Antimicrobial resistance is a major health problem, particularly in developing countries like Bangladesh, where there is a paucity of information on resistance patterns and prevalence of antimicrobial determinants. Therefore, the aims of this study were to investigate the prevalence of resistance, including multi-drug resistance (MDR), and the associated genetic determinants in Escherichia coli isolates from cloacal swabs of live broiler chickens in Bangladesh. Altogether, 400 cloacal swabs (200 from Rajshahi and 200 from Dhaka divisions) were randomly collected from individual chickens in 50 broiler farms. E. coli was isolated and identified using conventional bacteriological culture and biochemical methods. The isolates were further confirmed using genus-specific 16S rRNAtargeted polymerase chain reaction (PCR) primers. Antimicrobial susceptibilities and MDR of the isolates against nine different antimicrobial agents (ampicillin, erythromycin, tetracycline, gentamicin, ciprofloxacin, levofloxacin, trimethoprim-sulfamethoxazole, colistin sulphate, and streptomycin) were determined using the Kirby-Bauer disc diffusion method. Resistance determinants of E. coli to ampicillin (blaTEM), streptomycin (aadA1), erythromycin [ere(A)], trimethoprim (dfrA1), and tetracycline [tet(A), tet(B)] were screened using PCR. Our results showed that all swab samples were positive for E. coli. The isolates were uniformly resistant to ampicillin, tetracycline, streptomycin, ciprofloxacin, erythromycin, and trimethoprim-sulphamethoxazole. The isolates exhibited highest susceptibility to colistin sulphate (73.5%), followed by gentamicin (49%), and levofloxacin (17%). All isolates were resistant to three classes of antibiotics, 204 isolates (51%) were resistant to four classes, and 56 isolates (14%) were resistant to five. The highest prevalence of antimicrobial resistance gene was recorded for tetracycline (tet(A):95.25%; tet(B):95.25%) followed by ampicillin (blaTEM:91.25%), streptomycin (aadA1:88.25%), erythromycin (ere(A):84.75%), and trimethoprim (dfrA1:65.5%). In conclusion, surveillance for MDR bacteria in poultry is a critical piece of knowledge, which would be useful for optimizing empiric antimicrobial treatments and exploring alternative antimicrobial agents.

BackgroundElephant endotheliotropic herpesviruses (EEHV) can cause an acute highly fatal hemorrhagic disease in young Asian elephants (Elephas maximus), both ex situ and in situ. Amongst eight EEHV types described so far, type 1 (subtype 1A and 1B) is the predominant disease-associated type. Little is known about routes of infection and pathogenesis of EEHV, and knowledge of disease prevalence, especially in range countries, is limited.MethodsA large cross-sectional serological survey was conducted in captive elephants (n = 994) throughout Thailand using an EEHV-1A glycoprotein B protein antigen specific antibody ELISA.ResultsAntibody seroprevalence was 42.3%, with 420 of 994 elephants testing positive. Associations between seropositivity and potential risk factors for EEHV infection were assessed and included: elephant age, sex, camp cluster size, management type (extensive versus intensive), sampling period (wet vs. dry season) and location of camp (region). Univariable regression analysis identified management system and region as risk factors for the presence of EEHV antibodies in elephants, with region being significant in the final multivariable regression model. Prevalence was highest in the North region of the country (49.4%).ConclusionsThis study produced baseline serological data for captive elephants throughout Thailand, and showed a significant EEHV burden likely to be maintained in the captive population.Electronic supplementary materialThe online version of this article (10.1186/s12985-019-1142-8) contains supplementary material, which is available to authorized users.

