Small distances can keep bacteria at bay for days

Transmission rates of veterinary and clinically important antibiotic resistant Escherichia coli: A meta- ANALYSIS

The transmission rate per hour between hosts is a key parameter for simulating transmission dynamics of antibiotic-resistant bacteria, and might differ for antibiotic resistance genes, animal species, and antibiotic usage. We conducted a Bayesian meta-analysis of resistant Escherichia coli (E. coli) transmission in broilers and piglets to obtain insight in factors determining the transmission rate, infectious period, and reproduction ratio. We included blaCTX-M-1, blaCTX-M-2, blaOXA-162, catA1, mcr-1, and fluoroquinolone resistant E. coli. The Maximum a Posteriori (MAP) transmission rate in broilers without antibiotic treatment ranged from 0.4∙10-3 to 2.5∙10-3 depending on type of broiler (SPF vs conventional) and inoculation strains. For piglets, the MAP in groups without antibiotic treatment were between 0.7∙10-3 and 0.8∙10-3, increasing to 0.9∙10-3 in the group with antibiotic treatment. In groups without antibiotic treatment, the transmission rate of resistant E. coli in broilers was almost twice the transmission rate in piglets. Amoxicillin increased the transmission rate of E. coli carrying blaCTX-M-2 by three-fold. The MAP infectious period of resistant E. coli in piglets with and without antibiotics is between 971 and 1065 hours (40 - 43 days). The MAP infectious period of resistant E. coli in broiler without antibiotics is between 475 and 2306 hours (20 - 96 days). The MAP infectious period of resistant E. coli in broiler with antibiotics is between 2702 and 3462 hours (113 - 144 days) which means a lifelong colonization. The MAP basic reproduction ratio in piglets of infection with resistant E. coli when using antibiotics is 27.70, which is higher than MAP in piglets without antibiotics between 15.65 and 18.19. The MAP basic reproduction ratio in broilers ranges between 3.46 and 92.38. We consider three possible explanations for our finding that in the absence of antibiotics the transmission rate is higher among broilers than among piglets: i) due to the gut microbiome of animals, ii) fitness costs of bacteria, and iii) differences in experimental set-up between the studies. Regarding infectious period and reproduction ratio, the effect of the resistance gene, antibiotic treatment, and animal species are inconclusive due to limited data.

Comparing the transmission of carbapenemase-producing and extended-spectrum beta-lactamase-producing Escherichia coli between broiler chickens

The emergence of carbapenemase-producing Enterobacteriaceae (CPE) is a threat to public health, because of their resistance to clinically important carbapenem antibiotics. The emergence of CPE in meat-producing animals is particularly worrying because consumption of meat contaminated with resistant bacteria comparable to CPE, such as extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae, contributed to colonization in humans worldwide. Currently, no data on the transmission of CPE in livestock is available. We performed a transmission experiment to quantify the transmission of CPE between broilers to fill this knowledge gap and to compare the transmission rates of CPE and other antibiotic-resistant E. coli. A total of 180 Ross 308 broiler chickens were distributed over 12 pens on the day of hatch (day 0). On day 5, half of the 10 remaining chickens in each pen were orally inoculated with 5·102 colony-forming units of CPE, ESBL, or chloramphenicol-resistant E. coli (catA1). To evaluate the effect of antibiotic treatment, amoxicillin was given twice daily in drinking water in 6 of the 12 pens from days 2-6. Cloacal swabs of all animals were taken to determine the number of infectious broilers. We used a Bayesian hierarchical model to quantify the transmission of the E. coli strains. E. coli can survive in the environment and serve as a reservoir. Therefore, the susceptible-infectious transmission model was adapted to account for the transmission of resistant bacteria from the environment. In addition, the caecal microbiome was analyzed on day 5 and at the end of the experiment on day 14 to assess the relationship between the caecal microbiome and the transmission rates. The transmission rates of CPE were 52 - 68 per cent lower compared to ESBL and catA1, but it is not clear if these differences were caused by differences between the resistance genes or by other differences between the E. coli strains. Differences between the groups in transmission rates and microbiome diversity did not correspond to each other, indicating that differences in transmission rates were probably not caused by major differences in the community structure in the caecal microbiome. Amoxicillin treatment from day 2-6 increased the transmission rate more than three-fold in all inoculums. It also increased alpha-diversity compared to untreated animals on day 5, but not on day 14, suggesting only a temporary effect. Future research could incorporate more complex transmission models with different species of resistant bacteria into the Bayesian hierarchical model.

