Prevalence and characterization of Staphylococcus aureus isolated in raw milk from cows in Hokkaido, Japan.

The aim of this study was to characterize the phenotypes and genotypes of Staphylococcus aureus isolated from raw bovine milk in Hokkaido, Japan. S. aureus isolates were identified in 135 of 436 milk samples from cows with and without signs of mastitis from three farms in Hokkaido. These clinical isolates were characterized for antimicrobial susceptibility patterns, molecular typing using phage-open-reading frame typing (POT), coagulase gene type, virulence genes, and biofilm-associated genes and were evaluated for biofilm-forming ability. Most isolates were susceptible to the antimicrobial agents tested. The highest rate of resistance was to ampicillin. Molecular typing of all S. aureus isolates indicated a predominance of coagulase type VI and 0-17-34 POT type, and virulence genes were highly prevalent in the isolates from all farms. Moreover, a high percentage of the 0-17-34 POT type isolates showed extensive formation of biofilm. These findings will help veterinarians and farmers to understand the epidemiology of S. aureus so that they can monitor the transmission and spread of this pathogen and control it more effectively.

Spatiotemporal patterns of rabid dogs and cats and the opinions of personnel responsible for disease control in Thailand.

The aim of this study was to evaluate the patterns of rabies cases in dogs and cats in Thailand from 2013 to 2016 via spatiotemporal analysis. We also assessed the opinions of responsible district-level personnel regarding risk factors and control measures for rabies. Evaluation of rabies case patterns was based on secondary data from a national online database, and a structured questionnaire was used to obtain the opinions of district-level personnel. A total of 1202 cases (1202/13058, 9.21 %) of rabid dogs and cats were documented between 2013 and 2016, with the majority of cases involving dogs (1165/13058; 8.92 %). The spatiotemporal analysis indicated that most of the cases were recorded in central Thailand and that there was a general trend of an increase in rabies cases from the beginning of 2013 to the end of 2016. Month-by-month analysis for each year suggested that the number of rabies cases tended to increase over the course of the year in 2013 and 2016. Results from the autocorrelation indicated that the correlation coefficient tended to be similar in adjacent time lags. In terms of the opinion analysis, only one factor (i.e., the presence of a forest that served as a habitat for carrier animals in the district) was statistically significant (P < 0.05) in the final binary logistic regression model. The results of this study may facilitate planning for effective rabies control in Thailand.

Antimicrobial Resistance in Poultry Farming: A Look Back at Environmental Escherichia coli Isolated from Poultry Farms during the Growing and Resting Periods.

During the production cycle of poultry farms, pathogens may remain in the next cycle of rearing young chickens. This study was conducted at three industrial chicken farms (A, B, and C) in central Thailand. Results showed that the percentages of E. coli during the resting period in farms A, B, and C were 28.6, 53.8, and 7.8, respectively, and those during the growing period were 45, 68.8, and 75. The most common resistant patterns during the resting period in all farms were AML-AMP-SXT and AML-AMP-DO-SXT, and those during the growing period were AML-AMP and AML-AMP-SXT. The locations of blaTEM-positive E. coli isolates from the inside houses (inside buildings) of all farms included cloacal swabs, floors, water nipples, pan feeders, and husks, whereas that from the outside environment included boots, wastewater, soil, and water from cooling pads and tanks. Our results indicate that the percentage of antimicrobial resistance (AMR) and its pattern depend on the husbandry period and the strictness of biosecurity. Moreover, our findings derived from samples gathered from broiler farms between 2013 and 2015 align with those of the current studies, highlighting persistent trends in E. coli resistance to various antimicrobial agents. Therefore, enhancing biosecurity measures throughout both the resting and growing periods is crucial, with a specific focus on managing raw materials, bedding, breeding equipment, and staff hygiene to reduce the transmission of antimicrobial resistance in poultry farms.

Spatial, Temporal, and Geographical Factors Associated with Stranded Marine Endangered Species in Thailand during 2006-2015.

