New methodology to assess the excess burden of antibiotic resistance using country-specific parameters: a case study regarding E. coli urinary tract infections.

OBJECTIVES: Antimicrobial resistant (AMR) infections are a major public health problem and the burden on population level is not yet clear. We developed a method to calculate the excess burden of resistance which uses country-specific parameter estimates and surveillance data to compare the mortality and morbidity due to resistant infection against a counterfactual (the expected burden if infection was antimicrobial susceptible). We illustrate this approach by estimating the excess burden for AMR (defined as having tested positive for extended-spectrum beta-lactamases) urinary tract infections (UTIs) caused by E. coli in the Netherlands in 2018, which has a relatively low prevalence of AMR E. coli, and in Italy in 2016, which has a relatively high prevalence. DESIGN: Excess burden was estimated using the incidence-based disability-adjusted life-years (DALYs) measure. Incidence of AMR E. coli UTI in the Netherlands was derived from ISIS-AR, a national surveillance system that includes tested healthcare and community isolates, and the incidence in Italy was estimated using data reported in the literature. A systematic literature review was conducted to find country-specific parameter estimates for disability duration, risks of progression to bacteraemia and mortality. RESULTS: The annual excess burden of AMR E. coli UTI was estimated at 3.89 and 99.27 DALY/100 0000 population and 39 and 2786 excess deaths for the Netherlands and Italy, respectively. CONCLUSIONS: For the first time, we use country-specific and pathogen-specific parameters to estimate the excess burden of resistant infections. Given the large difference in excess burden due to resistance estimated for Italy and for the Netherlands, we emphasise the importance of using country-specific parameters describing the incidence and disease progression following AMR and susceptible infections that are pathogen specific, and unfortunately currently difficult to locate.

A Bayesian inference method to estimate transmission trees with multiple introductions; applied to SARS-CoV-2 in Dutch mink farms.

Knowledge of who infected whom during an outbreak of an infectious disease is important to determine risk factors for transmission and to design effective control measures. Both whole-genome sequencing of pathogens and epidemiological data provide useful information about the transmission events and underlying processes. Existing models to infer transmission trees usually assume that the pathogen is introduced only once from outside into the population of interest. However, this is not always true. For instance, SARS-CoV-2 is suggested to be introduced multiple times in mink farms in the Netherlands from the SARS-CoV-2 pandemic among humans. Here, we developed a Bayesian inference method combining whole-genome sequencing data and epidemiological data, allowing for multiple introductions of the pathogen in the population. Our method does not a priori split the outbreak into multiple phylogenetic clusters, nor does it break the dependency between the processes of mutation, within-host dynamics, transmission, and observation. We implemented our method as an additional feature in the R-package phybreak. On simulated data, our method correctly identifies the number of introductions, with an accuracy depending on the proportion of all observed cases that are introductions. Moreover, when a single introduction was simulated, our method produced similar estimates of parameters and transmission trees as the existing package. When applied to data from a SARS-CoV-2 outbreak in Dutch mink farms, the method provides strong evidence for independent introductions of the pathogen at 13 farms, infecting a total of 63 farms. Using the new feature of the phybreak package, transmission routes of a more complex class of infectious disease outbreaks can be inferred which will aid infection control in future outbreaks.

Genome-wide association reveals host-specific genomic traits in Escherichia coli.

BACKGROUND: Escherichia coli is an opportunistic pathogen which colonizes various host species. However, to what extent genetic lineages of E. coli are adapted or restricted to specific hosts and the genomic determinants of such adaptation or restriction is poorly understood. RESULTS: We randomly sampled E. coli isolates from four countries (Germany, UK, Spain, and Vietnam), obtained from five host species (human, pig, cattle, chicken, and wild boar) over 16 years, from both healthy and diseased hosts, to construct a collection of 1198 whole-genome sequenced E. coli isolates. We identified associations between specific E. coli lineages and the host from which they were isolated. A genome-wide association study (GWAS) identified several E. coli genes that were associated with human, cattle, or chicken hosts, whereas no genes associated with the pig host could be found. In silico characterization of nine contiguous genes (collectively designated as nan-9) associated with the human host indicated that these genes are involved in the metabolism of sialic acids (Sia). In contrast, the previously described sialic acid regulon known as sialoregulon (i.e. nanRATEK-yhcH, nanXY, and nanCMS) was not associated with any host species. In vitro growth experiments with a Δnan-9 E. coli mutant strain, using the sialic acids 5-N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) as sole carbon source, showed impaired growth behaviour compared to the wild-type. CONCLUSIONS: This study provides an extensive analysis of genetic determinants which may contribute to host specificity in E. coli. Our findings should inform risk analysis and epidemiological monitoring of (antimicrobial resistant) E. coli.

