Cadaveric Study on Comparison of Neck Extension Angles for Endotracheal Intubation in Rabbits Using a Rigid and Flexible Endoscope.

Endotracheal intubation in rabbits is always challenging due to the unique anatomical conformation. To improve endotracheal intubation success, this study determined the relationship between head placement angles guided by endoscope-assisted visualization techniques and the endotracheal intubation success rate. Thirty-two rabbit cadavers were used in the study. Six veterinary practitioners who had no experience with rabbit endotracheal intubation were randomly assigned to intubate rabbit cadavers using the guidance of either a rigid endoscope (RE) or flexible endoscope (FE), with the head placement angles with an ascending neck at 90, 100, 110, 120 and 130 degrees. The endotracheal intubation completed in 90 s was determined to be a success. The success rates using RE and FE were 97.2% and 95.9%, respectively. The means and standard error of means (SEM) of endotracheal intubation times guided by RE and FE were 53.7 ± 4.68 and 55.2 ± 4.24 s, respectively. Results from survival time analysis show that the five veterinarians successfully intubated the rabbit within 90 s, regardless of the different types of endoscopes. Angle was the only significant factor that affected the endotracheal intubation success. The head placement angle at 110 and 120 degrees had the highest success rate of endotracheal intubation compared to 90 degrees (p ≤ 0.05). In conclusion, for inexperienced veterinarians, the success of endotracheal intubation in rabbits, guided by endoscope-assisted visualized techniques regardless of rigid endoscope or flexible endoscope guidance, is improved when the head extension is 110 and 120 degrees.

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.

Physiological changes in captive elephants in northern Thailand as a result of the COVID-19 tourism ban-stress biomarkers.

The international travel ban instituted by the Thai government in March 2020 in response to the COVID-19 pandemic greatly affected how tourist camp elephants were managed, with reductions in exercise opportunities, longer chaining hours, and diminished food provisioning. This study was conducted to determine how those changes affected health and welfare biomarkers in individual elephants over the 2 years of the countrywide lockdown (April 2020-April 2022). Blood and fecal samples were collected from 58 elephants at six camps (monthly in Year 1, quarterly in Year 2) and analyzed for stress biomarkers - fecal glucocorticoid metabolites (fGCM), serum oxidative stress [malondialdehyde (MDA) and 8-hydroxy-2'-deoxyguanosine (8-OHdG)], and stress leukograms. Overall, fGCM concentrations increased within the first few months and remained higher than pre-COVID levels, as did the H/L ratio, a measure affected by cortisol. Serum 8-OHdG, an indicator of DNA oxidative damage, also increased over time, while monocytosis and lymphopenia further suggested alterations in immune function as a result of stress. By contrast, another marker of oxidative stress, serum MDA, declined, possibly in response to reduced roughage and supplement intake. A notable finding was a seasonal pattern of fGCM that was significantly different from previous studies. Whereas higher fGCM during the rainy season were observed in this study, previously, concentrations were highest during the winter, high tourist season. Thus, ironically, both the presence and absence of tourists have been associated with increased fGCM concentrations, albeit for different reasons. Camp management factors negatively affecting stress outcomes included shorter chain lengths, longer chain hours, lack of exercise, and reduced roughage and supplements. Overall, it was clear that camps struggled to maintain adequate care for elephants during the COVID-19 pandemic, highlighting the importance of tourist income and need for contingency plans to cope with potential future disruptions to tourism.

Cross-correlation and time series analysis of rabies in different animal species in Nepal from 2005 to 2018.

