which may be related to the owner's preference for herbal therapy over antimicrobial drugs. Conclusion: This study presented many types of staphylococci displayed on bulls, cocks, and horses. However, we found a high prevalence of MRS in horses that could be transmitted to owners through close contact activities and might be a source of AMR genotype transmission to other staphylococci.

Characterizing the antimicrobial resistance profile of Escherichia coli found in sport animals (fighting cocks, fighting bulls, and sport horses) and soils from their environment.

Background and Aim: Antimicrobial resistance (AMR) is a significant threat to global health and development. Inappropriate antimicrobial drug use in animals cause AMR, and most studies focus on livestock because of the widespread use of antimicrobial medicines. There is a lack of studies on sports animals and AMR issues. This study aimed to characterize the AMR profile of E. coli found in sports animals (fighting cocks, fighting bulls, and sport horses) and soils from their environment. Materials and Methods: Bacterial isolation and identification were conducted to identify E. coli isolates recovered from fresh feces that were obtained from fighting cocks (n = 32), fighting bulls (n = 57), sport horses (n = 33), and soils from those farms (n = 32) at Nakhon Si Thammarat. Antimicrobial resistance was determined using 15 tested antimicrobial agents - ampicillin (AM), amoxicillin-clavulanic acid, cephalexin (CN), cefalotin (CF), cefoperazone, ceftiofur, cefquinome, gentamicin, neomycin, flumequine (UB), enrofloxacin, marbofloaxacin, polymyxin B, tetracycline (TE), and sulfamethoxazole/trimethoprim (SXT). The virulence genes, AMR genes, and phylogenetic groups were also examined. Five virulence genes, iroN, ompT, hlyF, iss, and iutA, are genes determining the phylogenetic groups, chuA, cjaA, and tspE4C2, were identified. The AMR genes selected for detection were blaTEM and blaSHV for the beta-lactamase group; cml-A for phenicol; dhfrV for trimethoprim; sul1 and sul2 for sulfonamides; tetA, tetB, and tetC for TEs; and qnrA, qnrB, and qnrS for quinolones. Results: The E. coli derived from sports animals were resistant at different levels to AM, CF, CN, UB, SXT, and TE. The AMR rate was overall higher in fighting cocks than in other animals, with significantly higher resistance to AM, CF, and TE. The highest AMR was found in fighting cocks, where 62.5% of their isolates were AM resistant. In addition, multidrug resistance was highest in fighting cocks (12.5%). One extended-spectrum beta-lactamase E. coli isolate was found in the soils, but none from animal feces. The phylogenetic analysis showed that most E. coli isolates were in Group B1. The E. coli isolates from fighting cocks had more virulence and AMR genes than other sources. The AMR genes found in 20% or more of the isolates were blaTEM (71.9%), qnrB (25%), qnrS (46.9%), and tetA (56.25%), whereas in the E. coli isolates collected from soils, the only resistance genes found in 20% or more of the isolates were blaTEM (30.8%), and tetA (23.1%). Conclusion: Escherichia coli from fighting cock feces had significantly higher resistance to AM, CF, and TE than isolates from other sporting animals. Hence, fighting cocks may be a reservoir of resistant E. coli that can transfer to the environment and other animals and humans in direct contact with the birds or the birds' habitat. Programs for antimicrobial monitoring should also target sports animals and their environment.

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.

The First Lumpy Skin Disease Outbreak in Thailand (2021): Epidemiological Features and Spatio-Temporal Analysis.