The importance of sustained compliance with physical distancing during COVID-19 vaccination rollout.

BACKGROUND: Increasing vaccination coverage against SARS-CoV-2 enabled relaxation of lockdowns in many countries in Europe. As the vaccination rollouts progressed, the public health authorities were seeking recommendations on the continuation of physical distancing measures during ongoing vaccination rollouts. Compliance with these measures was declining while more transmissible virus variants have emerged. METHODS: We used a SARS-CoV-2 transmission model to investigate the feedback between compliance, infection incidence, and vaccination coverage. We quantified our findings in terms of cumulative number of new hospitalisations three and six months after the start of vaccination. RESULTS: Our results suggest that the combination of fast waning compliance in non-vaccinated individuals, low compliance in vaccinated individuals, low vaccine efficacy against infection and more transmissible virus variants may result in a higher cumulative number of new hospitalisations than in a situation without vaccination. These adverse effects can be alleviated by deploying behavioural interventions that should preferably target both vaccinated and non-vaccinated individuals. The choice of the most appropriate intervention depends on vaccination rate and vaccine efficacy against infection. CONCLUSIONS: Supplementary behavioural interventions aiming to boost compliance to physical distancing measures can improve the outcome of vaccination programmes, until vaccination coverage is sufficiently high. For optimal results, these interventions should be selected based on the vaccine efficacy against infection and expected vaccination rate. While we considered the dynamics of SARS-CoV-2, the qualitative effects of the interplay between infectious disease spread and behavior on the outcomes of a vaccination programme can be used as guidance in a future similar pandemic.

Duration of rectal colonization with extended-spectrum beta-lactamase-producing Escherichia coli: results of an open, dynamic cohort study in Dutch nursing home residents (2013-2019).

BACKGROUND: In 2016, a study in a Dutch nursing home showed prolonged colonization duration of extended-spectrum ß-lactamase-producing (ESBL)-ST131 compared to ESBL-non-ST131. In this study, we assessed the duration of rectal ESBL-producing E. coli (ESBL-EC) colonization in residents in the same nursing home for an extended period of six years. We aimed to estimate the influence of a possible bias when follow up is started during an outbreak. METHODS: Between 2013 and 2019, repetitive point prevalence surveys were performed by culturing rectal or faecal swabs from all residents. Kaplan-Meier survival analysis was performed to calculate the median time to clearance of ESBL-EC with a log-rank analysis to test for differences between ESBL-ST131 and ESBL-non-ST131. RESULTS: The study showed a median time to clearance of 13.0 months (95% CI 0.0-27.9) for ESBL-ST131 compared to 11.2 months (95% CI 4.8-17.6) for ESBL-non-ST131 (p = 0.044). In the subgroup analysis of residents who were ESBL-EC positive in their first survey, the median time to clearance for ST131 was 59.7 months (95% CI 23.7-95.6) compared to 16.2 months (95% CI 2.1-30.4) for ESBL-non-ST131 (p = 0.036). In the subgroup analysis of residents who acquired ESBL-EC, the median time to clearance for ST131 was 7.2 months (95% CI 2.1-12.2) compared to 7.9 months (95% CI 0.0-18.3) for ESBL-non-ST131 (p = 0.718). The median time to clearance in the ESBL-ST131 group was significantly longer in residents who were ESBL-ST131 colonised upon entering the study than in residents who acquired ESBL-ST131 during the study (p = 0.001). CONCLUSION: A prolonged colonization with ESBL-ST131 was only found in the subgroup who was ESBL-EC positive upon entering the study. The prolonged duration with ESBL-ST131 in the previous study was probably biased by factors that occured during (the start of) the outbreak.