Rabies is a fatal zoonotic disease, resulting in human and livestock deaths. In Nepal, animal rabies has posed a significant challenge to public health. Because animals are the primary source of rabies in humans, a better understanding of rabies epidemiology in animals is necessary. The objectives of this study were to determine the correlation between rabies occurrences in dogs and livestock animals and to detect the trends and change points of the disease using longitudinal data. The nationwide rabies dataset from 2005 to 2018 was analyzed using cross-correlation, multiple change points, and time series methods. Autoregressive Integrated Moving Average (ARIMA) and Neural Network Autoregression (NNAR) were applied to the time series data. The results show a positive correlation between canine rabies and livestock rabies occurrences. Three significant change points were detected in the time series data, demonstrating that the occurrences were high in the initial years but stabilized before peaking to an upward trend in the final years of the study period. Nonetheless, there was no seasonality pattern in rabies occurrences. The most suitable models were ARIMA (2,1,2) and NNAR (5,1,4) (12). Based on the study findings, both locals and tourists in Nepal need to have enhanced awareness of the potential dangers posed by rabies in canines and livestock. This study offers much-needed insight into the patterns and epidemiology of animal rabies which will be helpful for policymakers in drafting rabies control plans for Nepal.

Association between Opisthorchis viverrini infection in cats and humans: Non-spatial and spatial analyses.

Cats are recognized as significant reservoir hosts for human opisthorchiasis, particularly in areas with a high prevalence of infection. Despite this, the precise role of cats in the transmission of Opisthorchis viverrini between humans and felines remains unclear. This study investigates the association between these two hosts through both spatial and non-spatial analyses in the endemic Thanya sub-district of Thailand. A total of 105 owned cats were randomly sampled from 15 villages within the sub-district for stool examination. A questionnaire was administered to 66 cat owners to explore the human-pet relationship. Household locations were collected using GPS devices. Non-spatial analyses revealed a positive association between the two hosts (P= 0.011; OR 7, 95% CI: 1.6-30.9), highlighting two independent significant risk factors: cat owners consuming raw fish (P = 0.028; OR = 4.52, 95% CI: 1.25-19.45) and feeding cats raw fish (P = 0.011; OR = 16.41, 95% CI: 2.78-317.04) according to multivariate analysis. Spatial analysis provided further support to the non-spatial findings (p = 0.0123; OR = 3.45, 95% CI = 0.88-13.61). Multiple autologistic regression confirmed two significant risk factors: cat owners consuming raw fish (p = 0.054; OR = 3.37, 95% CI: 0.98-11.59) and feeding cats raw fish (p = 0.014; OR = 7.43, 95% CI: 1.49-37.05). Risk mapping identified the western part of the study site as a hotspot for O. viverrini infection. Hyper-endemic focusing revealed a union of human and cat buffers at 0.46 km², with an overlapping area of 0.22 km² (47.83%). This study underscores the impact of owners' behaviors, specifically consuming and feeding raw fish to cats, on the increased probability of infection in cats. It emphasizes the need for effective opisthorchiasis control through health education targeting cat owners in endemic areas.

Application of machine learning algorithms to predict dead on arrival of broiler chickens raised without antibiotic program.

Understanding the factors of dead-on-arrival (DOA) incidents during pre-slaughter handling is crucial for informed decision-making, improving broiler welfare, and optimizing farm profitability. In this study, 3 different machine learning (ML) algorithms - least absolute shrinkage and selection operator (LASSO), classification tree (CT), and random forest (RF) - were used together with 4 sampling techniques to optimize imbalanced data. The dataset comes from 22,115 broiler truckloads from a large producer in Thailand (2021-2022) and includes 14 independent variables covering the rearing, catching, and transportation stages. The study focuses on DOA% in the range of 0.10 to 1.20%, with a threshold for high DOA% above 0.3%, and records DOA% per truckload during pre-slaughter ante-mortem inspection. With a high DOA rate of 25.2%, the imbalanced dataset prompts the implementation of 4 methods to tune the imbalance parameters: random over sampling (ROS), random under sampling (RUS), both sampling (BOTH), and synthetic sampling or random over sampling example (ROSE). The aim is to improve the performance of the prediction model in classifying and predicting high DOA%. The comparative analysis of the different error metrics shows that RF outperforms the other models in a balanced dataset. In particular, RUS shows a significant improvement in prediction performance across all models compared to the original unbalanced dataset. The identification of the 4 most important variables for predicting high DOA percentages - mortality and culling rate, rearing stocking density, season, and mean body weight - emphasizes their importance for broiler production. This study provides valuable insights into the prediction of DOA status using an ML approach and contributes to the development of more effective strategies to mitigate high DOA percentages in commercial broiler production.