The stranding of endangered marine animals is cause for concern. We used spatial and temporal analyses to investigate the stranding of endangered marine species (whales, dolphins, dugongs, and sea turtles) in Thailand, based on stranding data and geographical records during 2006-2015. A total of 1988 stranding events were obtained, including 105 whales (5.28%), 714 dolphins (35.92%), 103 dugongs (5.18%), and 1065 sea turtles (53.57%), at an average of 198.80 stranded animals/year (standard deviation = 47.19). Most strandings occurred along the Gulf of Thailand (56.94%), while the rest occurred along the Andaman Sea (43.06%). Cluster and kernel analyses showed that strandings were the most common in Phuket Province. The average number of stranded animals in the rainy season was significantly higher than that in summer and winter (p < 0.05). Our results indicate that the coastline of Thailand was significantly associated with the number of seasonal strandings (p < 0.05). However, there was no significant association between environmental factors and the number of strandings. In conclusion, surveillance systems based on spatial and temporal analyses should be established to monitor stranded animals. This will help relevant authorities to rescue stranded animals more effectively and to study the causes of stranding.

Prediction of the spread of African swine fever through pig and carcass movements in Thailand using a network analysis and diffusion model.

Background: African swine fever (ASF) is a serious contagious viral disease of pigs that affects the pig industry. This study aimed to evaluate the possible African swine fever (ASF) distribution using network analysis and a diffusion model through live pig, carcass, and pig product movement data. Material and Methods: Empirical movement data from Thailand for the year 2019 were used, and expert opinions were sought to evaluate network properties and the diffusion model. The networks were presented as live pig movement and carcass movement data at the provincial and district levels. For network analysis, a descriptive network analysis was performed using outdegree, indegree, betweenness, fragmentation, and power law distribution, and cutpoints were used to describe movement patterns. For the diffusion model, we simulated each network using spatially different infected locations, patterns, and initial infection sites. Based on expert opinions, the initial infection site, the probability of ASF occurrence, and the probability of the initial infected adopter were selected for the appropriated network. In this study, we also simulated networks under varying network parameters to predict the infection speed. Results and Conclusions: The total number of movements recorded was 2,594,364. These were divided into 403,408 (403,408/2,594,364; 15.55%) for live pigs and 2,190,956 (2,190,956/2,594,364; 84.45%) for carcasses. We found that carcass movement at the provincial level showed the highest outdegree (mean = 342.554, standard deviation (SD) = 900.528) and indegree values (mean = 342.554, SD = 665.509). In addition, the outdegree and indegree presented similar mean values and the degree distributions of both district networks followed a power-law function. The network of live pigs at provincial level showed the highest value for betweenness (mean = 0.011, SD = 0.017), and the network of live pigs at provincial level showed the highest value for fragmentation (mean = 0.027, SD = 0.005). Our simulation data indicated that the disease occurred randomly due to live pig and carcass movements along the central and western regions of Thailand, causing the rapid spread of ASF. Without control measures, it could spread to all provinces within 5- and 3-time units and in all districts within 21- and 30-time units for the network of live pigs and carcasses, respectively. This study assists the authorities to plan control and preventive measures and limit economic losses caused by ASF.

A longitudinal study of risk factors associated with white spot disease occurrence in marine shrimp farming in Rayong, Thailand.

Background: A longitudinal study was conducted to analyze farm characteristics, farm practices, and biosecurity measures that influenced the occurrence of white spot disease (WSD) in shrimp farming in Rayong, Thailand. Methods: Data were collected using a structured interview schedule administered between October 2017 and September 2019. A generalized estimating equations (GEE) model was used to identify risk factors. From the 270 responses, 86 possible risk factors were analyzed using univariate and multivariate analysis. Results: We found that 17 possible risk factors were statistically significant with an alpha level of 1% and associated with WSD status. In the final model, multivariate analysis found that two independent variables were statistically significant. First, the absence of inclement weather during ponding was a statistically significant factor associated with WSD occurrence and it prevented WSD occurrence as the odds ratio (OR) was <1 (OR = 0.196, P = 1.3 × 10-5). Second, the separation of ponds into three specific types (cultured, water treatment, and pond for water reservoir) was significantly associated with WSD occurrence. Likewise, the presence of this ponding system was a protective factor against WSD (OR = 0.0828, P = 0.001). Conclusions: The results of this study offer a reference for farmers and relevant authorities when addressing WSD occurrence in shrimp farming. In addition, our results can help relevant authorities in controlling WSD in other endemic areas.