The first outbreak of lumpy skin disease (LSD) in Thailand was reported in March 2021, but information on the epidemiological characteristics of the outbreak is very limited. The objectives of this study were to describe the epidemiological features of LSD outbreaks and to identify the outbreak spatio-temporal clusters. The LSD-affected farms located in Roi Et province were investigated by veterinary authorities under the outbreak response program. A designed questionnaire was used to obtain the data. Space-time permutation (STP) and Poisson space-time (Poisson ST) models were used to detect areas of high LSD incidence. The authorities identified 293 LSD outbreak farms located in four different districts during the period of March and the first week of April 2021. The overall morbidity and mortality of the affected cattle were 40.5 and 1.2%, respectively. The STP defined seven statistically significant clusters whereas only one cluster was identified by the Poisson ST model. Most of the clusters (n = 6) from the STP had a radius <7 km, and the number of LSD cases in those clusters varied in range of 3-51. On the other hand, the most likely cluster from the Poisson ST included LSD cases (n = 361) from 198 cattle farms with a radius of 17.07 km. This is the first report to provide an epidemiological overview and determine spatio-temporal clusters of the first LSD outbreak in cattle farms in Thailand. The findings from this study may serve as a baseline information for future epidemiological studies and support authorities to establish effective control programs for LSD in Thailand.

The first report of the seroprevalence of antibodies against Bartonella spp. in water buffaloes (Bubalus bubalis) from South Thailand.

BACKGROUND AND AIM: Bartonellosis is an emerging worldwide zoonosis caused by bacteria belonging to the genus Bartonella. Several studies have been conducted on the prevalence of Bartonella infections from animals and humans, including reports from wild and domestic ruminants. However, there has been only one report of Bartonella infection in water buffaloes from the northeastern part of Thailand. Moreover, the seroprevalence of Bartonella spp. in water buffaloes still remains unknown. This study was conducted to explore the prevalence of Bartonella spp. among water buffaloes from South Thailand using molecular and serological techniques. MATERIALS AND METHODS: A total of 312 samples (156 blood and 156 sera) of 156 water buffaloes from 29 farms in Phatthalung Province, South Thailand, were collected from January to March 2021. All samples were screened for Bartonella spp. using polymerase chain reaction and indirect immunofluorescence assay. RESULTS: The seroprevalence of antibodies against three Bartonella spp. was 16.03% (25/156, 95% confidence interval: 10.65-22.74%), and among 25 water buffaloes with seroprevalence, 56%, 20%, and 24% were positive for antibodies against Bartonella henselae, Bartonella vinsonii subspp. berkhoffii, and Bartonella tamiae, respectively. No significant difference was detected among seroprevalence, gender, age, and ectoparasite infestation. CONCLUSION: This is the first report of the seroprevalence of antibodies against B. henselae, B. vinsonii subspp. berkhoffii, and B. tamiae in water buffaloes from South Thailand. Further studies are required on the epidemiology of Bartonella infection among water buffaloes, related personnel, and ectoparasites.

Application of statistical process control for monitoring bulk tank milk somatic cell count of smallholder dairy farms.

BACKGROUND AND AIM: Consistency in producing raw milk with less variation in bulk tank milk somatic cell count (BMSCC) is important for dairy farmers as their profit is highly affected by it in the long run. Statistical process control (SPC) is widely used for monitoring and detecting variations in an industrial process. Published reports on the application of the SPC method to smallholder farm data are very limited. Thus, the purpose of this study was to assess the capability of the SPC method for monitoring the variation of BMSCC levels in milk samples collected from smallholder dairy farms. MATERIALS AND METHODS: Bulk tank milk samples (n=1302) from 31 farms were collected 3 times/month for 14 consecutive months. The samples were analyzed to determine the BMSCC levels. The SPC charts, including the individual chart (I-chart) and the moving range chart (MR-chart), were created to determine the BMSCC variations, out of control points, and process signals for each farm every month. The interpretation of the SPC charts was reported to dairy cooperative authorities and veterinarians. RESULTS: Based on a set of BMSCC values as well as their variance from SPC charts, a series of BMSCC data could be classified into different scenarios, including farms with high BMSCC values but with low variations or farms with low BMSCC values and variations. Out of control points and signals or alarms corresponding to the SPC rules, such as trend and shift signals, were observed in some of the selected farms. The information from SPC charts was used by authorities and veterinarians to communicate with dairy farmers to monitor and control BMSCC for each farm. CONCLUSION: This study showed that the SPC method can be used to monitor the variation of BMSCC in milk sampled from smallholder farms. Moreover, information obtained from the SPC charts can serve as a guideline for dairy farmers, dairy cooperative boards, and veterinarians to manage somatic cell counts in bulk tanks from smallholder dairy farms.