Combining a parsimonious mathematical model with infection data from tailor-made experiments to understand environmental transmission

Although most infections are transmitted through the environment, the processes underlying the environmental stage of transmission are still poorly understood for most systems. Improved understanding of the environmental transmission dynamics is important for effective non-pharmaceutical intervention strategies. To study the mechanisms underlying environmental transmission we formulated a parsimonious modelling framework including hypothesised mechanisms of pathogen dispersion and decay. To calibrate and validate the model, we conducted a series of experiments studying distance-dependent transmission of Campylobacter jejuni in broilers. We obtained informative simultaneous estimates for all three model parameters: the parameter of C. jejuni inactivation, the diffusion coefficient describing pathogen dispersion, and the transmission rate parameter. The time and distance dependence of transmission in the fitted model is quantitatively consistent with marked spatiotemporal patterns in the experimental observations. These results, for C. jejuni in broilers, show that the application of our modelling framework to suitable transmission data can provide mechanistic insight in environmental pathogen transmission.

The Role of the Environment in Dynamics of Antibiotic Resistance in Humans and Animals: A Modelling Study

Antibiotic resistance is transmitted between animals and humans either directly or indirectly, through transmission via the environment. However, little is known about the contribution of the environment to resistance epidemiology. Here, we use a mathematical model to study the effect of the environment on human resistance levels and the impact of interventions to reduce antibiotic consumption in animals. We developed a model of resistance transmission with human, animal, and environmental compartments. We compared the model outcomes under different transmission scenarios, conducted a sensitivity analysis, and investigated the impacts of curtailing antibiotic usage in animals. Human resistance levels were most sensitive to parameters associated with the human compartment (rate of loss of resistance from humans) and with the environmental compartment (rate of loss of environmental resistance and rate of environment-to-human transmission). Increasing environmental transmission could lead to increased or reduced impact of curtailing antibiotic consumption in animals on resistance in humans. We highlight that environment-human sharing of resistance can influence the epidemiology of resistant bacterial infections in humans and reduce the impact of interventions that curtail antibiotic consumption in animals. More data on resistance in the environment and frequency of human-environment transmission is crucial to understanding antibiotic resistance dynamics.

Probabilistic modelling of effects of antibiotics and calendar time on transmission of healthcare-associated infection

Healthcare-associated infection and antimicrobial resistance are major concerns. However, the extent to which antibiotic exposure affects transmission and detection of infections such as MRSA is unclear. Additionally, temporal trends are typically reported in terms of changes in incidence, rather than analysing underling transmission processes. We present a data-augmented Markov chain Monte Carlo approach for inferring changing transmission parameters over time, screening test sensitivity, and the effect of antibiotics on detection and transmission. We expand a basic model to allow use of typing information when inferring sources of infections. Using simulated data, we show that the algorithms are accurate, well-calibrated and able to identify antibiotic effects in sufficiently large datasets. We apply the models to study MRSA transmission in an intensive care unit in Oxford, UK with 7924 admissions over 10 years. We find that falls in MRSA incidence over time were associated with decreases in both the number of patients admitted to the ICU colonised with MRSA and in transmission rates. In our inference model, the data were not informative about the effect of antibiotics on risk of transmission or acquisition of MRSA, a consequence of the limited number of possible transmission events in the data. Our approach has potential to be applied to a range of healthcare-associated infections and settings and could be applied to study the impact of other potential risk factors for transmission. Evidence generated could be used to direct infection control interventions.

Non-respiratory particles emitted by guinea pigs in airborne disease transmission experiments

Animal models are often used to assess the airborne transmissibility of various pathogens, which are typically assumed to be carried by expiratory droplets emitted directly from the respiratory tract of the infected animal. We recently established that influenza virus is also transmissible via "aerosolized fomites," micron-scale dust particulates released from virus-contaminated surfaces (Asadi et al. in Nat Commun 11(1):4062, 2020). Here we expand on this observation, by counting and characterizing the particles emitted from guinea pig cages using an Aerodynamic Particle Sizer (APS) and an Interferometric Mie Imaging (IMI) system. Of over 9000 airborne particles emitted from guinea pig cages and directly imaged with IMI, none had an interference pattern indicative of a liquid droplet. Separate measurements of the particle count using the APS indicate that particle concentrations spike upwards immediately following animal motion, then decay exponentially with a time constant commensurate with the air exchange rate in the cage. Taken together, the results presented here raise the possibility that a non-negligible fraction of airborne influenza transmission events between guinea pigs occurs via aerosolized fomites rather than respiratory droplets, though the relative frequencies of these two routes have yet to be definitively determined.

