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.

Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data.

Introduction: Rabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attributed to animal-transmitted rabies. For strategic allocation and optimization of resources, sophisticated and accurate forecasting of rabies incidents is imperative. This research aims to determine temporal patterns, and seasonal fluctuations, and project the incidence of canine rabies throughout Thailand, using various time series techniques. Methods: Monthly total laboratory-confirmed rabies cases data from January 2013 to December 2022 (full dataset) were split into the training dataset (January 2013 to December 2021) and the test dataset (January to December 2022). Time series models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), Error Trend Seasonality (ETS), the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and Seasonal and Trend Decomposition using Loess (STL) were used to analyze the training dataset and the full dataset. The forecast values obtained from the time series models applied to the training dataset were compared with the actual values from the test dataset to determine their predictive performance. Furthermore, the forecast projections from January 2023 to December 2025 were generated from models applied to the full dataset. Results: The findings revealed a total of 4,678 confirmed canine rabies cases during the study duration, with apparent seasonality in the data. Among the models tested with the test dataset, TBATS exhibited superior predictive accuracy, closely trailed by the SARIMA model. Based on the full dataset, TBATS projections suggest an annual average of approximately 285 canine rabies cases for the years 2023 to 2025, translating to a monthly average of 23 cases (range: 18-30). In contrast, SARIMA projections averaged 277 cases annually (range: 208-214). Discussion: This research offers a new perspective on disease forecasting through advanced time series methodologies. The results should be taken into consideration when planning and conducting rabies surveillance, prevention, and control activities.

Estimating the Transmission Kernel for Lumpy Skin Disease Virus from Data on Outbreaks in Thailand in 2021.

Nationwide outbreaks of lumpy skin disease (LSD) were observed in Thailand in 2021. A better understanding of its disease transmission is crucial. This study utilized a kernel-based approach to characterize the transmission of LSD between cattle herds. Outbreak data from the Khon Kaen and Lamphun provinces in Thailand were used to estimate transmission kernels for each province. The results showed that the majority of herd-to-herd transmission occurs over short distances. For Khon Kaen, the median transmission distance from the donor herd was estimated to be between 0.3 and 0.8 km, while for Lamphun, it ranged from 0.2 to 0.6 km. The results imply the critical role that insects may play as vectors in the transmission of LSD within the two study areas. This is the first study to estimate transmission kernels from data on LSD outbreaks in Thailand. The findings from this study offer valuable insights into the spatial transmission of this disease, which will be useful in developing prevention and control strategies.

Long-term monitoring of immune response to recombinant lumpy skin disease virus in dairy cattle from small-household farms in western Thailand.

Lumpy skin disease (LSD) was firstly reported in Thailand in 2021 which affected the cattle industry. However, there is limited information on the immune response of LSDV infection in Thailand where recombinant vaccine strain circulated. The aim of this research was to study the duration of LSD immune response of subclinical and clinical animals after natural infection in dairy cattle. Sixty-six dairy cattle from ten farms in central and western regions of Thailand were investigated. Antibody was detected by virus neutralization test and ELISA. Cell mediated immunity (CMI)-related cytokine gene expressions were evaluated. Antibody was detected until at least 15 months after the noticeable symptom. Cattle with subclinical disease had lower antibody levels compared to animals which had clinical disease. IFN-γ and TNF-α levels were increased, while IL-10 level was decreased in the infected animals compared to the controls. This study elucidated immune responses in dairy cattle herd affected by recombinant LSDV.

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.

Modelling epidemic growth models for lumpy skin disease cases in Thailand using nationwide outbreak data, 2021-2022.

Lumpy skin disease (LSD) is a transboundary disease affecting cattle and has a detrimental effect on the cattle industries in numerous countries in Africa, Europe and Asia. In 2021, LSD outbreaks have been reported in almost all of Thailand's provinces. Indeed, fitting LSD occurrences using mathematical models provide important knowledge in the realm of animal disease modeling. Thus, the objective of this study is to fit the pattern of daily new LSD cases and daily cumulative LSD cases in Thailand using mathematical models. The first- and second-order models in the forms of Lorentzian, Gaussian and Pearson-type VII models are used to fit daily new LSD cases whereas Richard's growth, Boltzmann sigmoidal and Power-law growth models are utilized to fit the curve of cumulative LSD cases. Based on the root-mean-squared error (RMSE) and Akaike information criterion (AIC), results showed that both first and second orders of Pearson-type VII models and Richard's growth model (RGM) were fit to the data better than other models used in the present study. The obtained models and their parameters can be utilized to describe the LSD outbreak in Thailand. For disease preparedness purposes, we can use the first order of the Pearson-type VII model to estimate the time of maximum infected cases occurring when the growth rate of infected cases starts to slow down. Furthermore, the period when the growth rate changes at a slower rate, known as the inflection time, obtained from RGM allows us to anticipate when the pandemic has peaked and the situation has stabilized. This is the first study that utilizes mathematical methods to fit the LSD epidemics in Thailand. This study offers decision-makers and authorities with valuable information for establishing an effective disease control strategy.