Transmission routes of antibiotic resistant bacteria: a systematic review.

BACKGROUND: Quantification of acquisition routes of antibiotic resistant bacteria (ARB) is pivotal for understanding transmission dynamics and designing cost-effective interventions. Different methods have been used to quantify the importance of transmission routes, such as relative risks, odds ratios (OR), genomic comparisons and basic reproduction numbers. We systematically reviewed reported estimates on acquisition routes' contributions of ARB in humans, animals, water and the environment and assessed the methods used to quantify the importance of transmission routes. METHODS: PubMed and EMBASE were searched, resulting in 6054 articles published up until January 1st, 2019. Full text screening was performed on 525 articles and 277 are included. RESULTS: We extracted 718 estimates with S. aureus (n = 273), E. coli (n = 157) and Enterobacteriaceae (n = 99) being studied most frequently. Most estimates were derived from statistical methods (n = 560), mainly expressed as risks (n = 246) and ORs (n = 239), followed by genetic comparisons (n = 85), modelling (n = 62) and dosage of ARB ingested (n = 17). Transmission routes analysed most frequently were occupational exposure (n = 157), travelling (n = 110) and contacts with carriers (n = 83). Studies were mostly performed in the United States (n = 142), the Netherlands (n = 87) and Germany (n = 60). Comparison of methods was not possible as studies using different methods to estimate the same route were lacking. Due to study heterogeneity not all estimates by the same method could be pooled. CONCLUSION: Despite an abundance of published data the relative importance of transmission routes of ARB has not been accurately quantified. Links between exposure and acquisition are often present, but the frequency of exposure is missing, which disables estimation of transmission routes' importance. To create effective policies reducing ARB, estimates of transmission should be weighed by the frequency of exposure occurrence.

Routes of transmission of VIM-positive Pseudomonas aeruginosa in the adult intensive care unit-analysis of 9 years of surveillance at a university hospital using a mathematical model.

BACKGROUND: Hospital outbreaks of multidrug resistant Pseudomonas aeruginosa are often caused by Pseudomonas aeruginosa clones which produce metallo-ß-lactamases, such as Verona Integron-encoded Metallo-ß-lactamase (VIM). Although different sources have been identified, the exact transmission routes often remain unknown. However, quantifying the role of different transmission routes of VIM-PA is important for tailoring infection prevention and control measures. The aim of this study is to quantify the relative importance of different transmission routes by applying a mathematical transmission model using admission and discharge dates as well as surveillance culture data of patients. METHODS: We analyzed VIM-PA surveillance data collected between 2010 and 2018 of two intensive-care unit (ICU) wards for adult patients of the Erasmus University Medical Center Rotterdam using a mathematical transmission model. We distinguished two transmission routes: direct cross-transmission and a persistent environmental route. Based on admission, discharge dates, and surveillance cultures, we estimated the proportion of transmissions assigned to each of the routes. RESULTS: Our study shows that only 13.7% (95% CI 1.4%, 29%) of the transmissions that occurred in these two ICU wards were likely caused by cross-transmission, leaving the vast majority of transmissions (86.3%, 95% CI 71%, 98.6%) due to persistent environmental contamination. CONCLUSIONS: Our results emphasize that persistent contamination of the environment may be an important driver of nosocomial transmissions of VIM-PA in ICUs. To minimize the transmission risk from the environment, potential reservoirs should be regularly and thoroughly cleaned and disinfected, or redesigned.

Does plasmid-based beta-lactam resistance increase E. coli infections: Modelling addition and replacement mechanisms.