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.

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.

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.

Seasonal patterns of oxidative stress markers in captive Asian elephants in Thailand and relationships to elephant endotheliotropic herpesvirus shedding.

Introduction: Oxidative stress refers to an imbalance between oxidant and antioxidant activity and accumulation of reactive oxygen species, which can have detrimental effects on animal health. Annual fluctuations in oxidative stress status can occur, increasing disease susceptibility during certain time periods. However, a full understanding of factors related to oxidative stress in Asian elephants and how to mitigate the negative consequences is lacking. Methods: This study measured six serum oxidative stress markers [reactive oxygen species (ROS), malondialdehyde (MDA), 8-hydroxydeoxyguanosine (8-OHdG), albumin, glutathione peroxidase (GPx), and catalase] and two stress markers [serum cortisol and fecal glucocorticoid metabolites (fGCM)] in 23 captive Asian elephants in Thailand over a 12 months period to examine relationships with age and season. Results: Seasonal variations were observed, with several markers exhibiting significantly higher concentrations in the summer (ROS, MDA, 8-OHdG, albumin) and lower values during the rainy/winter seasons (MDA, 8-OHdG, albumin, catalase). By contrast, GPx was the only marker to be highest during the rainy season. For the stress markers, higher fGCM concentrations were noted during the rainy season, which contrasts with earlier studies showing more activity in the winter (tourist season). Positive correlations were found between the temperature-humidity index and ROS, GPx, and fGCM, while a negative correlation was observed with serum albumin. Elephant endotheliotropic herpesvirus (EEHV) shedding events were associated with higher concentrations of ROS and MDA. A moderate negative correlation was observed between 8-OHdG and the PCR threshold cycle of EEHV shedding (Ct), indicating DNA damage may be involved in EEHV shedding in elephants. Discussion: Results revealed significant age and seasonal effects on several oxidative stress markers, indicating those factors should be considered in study design and data interpretation. There also may be physiological adaptations in oxidative stress conditions in relation to environmental changes that could impact health outcomes.

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.

An Overview of Pulses Production in India: Retrospect and Prospects of the Future Food with an Application of Hybrid Models.

Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of protein needed for human health. India's pulses production guidelines must be based on accurate and best forecast models. Comparing classical statistical and machine learning models based on different scientific data series is the subject of high-level research today. This study focused on the forecasting behaviour of pulses production for India, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh. The data series was split into a training dataset (1950-2014) and a testing dataset (2015-2019) for model building and validation purposes, respectively. ARIMA, NNAR and hybrid models were used and compared on training and validation datasets based on goodness of fit (RMSE, MAE and MASE). This research demonstrates that due to the diverse agricultural conditions across different provinces in India, there is no single model that can accurately predict pulse production in all regions. This study's highest accuracy model is ARIMA. ARIMA outperforms NNAR, a machine learning model. Pulse production in India, Rajasthan, and Madhya Pradesh will expand by 26.11%, 12.62%, and 0.51% from 2020 to 2030, whereas it would decline by - 6.5%, - 6.21%, and - 6.76 per cent in Karnataka, Maharashtra, and Uttar Pradesh, respectively. The current forecast results could allow policymakers to develop more aggressive food security and sustainability plans and better Indian pulses production policies in the future.

Effect of Electroejaculation Protocols on Semen Quality and Concentrations of Testosterone, Cortisol, Malondialdehyde, and Creatine Kinase in Captive Bengal Tigers.