Estimating the number of farms experienced foot and mouth disease outbreaks using capture-recapture methods.

Foot and mouth disease (FMD) is recognized as an endemic disease in Thailand and throughout other countries in Southeast Asia. The underreporting of FMD outbreaks has affected the true status of the disease. This study aimed to determine the number of dairy farms in Chiang Mai that had experienced FMD outbreaks (FMD outbreak farm) during 2015-2016 using capture-recapture (CR) methods. Two independent FMD outbreak data sources including data from the livestock authorities and survey questionnaires were analyzed using Chapman estimator and Chao estimator. Results showed that the estimated number of FMD outbreak farms was 264 (95% CI = 250, 277) and 273 (95% CI = 259, 292) farms based on the Chapman estimator and Chao estimator, respectively. The estimated prevalence of FMD corresponding to the Chapman estimator was lower than the Chao estimator. The active approach of the survey method offered a higher degree of sensitivity compared to the passive method used by the livestock authorities. Estimations from the CR method provided an upper bound for the true number of outbreak farms. This study demonstrated the use of the CR method to estimate the true status of FMD outbreaks. Our proposed approach can potentially be used as a tool to enhance the accuracy and sensitivity of established monitoring and surveillance systems.

Cattle Manure Trade Network Analysis and the Relevant Spatial Pathways in an Endemic Area of Foot and Mouth Disease in Northern Thailand.

Animal movement is one of the most important risk factors for outbreaks of foot and mouth disease (FMD) in cattle. Likewise, FMD can spread to cattle farms via vehicles contaminated with the FMD virus. In Northern Thailand, the movement of manure transport vehicles and the circulation of manure bags among cattle farms are considered as potential risk factors for FMD outbreaks among cattle farms. This study aimed to determine the characteristics and movement patterns of manure tradesman using social network analysis. A structured questionnaire was used to identify sequences of farms routinely visited by each tradesman. A total of 611 participants were interviewed, including 154 beef farmers, 407 dairy farmers, 36 tradesmen, and 14 final purchasers. A static weighted directed one-mode network was constructed, and the network metrics were measured. For the manure tradesman-cattle farmer network, the tradesman possessed the highest value of in- and out-degree centralities (71 and 4), betweenness centralities (114.5), and k-core values (2). These results indicated that the tradesman had a high frequency of farm visits and had a remarkable influence on other persons (nodes) in the network. The movement of vehicles ranged from within local districts, among districts, or even across provinces. Unclean manure plastic bags were circulated among cattle farms. Therefore, both vehicles and the bags may act as a disease fomite. Interestingly, no recording system was implemented for the movement of manure transport vehicles. This study suggested that the relevant authority and stakeholders should be aware of the risk of FMD spreading within this manure trading network. The findings from this study can be used as supporting data that can be used for enhancing FMD control measures, especially for FMD endemic areas.

Epidemiology and National Surveillance System for Foot and Mouth Disease in Cattle in Thailand during 2008-2019.

Foot and mouth disease (FMD) is a prominent transboundary disease that threatens livestock production and can disrupt the trade in animals and animal products at both regional and international levels. The aims of this study were: (1) to analyze the distribution of FMD in Thailand during the period of 2008 to 2019, (2) to outline a national surveillance approach, and (3) to identify the existing knowledge gap that is associated with this disease in relation to cattle production. We analyzed FMD outbreak data in order to determine the existing spatial and temporal trends and reviewed relevant publications and official documents that helped us outline a national surveillance program. There were 1209 FMD outbreaks in cattle farms during the study period. FMD outbreaks occurred every year throughout the study period in several regions. Notably, FMD serotype O and A were considered the predominant types. The FMD National Strategic Plan (2008-2015) and the national FMD control program (2016-2023) have been implemented in order to control this disease. The surveillance approach employed by livestock authorities included both active and passive surveillance techniques. The vaccination program was applied to herds of cattle 2-3 times per year. Additionally, numerous control measures have been implemented across the country. We have identified the need for a study on the assessment of an applicable surveillance program, the evaluation of an appropriate vaccination strategy and an assessment of the effectiveness of a measured control policy. In conclusion, this study provided much needed knowledge on the epidemiology of FMD outbreaks across Thailand from 2008 to 2019. Additionally, we identified the need for future studies to address the existing knowledge gaps. The findings from this study may also be useful for livestock authorities and stakeholders to establish an enhanced control strategy and to implement an effective surveillance system that would control and eradicate FMD throughout the country.