Infections caused by antibiotic-resistant bacteria have become more prevalent during past decades. Yet, it is unknown whether such infections occur in addition to infections with antibiotic-susceptible bacteria, thereby increasing the incidence of infections, or whether they replace such infections, leaving the total incidence unaffected. Observational longitudinal studies cannot separate both mechanisms. Using plasmid-based beta-lactam resistant E. coli as example we applied mathematical modelling to investigate whether seven biological mechanisms would lead to replacement or addition of infections. We use a mathematical neutral null model of individuals colonized with susceptible and/or resistant E. coli, with two mechanisms implying a fitness cost, i.e., increased clearance and decreased growth of resistant strains, and five mechanisms benefitting resistance, i.e., 1) increased virulence, 2) increased transmission, 3) decreased clearance of resistant strains, 4) increased rate of horizontal plasmid transfer, and 5) increased clearance of susceptible E. coli due to antibiotics. Each mechanism is modelled separately to estimate addition to or replacement of antibiotic-susceptible infections. Fitness costs cause resistant strains to die out if other strain characteristics are maintained equal. Under the assumptions tested, increased virulence is the only mechanism that increases the total number of infections. Other benefits of resistance lead to replacement of susceptible infections without changing the total number of infections. As there is no biological evidence that plasmid-based beta-lactam resistance increases virulence, these findings suggest that the burden of disease is determined by attributable effects of resistance rather than by an increase in the number of infections.

The discrete-time Kermack-McKendrick model: A versatile and computationally attractive framework for modeling epidemics.

The COVID-19 pandemic has led to numerous mathematical models for the spread of infection, the majority of which are large compartmental models that implicitly constrain the generation-time distribution. On the other hand, the continuous-time Kermack-McKendrick epidemic model of 1927 (KM27) allows an arbitrary generation-time distribution, but it suffers from the drawback that its numerical implementation is rather cumbersome. Here, we introduce a discrete-time version of KM27 that is as general and flexible, and yet is very easy to implement computationally. Thus, it promises to become a very powerful tool for exploring control scenarios for specific infectious diseases such as COVID-19. To demonstrate this potential, we investigate numerically how the incidence-peak size depends on model ingredients. We find that, with the same reproduction number and the same initial growth rate, compartmental models systematically predict lower peak sizes than models in which the latent and the infectious period have fixed duration.

Interventions to control nosocomial transmission of SARS-CoV-2: a modelling study.

BACKGROUND: Emergence of more transmissible SARS-CoV-2 variants requires more efficient control measures to limit nosocomial transmission and maintain healthcare capacities during pandemic waves. Yet the relative importance of different strategies is unknown. METHODS: We developed an agent-based model and compared the impact of personal protective equipment (PPE), screening of healthcare workers (HCWs), contact tracing of symptomatic HCWs and restricting HCWs from working in multiple units (HCW cohorting) on nosocomial SARS-CoV-2 transmission. The model was fit on hospital data from the first wave in the Netherlands (February until August 2020) and assumed that HCWs used 90% effective PPE in COVID-19 wards and self-isolated at home for 7 days immediately upon symptom onset. Intervention effects on the effective reproduction number (RE), HCW absenteeism and the proportion of infected individuals among tested individuals (positivity rate) were estimated for a more transmissible variant. RESULTS: Introduction of a variant with 56% higher transmissibility increased - all other variables kept constant - RE from 0.4 to 0.65 (+ 63%) and nosocomial transmissions by 303%, mainly because of more transmissions caused by pre-symptomatic patients and HCWs. Compared to baseline, PPE use in all hospital wards (assuming 90% effectiveness) reduced RE by 85% and absenteeism by 57%. Screening HCWs every 3 days with perfect test sensitivity reduced RE by 67%, yielding a maximum test positivity rate of 5%. Screening HCWs every 3 or 7 days assuming time-varying test sensitivities reduced RE by 9% and 3%, respectively. Contact tracing reduced RE by at least 32% and achieved higher test positivity rates than screening interventions. HCW cohorting reduced RE by 5%. Sensitivity analyses show that our findings do not change significantly for 70% PPE effectiveness. For low PPE effectiveness of 50%, PPE use in all wards is less effective than screening every 3 days with perfect sensitivity but still more effective than all other interventions. CONCLUSIONS: In response to the emergence of more transmissible SARS-CoV-2 variants, PPE use in all hospital wards might still be most effective in preventing nosocomial transmission. Regular screening and contact tracing of HCWs are also effective interventions but critically depend on the sensitivity of the diagnostic test used.