The Bengal tiger (Panthera tigris tigris) is critically endangered, so assisted reproductive technologies, including artificial insemination, are important conservation tools. For wild and domestic felids, electroejaculation (EE) is the most common semen collection method, with protocols optimized to obtain sufficient amounts of viable sperm for artificial insemination. However, less attention has been paid to ensuring animal wellbeing during the process. This study examined the effects of three EE protocols (Low, 2-5 volts; Medium, 3-6 volts; High, 4-7 volts) on semen quality, testicular size, serum testosterone, creatine kinase (CK), and malondialdehyde (MDA) concentrations, and serum cortisol as a proxy for stress. Blood samples were collected before, during, and after each EE series. Seminal plasma pH, and sperm motility, viability, and morphology were evaluated after each procedure. Seminal plasma and sperm pellet MDA concentrations were also determined. Primary sperm abnormalities and seminal plasma MDA were higher in the Low compared to Medium and High voltage groups (p < 0.05). Serum CK in the High voltage group increased during the EE series (p < 0.05), suggesting the potential for muscle damage. However, no significant changes were observed for serum cortisol, testosterone, or MDA concentrations. Results suggest the Medium voltage protocol produced good quality samples at lower voltages than the High protocol with no negative effect on muscle function, which might be better for animal welfare.

Preslaughter handling factors affecting dead on arrival, condemnations, and bruising in broiler chickens raised without an antibiotic program.

In Thailand, knowledge about the factors affecting broiler losses during the preslaughter process is very limited, especially for broilers raised without an antibiotic program. The objective of this study was to determine the preslaughter factors that influence the incidence of dead on arrival (DOA), condemnations, and bruising in broilers raised without antibiotics. Data from 13,581 truckloads of broilers raised without an antibiotic program in 95 contract farms of one of Thailand's largest broiler producers in 2021 were analyzed using a generalized linear mixed model that accounted for farm as a random effect. Results showed that the following risk factors were associated with the occurrence of DOA, condemnations, and bruising: season, time of transport, sex, age at slaughter, mortality and culling rate, and weight per crate. While mean body weight affected the incidence of condemnations and bruising, transport time and lairage time affected DOA and bruising. Feed withdrawal time affected DOA and condemnations. Rearing stocking density only affected condemnation rate. Reducing or eliminating the effects of these risk factors could reduce production losses due to DOA, condemnations, and bruising, thereby improving animal welfare and producer profitability. Reducing weight per crate could reduce DOA, condemnations, and bruising. Reducing lairage time could reduce DOA and bruising, while reducing feed withdrawal time could reduce DOA and condemnations. Raising broilers at a younger age with a lower slaughter weight could prevent the occurrence of DOA, condemnations, and bruising.

Measures of Oxidative Status Markers in Relation to Age, Sex, and Season in Sick and Healthy Captive Asian Elephants in Thailand.

Oxidative stress is a pathological condition that can have adverse effects on animal health, although little research has been conducted on wildlife species. In this study, blood was collected from captive Asian elephants for the assessment of five serum oxidative status markers (reactive oxygen species (ROS) concentrations; malondialdehyde, MDA; albumin; glutathione peroxidase, GPx; and catalase) in healthy (n = 137) and sick (n = 20) animals. Health problems consisted of weakness, puncture wounds, gastrointestinal distress, eye and musculoskeletal problems, and elephant endotheliotropic herpesvirus hemorrhagic disease (EEHV-HD). Fecal samples were also collected to assess glucocorticoid metabolites (fGCMs) as a measure of stress. All data were analyzed in relation to age, sex, sampling season, and their interactions using generalized linear models, and a correlation matrix was constructed. ROS and serum albumin concentrations exhibited the highest concentrations in aged elephants (>45 years). No sex differences were found for any biomarker. Interactions were observed for age groups and seasons for ROS and catalase, while GPx displayed a significant interaction between sex and season. In pairwise comparisons, significant increases in ROS and catalase were observed in summer, with higher ROS concentrations observed only in the adult female group. Lower catalase activity was exhibited in juvenile males, subadult males, adult females, and aged females compared to subadult and adult elephants (males and females) in winter and the rainy season. There was a positive association between catalase activity and fGCMs (r = 0.23, p < 0.05), and a number of red blood cell parameters were positively associated with several of these biomarkers, suggesting high oxidative and antioxidative activity covary in red cells (p < 0.05). According to health status, elephants with EEHV-HD showed the most significant changes in oxidative stress markers, with MDA, GPx, and catalase being higher and albumin being lower than in healthy elephants. This study provides an analysis of understudied health biomarkers in Asian elephants, which can be used as additional tools for assessing the health condition of this species and suggests age and season may be important factors in data interpretation.

