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

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

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

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 μg/mL (49%) and ≥16 μg/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

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