The impact of mass vaccination policy and control measures on lumpy skin disease cases in Thailand: insights from a Bayesian structural time series analysis.

Introduction: In 2021, Thailand reported the highest incidence of lumpy skin disease (LSD) outbreaks in Asia. In response to the widespread outbreaks in cattle herds, the government's livestock authorities initiated comprehensive intervention measures, encompassing control strategies and a national vaccination program. Yet, the efficacy of these interventions remained unevaluated. This research sought to assess the nationwide intervention's impact on the incidence of new LSD cases through causal impact analysis. Methods: Data on weekly new LSD cases in Thailand from March to September 2021 was analyzed. The Bayesian structural time series (BSTS) analysis was employed to evaluate the causal relationship between new LSD cases in the pre-intervention phase (prior to the vaccination campaign) and the post-intervention phase (following the vaccination campaign). The assessment involved two distinct scenarios, each determined by the estimated effective intervention dates. In both scenarios, a consistent decline in new LSD cases was observed after the mass vaccination initiative, while other control measures such as the restriction of animal movement, insect control, and the enhancement of the active surveillance approach remained operational throughout the pre-intervention and the post-intervention phases. Results and discussion: According to the relative effect results obtained from scenario A and B, it was observed that the incidence of LSD cases exhibited reductions of 119% (95% Credible interval [CrI]: -121%, -38%) and 78% (95% CrI: -126, -41%), respectively. The BSTS results underscored the significant influence of these interventions, with a Bayesian one-sided tail-area probability of p < 0.05. This model-based study provides insight into the application of BSTS in evaluating the impact of nationwide LSD vaccination based on the national-level data. The present study is groundbreaking in two respects: it is the first study to quantify the causal effects of a mass vaccination intervention on the LSD outbreak in Thailand, and it stands as the only endeavor of its kind in the Asian context. The insights collected from this study hold potential value for policymakers in Thailand and other countries at risk of LSD outbreaks.

Rapid Spread and Genetic Characterisation of a Recently Emerged Recombinant Lumpy Skin Disease Virus in Thailand.

The emergence of the lumpy skin disease virus (LSDV) was first detected in north-eastern Thailand in March 2021. Since then, the abrupt increase of LSD cases was observed throughout the country as outbreaks have spread rapidly to 64 out of a total of 77 provinces within four months. Blood, milk, and nodular skin samples collected from affected animals have been diagnosed by real-time PCR targeting the p32 gene. LSDV was isolated by primary lamb testis (PLT) cells, followed by Madin-Darby bovine kidney (MDBK) cells, and confirmed by immunoperoxidase monolayer assay (IPMA). Histopathology and immunohistochemistry (IHC) of a skin lesion showed inclusion bodies in keratinocytes and skin epithelial cells. Phylogenetic analyses of RPO30 and GPCR genes, and the whole genome revealed that Thai viruses were closely related to the vaccine-derived recombinant LSDV strains found previously in China and Vietnam. Recombination analysis confirmed that the Thai LSDV possesses a mosaic hybrid genome containing the vaccine virus DNA as the backbone and a field strain DNA as the minor donor. This is an inclusive report on the disease distributions, complete diagnoses, and genetic characterisation of LSDV during the first wave of LSD outbreaks in Thailand.

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.

Subdistrict-Level Reproductive Number for Foot and Mouth Disease in Cattle in Northern Thailand.