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.

Development of in-house ELISA for detection of antibodies against lumpy skin disease virus in cattle and assessment of its performance using a bayesian approach.

Lumpy skin disease (LSD) is a contagious disease among cattle and buffalo worldwide. Currently, an enzyme-linked immunosorbent assay (ELISA) has been recognized as an efficient diagnostic tool that is less time-consuming and easier than the viral neutralization test to measure the antibody levels. In the present study, an in-house method of indirect ELISA was developed to detect the bovine antibodies against Lumpy skin disease virus (LSDV) and its performance was assessed using field samples. This in-house method has been compared with the commercial ELISA test kit for detection of bovine antibodies against LSDV. The sensitivity (Se) and the specificity (Sp) of the test were estimated using a Bayesian latent class model. Checkerboard titration was performed using the naturally LSDV-infected bovine sera and colostrum-deprived calf sera. The LSDV antigen concentrations (1 TCID50/mL), the sample serum (1:500), and goat anti-bovine immunoglobulin G (IgG) labeled with horseradish peroxidase (HRP) (1:10,000) were determined to be optimal for this assay. The calculated cut-off value was 0.067, and there were no differences in the results of tests that utilized positive and negative sera (p < 0.05). The characteristics of two diagnostic tests were evaluated using a conditional dependent and one-population Bayesian model. The Se value of an in-house indirect ELISA were almost similar to ELISA test kit. On the other hand, the Sp value of the in-house ELISA test was lower than that of the commercial ELISA test with the median values of 89% (95% PPI = 75.9-99.3%) and 91.4% (95% PPI = 85.3-95.5%), respectively. A posterior estimate for the prevalence was 66.9% (95% PPI = 60.8-83.3%) and higher than initially expected.

Physiological changes in captive elephants in Northern Thailand as a result of the COVID-19 tourism ban - muscle, liver, metabolic function, and body condition.

The international travel ban initiated in March 2020 due to the COVID-19 pandemic greatly affected how captive elephants were managed in Thailand. A lack of tourists and associated income meant elephants were chained longer with reduced food provisions, had fewer mahouts, and limited exercise like riding, which likely affected health and welfare. Fifty-eight elephants from six tourist camps were assigned a body condition score (BCS) and blood samples were collected monthly for 2 years during the travel ban to measure: (1) muscle enzymes [creatine kinase (CK), aspartate aminotransferase (AST)]; (2) liver enzymes [aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT)]; (3) lipids [total cholesterol (TC), triglycerides (TG), low (LDL) and high (HDL) density lipoproteins]; and metabolic function [glucose, insulin, fructosamine]. Serum CK concentrations were lower at the end of the study, possibly due to no tourist activities like riding. Changes in liver function included increased AST and ALP, also possibly due to physical inactivity. Feeding less bananas and sugar cane was associated with fewer elephants in the obese category and lower TG concentrations. However, increases in glucose, insulin and fructosamine were observed as local people returned to feed elephants after lifting travel restrictions. In sum, changes in several health biomarkers were observed in association with restricted activities and food resources. Camps need better plans to meet the health and welfare needs of elephants during any future disruptions to the tourism industry, although reduced feeding of sweet treats appeared to have positive effects on body condition and metabolic function.

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.