The Philippines confirmed its first epidemic of Highly Pathogenic Avian Influenza (HPAI) on August 11, 2017. It ended in November of 2017. Despite the successful management of the epidemic, reemergence is a continuous threat. The aim of this study was to conduct a mathematical model to assess the spatial transmission of HPAI among poultry farms in Central Luzon. Different control strategies and the current government protocol of 1 km radius preemptive culling (PEC) from infected farms were evaluated. The alternative strategies include 0.5km PEC, 1.5km PEC, 2 km PEC, 2.5 km PEC, and 3 km PEC, no pre-emptive culling (NPEC). The NPEC scenario was further modeled with a time of government notification set at 24hours, 48 hours, and 72 hours after the detection. Disease spread scenarios under each strategy were generated using an SEIR (susceptible-exposed-infectious-removed) stochastic model. A spatial transmission kernel was calculated and used to represent all potential routes of infection between farms. We assumed that the latent period occurs between 1-2 days, disease detection at 5-7 days post-infection, notification of authorities at 5-7 days post-detection and start of culling at 1-3 days post notification. The epidemic scenarios were compared based on the number of infected farms, the total number of culled farms, and the duration of the epidemic. Our results revealed that the current protocol is the most appropriate option compared with the other alternative interventions considered among farms with reproductive ratio (R i) > 1. Shortening the culling radius to 0.5 km increased the duration of the epidemic. Further increase in the PEC zone decreased the duration of the epidemic but may not justify the increased number of farms to be culled. Nonetheless, the no-pre-emptive culling (NPEC) strategy can be an effective alternative to

Background: Foot and mouth disease (FMD) is a highly infectious and contagious febrile vesicular disease of cloven-hoofed livestock with high socio-economic consequences globally. In Thailand, FMD is endemic with 183 and 262 outbreaks occurring in the years 2015 and 2016, respectively. In this study, we aimed to assess the spatiotemporal distribution of FMD outbreaks among cattle in Chiang Mai and Lamphun provinces in the northern part of Thailand during the period of 2015-2016. A retrospective space-time scan statistic including a space-time permutation (STP) and the Poisson and Bernoulli models were applied in order to detect areas of high incidence of FMD. Results: Results have shown that 9 and 8 clusters were identified by the STP model in 2015 and 2016, respectively, whereas 1 and 3 clusters were identified by the Poisson model, and 3 and 4 clusters were detected when the Bernoulli model was applied for the same time period. In 2015, the most likely clusters were observed in Chiang Mai and these had a minimum radius of 1.49 km and a maximum radius of 20 km. Outbreaks were clustered in the period between the months of May and October of 2015. The most likely clusters in 2016 were observed in central Lamphun based on the STP model and in the eastern area of Chiang Mai by the Poisson and Bernoulli models. The cluster size of the STP model (8.51 km) was smaller than those of the Poisson and Bernoulli models (> 20 km). The cluster periods in 2016 were approximately 7 months, while 4 months and 1 month were identified by the Poisson, Bernoulli and STP models respectively.Conclusions: The application of three models provided more information for FMD outbreak epidemiology. The findings from this study suggest the use of three different space-time scan models for the investigation process of outbreaks along with the follow-up process to identify FMD outbreak clusters. Therefore, active prevention and control strategies should be implemented in the areas that are most susceptible to FMD outbreaks.

LSD is an important transboundary disease affecting the cattle industry worldwide. The objectives of this study were to determine trends and significant change points, and to forecast the number of LSD outbreak reports in Africa, Europe, and Asia. LSD outbreak report data (January 2005 to January 2022) from the World Organization for Animal Health were analyzed. We determined statistically significant change points in the data using binary segmentation, and forecast the number of LSD reports using auto-regressive moving average (ARIMA) and neural network auto-regressive (NNAR) models. Four significant change points were identified for each continent. The year between the third and fourth change points (2016–2019) in the African data was the period with the highest mean of number of LSD reports. All change points of LSD outbreaks in Europe corresponded with massive outbreaks during 2015–2017. Asia had the highest number of LSD reports in 2019 after the third detected change point in 2018. For the next three years (2022–2024), both ARIMA and NNAR forecast a rise in the number of LSD reports in Africa and a steady number in Europe. However, ARIMA predicts a stable number of outbreaks in Asia, whereas NNAR predicts an increase in 2023–2024. This study provides information that contributes to a better understanding of the epidemiology of LSD.