Spatial and temporal patterns of white spot disease in Rayong Province, Thailand, from october 2015 to september 2018.

This study aimed to analyze the spatial and temporal patterns of white spot disease (WSD) in shrimp farms in Rayong Province, Thailand, between October 2015 and September 2018. The longitudinal data of all active shrimp farms were collected and categorized into two groups: cases and non-cases. A space-time permutation scan, epidemic curve, and time-series analysis were used to evaluate the spatiotemporal patterns. We assessed a total of 1126 ponds across 176 farms in two districts (Klaeng and Mueang Rayong) and identified three significant (P < 0.05) clusters of WSD cases. The biggest cluster encompassed 21 geographical coordinates. This cluster had a radius of 1.14 km and occurred between January 31, 2017, and February 28, 2017. The epidemic curve showed that the biggest outbreak peaked from December 2017 to February 2018. In the time-series analysis, the highest probability of actual WSD cases was at the beginning of each calendar year, consistent with the prominent high probability recorded in WSD forecasts. Our analysis presents the interaction between hotspot areas and time period. These results should help the relevant authorities implement appropriate surveillance programs and control measures to limit the occurrence and transmission of WSD.

Antimicrobial resistance profiles of Escherichia coli from swine farms using different antimicrobials and management systems.

BACKGROUND AND AIM: The emerging of antimicrobial-resistant foodborne bacteria is a serious public health concern worldwide. This study was conducted to determine the association between farm management systems and antimicrobial resistance profiles of Escherichia coli isolated from conventional swine farms and natural farms. E. coli isolates were evaluated for the minimum inhibitory concentration (MIC) of 17 antimicrobials, extended-spectrum beta-lactamase (ESBL)-producing enzymes, and plasmid-mediated colistin-resistant genes. MATERIALS AND METHODS: Fecal swabs were longitudinally collected from healthy pigs at three stages comprising nursery pigs, fattening pigs, and finishers, in addition to their environments. High-generation antimicrobials, including carbapenem, were selected for the MIC test. DNA samples of colistin-resistant isolates were amplified for mcr-1 and mcr-2 genes. Farm management and antimicrobial applications were evaluated using questionnaires. RESULTS: The detection rate of ESBL-producing E. coli was 17%. The highest resistance rates were observed with trimethoprim/sulfamethoxazole (53.9%) and colistin (48.5%). All isolates were susceptible to carbapenem. Two large intensive farms that used colistin-supplemented feed showed the highest colistin resistance rates of 84.6% and 58.1%. Another intensive farm that did not use colistin showed a low colistin resistance rate of 14.3%. In contrast, a small natural farm that was free from antimicrobials showed a relatively high resistance rate of 41.8%. The majority of colistin-resistant isolates had MIC values of 8 mg/mL (49%) and ≥16 mg/mL (48%). The genes mcr-1 and mcr-2 were detected at rates of 64% and 38%, respectively, among the colistin-resistant E. coli. CONCLUSION: Commensal E. coli were relatively sensitive to the antimicrobials used for treating critical human infections. Colistin use was the primary driver for the occurrence of colistin resistance in swine farms having similar conventional management systems. In the natural farm, cross-contamination could just occur through the environment if farm biosecurity is not set up carefully, thus indicating the significance of farm biosecurity risk even in an antimicrobial-free farm.

Evaluation of nosocomial infections through contact patterns in a small animal hospital using social network analysis and genotyping techniques.