Spatiotemporal analyses of foot and mouth disease outbreaks in cattle farms in Chiang Mai and Lamphun, Thailand.

BACKGROUND: Foot and mouth disease (FMD) is a highly infectious and contagious febrile vesicular disease of cloven-hoofed livestock with high socio-economic consequences globally. In Thailand, FMD is endemic with 183 and 262 outbreaks occurring in the years 2015 and 2016, respectively. In this study, we aimed to assess the spatiotemporal distribution of FMD outbreaks among cattle in Chiang Mai and Lamphun provinces in the northern part of Thailand during the period of 2015-2016. A retrospective space-time scan statistic including a space-time permutation (STP) and the Poisson and Bernoulli models were applied in order to detect areas of high incidence of FMD. RESULTS: Results have shown that 9 and 8 clusters were identified by the STP model in 2015 and 2016, respectively, whereas 1 and 3 clusters were identified by the Poisson model, and 3 and 4 clusters were detected when the Bernoulli model was applied for the same time period. In 2015, the most likely clusters were observed in Chiang Mai and these had a minimum radius of 1.49 km and a maximum radius of 20 km. Outbreaks were clustered in the period between the months of May and October of 2015. The most likely clusters in 2016 were observed in central Lamphun based on the STP model and in the eastern area of Chiang Mai by the Poisson and Bernoulli models. The cluster size of the STP model (8.51 km) was smaller than those of the Poisson and Bernoulli models (> 20 km). The cluster periods in 2016 were approximately 7 months, while 4 months and 1 month were identified by the Poisson, Bernoulli and STP models respectively. CONCLUSIONS: The application of three models provided more information for FMD outbreak epidemiology. The findings from this study suggest the use of three different space-time scan models for the investigation process of outbreaks along with the follow-up process to identify FMD outbreak clusters. Therefore, active prevention and control strategies should be implemented in the areas that are most susceptible to FMD outbreaks.

Determination of Risk Factors Associated with Foot and Mouth Disease Outbreaks in Dairy Farms in Chiang Mai Province, Northern Thailand.

Foot and mouth disease (FMD) is considered a highly contagious transboundary disease of cloven-hoofed animals. FMD has become endemic to northern Thailand over the past decade. In 2016, FMD outbreaks were recorded in three districts in Chiang Mai Province. The objective of this study was to determine the farm-level risk factors associated with FMD outbreaks. This study was conducted via a face-to-face interview questionnaire survey at 140 FMD outbreak farms and 307 control farms. Univariable and multivariable logistic regression analyses were used to determine the association between potential risk factors and FMD outbreaks. The final logistic regression model identified factors associated with FMD outbreaks including the purchasing of a new cow without following quarantine protocol (odds ratio = 2.41, 95%CI = 1.45, 4.05), farms located near shared cattle grazing areas in a 10 km radius (OR = 1.83, 95%CI =1.11, 3.02), FMD vaccination administration by non-official livestock personnel (OR = 2.52, 95%CI = 1.39, 4.58), farms located in a 5 km radius of cattle abattoirs (OR = 1.83, 95%CI = 0.99, 3.40) and no history of FMD outbreaks over the previous 12 months in districts where farms were located (OR = 0.44, 95%CI = 0.22, 0.86). The risk factors identified in this study were related to farm biosecurity, FMD vaccination administration and distance from the farms to risk areas. Therefore, it was important to strengthen on-farm biosecurity and to improve farm management practices in order to reduce incidences of FMD at the farm level. Education or training programs for dairy farmers that would enhance knowledge and practices in relation to the assessed topics are needed.