Competitive Exclusion Prevents Colonization and Compartmentalization Reduces Transmission of ESBL-Producing Escherichia coli in Broilers

Extended spectrum beta-lactamase (ESBL)-producing bacteria are resistant to extended-spectrum cephalosporins and are common in broilers. Interventions are needed to reduce the prevalence of ESBL-producing bacteria in the broiler production pyramid. This study investigated two different interventions. The effect of a prolonged supply of competitive exclusion (CE) product and compartmentalization on colonization and transmission, after challenge with a low dose of ESBL-producing Escherichia coli, in broilers kept under semi-field conditions, were examined. One-day-old broilers (Ross 308) (n = 400) were housed in four experimental rooms, subdivided in one seeder (S/C1)-pen and eight contact (C2)-pens. In two rooms, CE product was supplied from day 0 to 7. At day 5, seeder-broilers were inoculated with E. coli strain carrying bla CTX-M- 1 on plasmid IncI1 (CTX-M-1-E. coli). Presence of CTX-M-1-E. coli was determined using cloacal swabs (day 5-21 daily) and cecal samples (day 21). Time until colonization and cecal excretion (log10 CFU/g) were analyzed using survival analysis and linear regression. Transmission coefficients within and between pens were estimated using maximum likelihood. The microbiota composition was assessed by 16S ribosomal RNA gene amplicon sequencing in cecal content of broilers on days 5 and 21. None of the CE broilers was CTX-M-1-E. coli positive. In contrast, in the untreated rooms 187/200 of the broilers were CTX-M-1-E. coli positive at day 21. Broilers in C2-pens were colonized later than seeder-broilers (Time to event Ratio 3.53, 95% CI 3.14 to 3.93). The transmission coefficient between pens was lower than within pens (3.28 × 10-4 day-2, 95% CI 2.41 × 10-4 to 4.32 × 10-4 vs. 6.12 × 10-2 day-2, 95% CI 4.78 × 10-2 to 7.64 × 10-2). The alpha diversity of the cecal microbiota content was higher in CE broilers than in control broilers at days 5 and 21. The supply of a CE product from day 0 to 7 prevented colonization of CTX-M-1-E. coli after challenge at day 5, likely as a result of CE induced effects on the microbiota composition. Furthermore, compartmentalization reduced transmission rate between broilers. Therefore, a combination of compartmentalization and supply of a CE product may be a useful intervention to reduce transmission and prevent colonization of ESBL/pAmpC-producing bacteria in the broiler production pyramid.

Take care of the environment: housing conditions affect the interplay of nutritional interventions and intestinal microbiota in broiler chickens

Background

The intestinal microbiota is shaped by many interactions between microorganisms, host, diet, and the environment. Exposure to microorganisms present in the environment, and exchange of microorganisms between hosts sharing the same environment, can influence intestinal microbiota of individuals, but how this affects microbiota studies is poorly understood. We investigated the effects of experimental housing circumstances on intestinal microbiota composition in broiler chickens, and how these effects may influence the capacity to determine diet related effects in a nutrition experiment. A cross-sectional experiment was conducted simultaneously in a feed research facility with mesh panels between pens (Housing condition 1, H1), in an extensively cleaned stable with floor pens with solid wooden panels (H2), and in isolators (H3). In H1 and H2 different distances between pens were created to assess gut microbiota exchange between pens. Feed with and without a blend of medium-chain fatty acids (MCFA) was used to create differences in cecal microbiota between pens or isolators within the same housing condition. Male one-day-old Ross broiler chickens (n = 370) were randomly distributed across H1, H2, and H3. After 35 days cecal microbiota composition was assessed by 16S ribosomal RNA gene amplicon sequencing. Metabolic functioning of cecal content was assessed based on high-performance liquid chromatography.

Results

Microbial alpha diversity was not affected in broilers fed +MCFA in H1 but was increased in H2 and H3. Based on weighted UniFrac distances, the nutritional intervention explained 10%, whereas housing condition explained 28% of cecal microbiota variation between all broilers. The effect size of the nutritional intervention varied within housing conditions between 11, 27, and 13% for H1, H2, and H3. Furthermore, performance and metabolic output were significantly different between housing conditions. The distance between pens within H1 and H2 did not influence the percentage of shared genera or operational taxonomic units (OTUs).

Conclusions

The cecal microbiota of broilers was modifiable by a nutritional intervention, but the housing condition affected microbiota composition and functionality stronger than the diet intervention. Consequently, for interpretation of intestinal microbiota studies in poultry it is essential to be aware of the potentially large impact of housing conditions on the obtained results.