Nosocomial infections or hospital-acquired infections (HAIs) are common health problems affecting patients in human and animal hospitals. Herein, we hypothesised that HAIs could be spread through human and animal movement, contact with veterinary medical supplies, equipment, or instruments. We used a combination of social network analysis and genotyping techniques to find key players (or key nodes) and spread patterns using Escherichia coli as a marker. This study was implemented in the critical care unit, outpatient department, operation room, and ward of a small animal hospital. We conducted an observational study used for key player determination (or key node identification), then observed the selected key nodes twice with a one-month interval. Next, surface swabs of key nodes and their connecting nodes were analysed using bacterial identification, matrix-assisted laser desorption/ionisation-time of flight mass spectrometry, and pulsed-field gel electrophoresis. Altogether, our results showed that veterinarians were key players in this contact network in all departments. We found two predominant similarity clusters; dendrogram results suggested E. coli isolates from different time points and places to be closely related, providing evidence of HAI circulation within and across hospital departments. This study could aid in limiting the spread of HAIs in veterinary and human hospitals.

Avian and human influenza A virus receptors in trachea and lung of animals.

BACKGROUND: Influenza A viruses are capable of crossing the specific barrier between human beings and animals resulting in interspecies transmission. The important factor of potential infectivity of influenza A viruses is the suitability of the receptor binding site of the host and viruses. The affinities of avian and human influenza virus to bind with the receptors and the distributions of receptors in animals are different. OBJECTIVE: This study aims to investigate the anatomical distribution of avian and human influenza virus receptors using the double staining lectin histochemistry method. METHODS: Double staining of lectin histochemistry was performed to identify both SA alpha2,3 Gal and SA alpha2,6 Gal receptors in trachea and lung tissue of dogs, cats, tigers, ferret, pigs, ducks and chickens. RESULTS: We have demonstrated that avian and human influenza virus receptors were abundantly present in trachea, bronchus and bronchiole, but in alveoli of dogs, cats and tigers showed SA alpha2,6 Gal only. Furthermore, endothelial cells in lung tissues showed presence of SA alpha2,3 Gal. CONCLUSION: The positive sites of both receptors in respiratory tract, especially in the trachea, suggest that all mammalian species studied can be infected with avian influenza virus. These findings suggested that dogs and cats in close contact with humans should be of greater concern as an intermediate host for avian influenza A in which there is the potential for viral adaptation and reassortment.

Risk factors of clinical mastitis and antimicrobial susceptibility test results of mastitis milk from dairy cattle in western Thailand: Bayesian network analysis.

Using Bayesian network analysis, this cross-sectional study aimed to identify the conditional probability among dairy farm practices, cow characteristics, bacteriological culture results, and antimicrobial susceptibility test results of milk from dairy cows with clinical mastitis in western Thailand. Data associated with risk factors and clinical signs were collected using a structured questionnaire that was administered to 34 small dairy holders. In total, 100 quarters of milk samples from 100 cows were used for Bayesian network analysis. Conditional probability results showed that the following variables had the highest probabilities relevant to the occurrence of clinical mastitis pathogens: parity, concrete and rubber floor, hand stripping after using machine milking, dry cow therapy, and routine cleaning of milking machines. These variables were associated with the first four highest posterior probabilities of the occurrence of Streptococcus spp. (16.68%; reachable range or the minimum and maximum posterior probability values for the occurrence of Streptococcus spp., 15.45%-17.91%), Staphylococcus spp. (11.87%; reachable range, 11.06%-12.67%), Escherichia coli (7.53%; reachable range, 6.95%-8.17%), and Streptococcus dysgalactiae (7.28%; reachable range, 6.73%-7.83%), which were the most frequently isolated pathogens. Conditional probability results indicated these pathogens were most sensitive to amoxicillin/clavulanic acid (80.58%) and cloxacillin (64.28%). Most pathogens were resistant to penicillin G (40.37%). In this study, Bayesian network analysis revealed several clinically significant risk factors of mastitis associated with various pathogens and farming characteristics. Simple statistics could not provide sufficient information for the successful control of mastitis. In contrast, through in-depth data analysis, Bayesian networks could identify risk factors in various situations, hence providing information that will be crucial to help farmers reduce the cost of farming.

