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.

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.

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.

The impact of farm practices and wild carriers on white spot disease in marine shrimp in Rayong Province, Thailand.

Background and Aim: White spot disease (WSD) is a highly lethal and contagious viral disease in marine shrimp caused by the white spot syndrome virus (WSSV). White spot disease impacts the worldwide crustacean aquaculture sector, including Thailand. This study aimed to investigate the effect of farm management practices and wild carriers on WSD occurrence in grow-out marine shrimp farms in Rayong Province, Thailand. Materials and Methods: A longitudinal study was conducted using a structured questionnaire from June 2018 to June 2020. A total of 186 questionnaires for 186 ponds were collected from 15 shrimp farms. Univariate and multivariable analyses using generalized estimating equations were used to determine the risk factors associated with WSD. In addition, possible carrier samples (wild shrimp and wild crabs) were collected inside and outside farms to test for the presence of WSSV. Results: Direct discharge of treated wastewater into farm ponds was statistically significant in the final model (p < 0.01), with an odd ratio (OR) factor of 0.097 (95% confidence interval [CI] of OR = 0.007-0.242). Pooled sampling for WSSV in wild shrimp and crabs showed that 48 out of 936 (5.13%) samples tested positive for WSD using nested polymerase chain reaction. The samples from banana shrimp, jinga shrimp, banded snapping shrimp, dwarf prawn, whiteleg shrimp, green tidal crabs, and mangrove crabs tested positive. Conclusion: Based on the findings of this study, we infer that the environment plays an important role in the spread of this disease. The results of this study will provide insights into the effective planning of disease control.

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.

Post-larvae movement network of marine shrimp during the 2013 outbreak of acute hepatopancreatic necrosis disease in Thailand.

This study aimed to assess the movement patterns of post-larvae (PL) of whiteleg shrimp and black tiger shrimp, which were linked to the epidemic of acute hepatopancreatic necrosis disease (AHPND) in Thailand in 2013, using social network analysis. Hatcheries or nurseries were assigned as origin nodes, whereas cultivated ponds were designated as destination nodes. From 376 farms, data of 1300 ponds were collected from network data using a questionnaire. We found that 1477 nodes with 1315 ties appeared in this study. These were divided into a network of cases and controls that presented 538 nodes with 436 ties and 1013 nodes with 879 ties, respectively. The top four out-degree centrality values in the entire partial network, cases, and controls were used to analyze ego-alter similarity. The results showed that each PL provider played a different role in the association with the disease sender. A simulated network using exponential random graph models presented edges as a significant microstructure. In addition, our analysis indicated that the hatchery or nursery (PL provider) is an important node for AHPND occurrence. In conclusion, this study led us to understand the source of PL, which plays a key role in the surveillance, prevention, and control of AHPND.

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.

Identification of risk areas for foot and mouth disease in Thailand using a geographic information system-based multi-criteria decision analysis.

In our study, we used geographic information system (GIS)-based multi-criteria decision analysis (MCDA) to predict suitable areas for foot and mouth disease (FMD) occurrence in Thailand. Eleven experts evaluated 10 spatial risk factors associated with the occurrence and spread of FMD in Thailand during 2014-2015. The analytic hierarchy process was used to conduct problem structuring and prioritising of pairwise comparisons with criterion weighting. Important spatial risk factors were converted to geographical layers using standardised fuzzy membership. Thus, weight linear combination was used to combine and create suitability and uncertainty maps as well as to perform sensitivity analysis. We identified areas in northern, north-eastern, western, and central Thailand as hotspots of FMD occurrence. In the predictive map, the suitable areas presented a moderate degree of agreement with those after FMD outbreaks in the year 2016 (AUC = 0.71, 95 %CI: 0.68-0.75). In conclusion, GIS-based MCDA mapping well supported veterinary services in identifying hotspot areas of FMD occurrence in Thailand. This tool was very useful for disease surveillance.

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.

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.

Social network analysis for the assessment of pig, cattle and buffalo movement in Xayabouli, Lao PDR.