Electronic supplementary material

The online version of this article (10.1186/s42523-019-0009-z) contains supplementary material, which is available to authorized users.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Estimating the between-farm transmission rates for highly pathogenic avian influenza subtype H5N1 epidemics in Bangladesh between 2007 and 2013

Highly Pathogenic Avian Influenza (HPAI) is classified by the World Organization for Animal Health as one of the notifiable diseases. Its occurrence is associated with severe socio-economic impacts and is also zoonotic. Bangladesh HPAI epidemic data for the period between 2007 and 2013 were obtained and split into epidemic waves based on the time lag between outbreaks. By assuming the number of newly infected farms to be binomially distributed, we fit a Generalized Linear Model to the data to estimate between-farm transmission rates (β). These parameters are then used together with the calculated infectious periods to estimate the respective basic reproduction numbers (R₀ ). The change in β and R₀ with time during the course of each epidemic wave was explored. Finally, sensitivity analyses of the effects of reducing the delay in detecting infection on a farm as well as extended infectiousness of a farm beyond the day of culling were assessed. The point estimates obtained for β ranged from 0.08 (95% CI: 0.06-0.10) to 0.11 (95% CI: 0.08-0.20) per infectious farm per day while R₀ ranged from 0.85 (95% CI: 0.77-1.02) to 0.96 (95% CI: 0.72-1.20). Sensitivity analyses reveal that the estimates are quite robust to changes in the assumptions about the day in reporting infection and extended infectiousness. In the analysis allowing for time-varying transmission parameters, the rising and declining phases observed in the epidemic data were synchronized with the moments when R₀ was greater and less than one, respectively. From an epidemiological perspective, the consistency of these estimates and their magnitude (R₀  ≈ 1) indicate that the effectiveness of the deployed control measures was largely invariant between epidemic waves and the trend of the time-varying R₀ supports the hypothesis of sustained farm-to-farm transmission that is possibly initiated by a few unique introductions.

Eight challenges in modelling infectious livestock diseases

The transmission of infectious diseases of livestock does not differ in principle from disease transmission in any other animals, apart from that the aim of control is ultimately economic, with the influence of social, political and welfare constraints often poorly defined. Modelling of livestock diseases suffers simultaneously from a wealth and a lack of data. On the one hand, the ability to conduct transmission experiments, detailed within-host studies and track individual animals between geocoded locations make livestock diseases a particularly rich potential source of realistic data for illuminating biological mechanisms of transmission and conducting explicit analyses of contact networks. On the other hand, scarcity of funding, as compared to human diseases, often results in incomplete and partial data for many livestock diseases and regions of the world. In this overview of challenges in livestock disease modelling, we highlight eight areas unique to livestock that, if addressed, would mark major progress in the area.

Extended-spectrum and AmpC β-lactamase-producing Escherichia coli in broilers and people living and/or working on broiler farms: prevalence, risk factors and molecular characteristics

OBJECTIVES

The objectives of this study were to: estimate the prevalence of extended-spectrum β-lactamase (ESBL)- and AmpC β-lactamase-producing Escherichia coli carriage among broiler farmers, their family members and employees; identify and quantify risk factors for carriage, with an emphasis on contact with live broilers; and compare isolates from humans and broilers within farms with respect to molecular characteristics to gain insight into transmission routes.

METHODS

A cross-sectional prevalence study was conducted on 50 randomly selected Dutch broiler farms. Cloacal swabs were taken from 20 randomly chosen broilers. Faecal swabs were returned by 141 individuals living and/or working on 47 farms. ESBL/AmpC-producing E. coli were isolated and, for selected isolates, phylogenetic groups, plasmids and sequence types were determined. Questionnaires were used for risk factor analysis.

RESULTS

All sampled farms were positive, with 96.4% positive pooled broiler samples. The human prevalence was 19.1%, with 14.3% and 27.1% among individuals having a low and a high degree of contact with live broilers, respectively. Five pairs of human-broiler isolates had identical genes, plasmid families and E. coli sequence types, showing clonal transmission. Furthermore, similar ESBL/AmpC genes on the same plasmid families in different E. coli sequence types in humans and broilers hinted at horizontal gene transfer.

CONCLUSIONS

The prevalence among people on broiler farms was higher than in previous studies involving patients and the general population. Furthermore, an increased risk of carriage was shown among individuals having a high degree of contact with live broilers. The (relative) contribution of transmission routes that might play a role in the dissemination of ESBL/AmpC-encoding resistance genes to humans on broiler farms should be pursued in future studies.