Foot and mouth disease (FMD) is an important contagious transboundary disease that causes a significant economic loss for several countries. The FMD virus (FMDV) can spread very rapidly by direct and indirect transmission among susceptible animals. The complexity and magnitude of FMDV transmission at the initial stages of the epidemic can be expressed by the basic reproductive number (R 0), and furthermore, control strategies can be assessed by the estimation of the effective reproductive number. In this study, we aimed to describe FMD outbreaks among smallholder cattle farms by subdistricts in the northern Thailand and compute the effective reproductive number for outbreaks caused by FMDV serotype O and overall serotypes, including serotype O, serotype A, and unidentified serotype, at the subdistrict level (R sd ) using an epidemic doubling time method. Field data of FMD outbreaks during 2015-2017 that affected 94 subdistricts in northern Thailand were assessed to estimate the R sd . Results showed that 63.38% (90/142) of the FMD outbreak episodes in cattle were caused by FMDV serotype O. The average doubling time and the R sd estimated of the outbreaks caused by FMDV serotype O and overall serotype were 2.80 and 4.67 months, and 1.06 and 1.04, respectively. Our results indicated that transmission of FMD in cattle at the subdistrict level in northern Thailand was not controlled (R sd > 1), which indicates the endemicity of the disease in the region. Although control measures are in place, the results from this study highlighted the need for enhancing FMD monitoring and control strategies in northern Thailand.

Clinical characteristics of elephant endotheliotropic herpesvirus (EEHV) cases in Asian elephants (Elephas maximus) in Thailand during 2006-2019.

BACKGROUND: Elephant endotheliotropic herpesvirus causes a hemorrhagic disease (EEHV-HD) that is a major cause of death in juvenile Asian elephants with EEHV1 and EEHV4 being the most prevalent. AIM: To perform a retrospective clinical data analysis. METHODS: Records of a total of 103 cases in Thailand confirmed by polymerase chain reaction (PCR) on blood and/or tissue samples. RESULTS: The severity of clinical signs varied among EEHV subtypes. EEHV1A was the most prevalent with 58%, followed by EEHV4 with 34%, EEHV1B with 5.8% and EEHV1&4 co-infection with 1.9%. Overall case fatality rate was 66%. When compared among subtypes, 100% case fatality rate was associated with EEHV1&4 co-infection, 83% with EEHV1B, 75% with EEHV1A, and the lowest at 40% for EEHV4. Calves 2- to 4-year old were in the highest age risk group and exhibited more severe clinical signs with the highest mortality. Majority of cases were found in weaned or trained claves and higher number of cases were observed in rainy season. A gender predilection could not be demonstrated. Severely affected elephants presented with thrombocytopenia, depletion of monocytes, lymphocytes and heterophils, a monocyte:heterophil (M:H) ratio lower than 2.37, hypoproteinemia (both albumin and globulin), severe grade of heterophil toxicity, and low red blood cell counts and pack cell volumes. Survival was not affected by antiviral drug treatment in the severely compromised animals. CONCLUSION: Early detection by laboratory testing and aggressive application of therapies comprising of supportive and anti-viral treatment can improve survival outcomes of this disease.

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.

Geographic Spatial Distribution Patterns of Dirofilaria immitis and Brugia pahangi Infection in Community Dogs in Chiang Mai, Thailand.

Filariasis is emerging as a public health concern in tropical and subtropical areas. Filariasis is an endemic problem commonly found in southeast Asian countries. Using the PCR-restriction fragment length polymorphism (PCR-RFLP) of the ITS1 region with Vsp I, the overall prevalence rates of Dirofilaria immitis (12.2% (41/337); 95% confidence interval: 9.1-16.1%) and Brugia pahangi (8.3% (28/337); 95% confidence interval: 5.8-11.8%) were determined based on 337 free-roaming community dogs from 20 districts in Northern Thailand. Microfilaremia was found in only 6.2% of dogs (21/337). Co-infection with D. immitis and B. pahangi was observed in two dogs. Of the 215 blood samples examined using a Canine Heartworm Ag Kit, only 3.72% (eight dogs) were D. immitis antigen positive. Among these eight, six dogs had occult D. immitis infections. In terms of geographic distribution, we found the abundance of D. immitis and B. pahangi in the central areas at altitudes less than 400 m to be 12.1% and 10.3%, respectively. In contrast, at higher altitudes between 400 and 800 m, a significantly higher number of B. pahangi compared with D. immitis infected individuals were observed at 14.29% and 4.1%, respectively. In conclusion, D. immitis and B. pahangi were the most common filarial infections found in community dogs in Northern Thailand. Dogs might be an important reservoir of B. pahangi in that region. Increasing awareness and concern and including proper deworming programs for community dogs should be endorsed to reduce the transmission risk. Additionally, the population dynamics of the mosquito vector of B. pahangi across altitudinal gradients deserved further investigation.

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.