The aim of this study is to understand the role that the movement patterns of pigs, cattle and buffalo play in the spread of foot-and-mouth disease (FMD). A cross-sectional survey consisting of a questionnaire was used in a hotspot area for FMD: Xayabouli Province, Lao People's Democratic Republic. A total of 189 respondents were interviewed. We found that the key players in this network were people who were involved with more than one species of animal or occupation (multipurpose occupational node), which represents the highest number of activities of animals moved off the holding (shown with the highest out-degree centrality) and a high likelihood of being an intermediary between others (shown with the highest betweenness centrality). Moreover, the results show that the animals moved to and away from each node had few connections. Some nodes (such as traders) always received animals from the same group of cattle owners at different times. The subgroup connection within this network has many weak components, which means a connection in this network shows that some people can be reached by others, but most people were not. In this way, the number of connections present in the network was low when we defined the proportion of observed connections with all possible connections (density). These findings indicate that the network might not be busy; only one type of node is dominant which enables increased control of disease spread. We recommend that the relevant authorities implement control measures regarding the key players, which is the best way to effectively control the spread of infectious diseases.

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.

Risk factors associated with white spot syndrome virus outbreaks in marine shrimp farms in Rayong Province, Thailand.

The purpose of this study was to determine risk factors for white spot disease (WSD) in Rayong, Thailand. A study was conducted from October 2014 to March 2015 to identify potential farm-level risk factors using a validated questionnaire. We completed 1 questionnaire for each farm; a total of 38 questionnaires from the case farms and 127 questionnaires from the control farms were collected. The results showed that the presence of WSD in previous crops and the use of seawater were risk factors (p < 0.01), indicating that the environment plays an important role in WSD outbreaks in Rayong. Good management practices for pond preparation and other mitigation steps should be part of a control measure program for WSD in this region.

Collective resistance to HPAI H5N1 surveillance in the Thai cockfighting community: Insights from a social anthropology study.

Farmers may organize themselves to collectively manage risks such as animal diseases. Our study shows some evidence of such organization among fighting cock owners in Thailand. Fighting cocks were specifically targeted by HPAI (Highly Pathogenic Avian Influenza) H5N1 surveillance and control measures in Thailand because they were thought to pose a high risk of spreading diseases. In this work, we used a social-anthropological approach to gain an inside view of the issues associated with HPAI H5N1 surveillance in the cockfighting community in Thailand. Based on a qualitative analysis of data collected through in-depth interviews and observation of cockfighters' practices, we found that fighting cock owners share a sense of belonging to the same community based on a common culture, values, interests, practices, and internal rules, including rules to manage poultry diseases. During the HPAI H5N1 outbreaks, these rules may have contributed to mitigating the potential risk associated with the intense movements of fighting cocks inside the country. Nevertheless, this community, despite the high awareness and know-how of its members regarding poultry diseases, has shown a strong reluctance to comply with HPAI surveillance programs. We suggest that this reluctance is due to important gaps between the logic and rationales underlying surveillance and those associated with cockfighting activities. Our study highlights the need for multi and trans-disciplinary research involving the social sciences to analyze interactions between stakeholders and the collective actions implemented by communities to face risks.

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.

Practices associated with Highly Pathogenic Avian Influenza spread in traditional poultry marketing chains: Social and economic perspectives.

In developing countries, smallholder poultry production contributes to food security and poverty alleviation in rural areas. However, traditional poultry marketing chains have been threatened by the epidemics caused by the Highly Pathogenic Avian Influenza (H5N1) virus. The article presents a value chain analysis conducted on the traditional poultry marketing chain in the rural province of Phitsanulok, Thailand. The analysis is based on quantitative data collected on 470 backyard chicken farms, and on qualitative data collected on 28 poultry collectors, slaughterhouses and market retailers, using semi-structured interviews. The article examines the organization of poultry marketing chains in time and space, and shows how this may contribute to the spread of Highly Pathogenic Avian Influenza H5N1 in the small-scale poultry sector. The article also discusses the practices and strategies developed by value chain actors facing poultry mortality, with their economic and social determinants. More broadly, this study also illustrates how value chain analysis can contribute to a better understanding of the complex mechanisms associated with the spread of epidemics in rural communities.