Exponential random graph models to evaluate the movement of backyard chickens after the avian influenza crisis in 2004-2005, Thailand.

The aim of this study was to use exponential random graph models (ERGMs) to explain networks of movement of backyard chickens in provinces which had been hotspots for avian influenza outbreaks in Thailand during 2004-2005. We used structured questionnaires to collect data for the period January to December 2009 from participants who were involved in the backyard chicken farming network in three avian influenza hotspots (Ratchaburi, Suphan Buri, and Nakhon Pathom provinces) in Thailand. From 557 questionnaires, we identified nodes, points of entry and exit from nodes, and activities relating to backyard chicken farming and movement of chickens, and generated ERGMs based on non-festive periods (Model 1) and the Chinese New Year period (Model 2). In Model 1, k-star (the central node is connected to k other nodes) connections were predominant (P < 0.001). In Model 2, the frequency of movement increased by 10.62 times, k-star connections were still predominant (P < 0.001), and the model was scale-free. Hubs were formed from owners/observers in the arenas/training fields, farmers who raised chickens for consumption only, and traders. In conclusion, our models indicated that, if avian influenza was introduced during non-festive periods, the authorities would need to regularly restrict the movement of chickens. However, during high-frequency periods of movement of backyard chickens, authorities would also need to focus on the network hubs. Our research can be used by the relevant authorities to improve control measures and reduce the risk or lessen the magnitude of disease spread during an avian influenza epidemic.

Social Network Analysis of Cattle Movement in Sukhothai Province, Thailand: A Study to Improve Control Measurements.

The aim of this study is to analyse the pattern of cattle movement in Sukhothai province, Thailand. A validated questionnaire was applied to 308 respondents related to cattle farming using one-step snowball sampling. The results showed that most of the nodes are farmers who move their animals in the province. The average normalized degree centrality and normalized closeness centrality were low (<0.01 and 0.04, resp.). We found that traders are the nodes with a high value of centrality. This corresponds with the cutpoint analysis results that traders are outstanding. In conclusion, the relevant authorities should focus on the nodes such as traders for controlling disease. However, a measure to detect disease in the early stages needs to be implemented.

Social network analysis used to assess the relationship between the spread of avian influenza and movement patterns of backyard chickens in Ratchaburi, Thailand.

In this study, we describe the movement and trading patterns of the backyard chicken in Ratchaburi, Thailand by using social network analysis with egocentric approach. From questionnaire results, we found that there is a close relationship between chicken owners' houses and fresh markets, and we concluded that this relationship needs attention from authorities to prevent future outbreaks of avian flu. Control measures should be applied over pathways to prevent and control highly pathogenic avian influenza subtype H5N1 in the backyard farming system. Results of our study may be useful to relevant authorities and researchers seeking to understand how H5N1 spreads in Ratchaburi. This may reflect on the spread of H5N1 throughout Thailand.

Risk factors for highly pathogenic avian influenza (HPAI) H5N1 infection in backyard chicken farms, Thailand.

To reduce the risk of highly pathogenic avian influenza (HPAI) H5N1 infection in humans, the pathways by which HPAI is spread in poultry must be determined. Backyard poultry farmers are particularly vulnerable to the threat of HPAI, with both their health and livelihoods at risk. Identifying the risk factors for HPAI infection in backyard farms should allow control measures to be better targeted. To study the risk factors of HPAI H5N1 infection, we carried out a case-control study on backyard chicken farms in Thailand, analyzing 104 case farms and 382 control farms. Data on farming practices and environmental characteristics were analyzed using multivariate logistic regression models. We show that farms where owners bought live chickens from another backyard farm had a higher risk of HPAI H5N1 infection (OR 3.34, 95% CI 1.72-6.47), while those where owners used a disinfectant to clean poultry areas were exposed to lower risk (OR 0.48, 95% CI 0.26-0.87). Our results highlight the important role of the trade of poultry between farms in the transmission of HPAI H5N1, in addition to farming practices and environmental characteristics. Findings from this study may help to tailor prevention measures to the local circumstances of backyard farms in different regions of the world.