Effectiveness of contact tracing apps for SARS-CoV-2: a rapid systematic review.

OBJECTIVE: To systematically review evidence on effectiveness of contact tracing apps (CTAs) for SARS-CoV-2 on epidemiological and clinical outcomes. DESIGN: Rapid systematic review. DATA SOURCES: EMBASE (OVID), MEDLINE (PubMed), BioRxiv and MedRxiv were searched up to 28 October 2020. STUDY SELECTION: Studies, both empirical and model-based, assessing effect of CTAs for SARS-CoV-2 on reproduction number (R), total number of infections, hospitalisation rate, mortality rate, and other epidemiologically and clinically relevant outcomes, were eligible for inclusion. DATA EXTRACTION: Empirical and model-based studies were critically appraised using separate checklists. Data on type of study (ie, empirical or model-based), sample size, (simulated) time horizon, study population, CTA type (and associated interventions), comparator and outcomes assessed, were extracted. The most important findings were extracted and narratively summarised. Specifically for model-based studies, characteristics and values of important model parameters were collected. RESULTS: 2140 studies were identified, of which 17 studies (2 empirical, 15 model-based studies) were eligible and included in this review. Both empirical studies were observational (non-randomised) studies and at high risk of bias, most importantly due to risk of confounding. Risk of bias of model-based studies was considered low for 12 out of 15 studies. Most studies demonstrated beneficial effects of CTAs on R, total number of infections and mortality rate. No studies assessed effect on hospitalisation. Effect size was dependent on model parameters values used, but in general, a beneficial effect was observed at CTA adoption rates of 20% or higher. CONCLUSIONS: CTAs have the potential to be effective in reducing SARS-CoV-2 related epidemiological and clinical outcomes, though effect size depends on other model parameters (eg, proportion of asymptomatic individuals, or testing delays), and interventions after CTA notification. Methodologically sound comparative empirical studies on effectiveness of CTAs are required to confirm findings from model-based studies.

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic.

The role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (Re) with non-school-based measures are exhausted or undesired and Re is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced Re below 1, with unchanged non-school-based contacts.

Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: A modelling study.

BACKGROUND: The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to nearly every country in the world since it first emerged in China in December 2019. Many countries have implemented social distancing as a measure to "flatten the curve" of the ongoing epidemics. Evaluation of the impact of government-imposed social distancing and of other measures to control further spread of COVID-19 is urgent, especially because of the large societal and economic impact of the former. The aim of this study was to compare the individual and combined effectiveness of self-imposed prevention measures and of short-term government-imposed social distancing in mitigating, delaying, or preventing a COVID-19 epidemic. METHODS AND FINDINGS: We developed a deterministic compartmental transmission model of SARS-CoV-2 in a population stratified by disease status (susceptible, exposed, infectious with mild or severe disease, diagnosed, and recovered) and disease awareness status (aware and unaware) due to the spread of COVID-19. Self-imposed measures were assumed to be taken by disease-aware individuals and included handwashing, mask-wearing, and social distancing. Government-imposed social distancing reduced the contact rate of individuals irrespective of their disease or awareness status. The model was parameterized using current best estimates of key epidemiological parameters from COVID-19 clinical studies. The model outcomes included the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. For fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate and diminish and postpone the peak number of diagnoses. We estimate that a large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government-imposed social distancing alone is estimated to delay (by at most 7 months for a 3-month intervention) but not to reduce the peak. The delay can be even longer and the height of the peak can be additionally reduced if this intervention is combined with self-imposed measures that are continued after government-imposed social distancing has been lifted. Our analyses are limited in that they do not account for stochasticity, demographics, heterogeneities in contact patterns or mixing, spatial effects, imperfect isolation of individuals with severe disease, and reinfection with COVID-19. CONCLUSIONS: Our results suggest that information dissemination about COVID-19, which causes individual adoption of handwashing, mask-wearing, and social distancing, can be an effective strategy to mitigate and delay the epidemic. Early initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden. We stress the importance of disease awareness in controlling the ongoing epidemic and recommend that, in addition to policies on social distancing, governments and public health institutions mobilize people to adopt self-imposed measures with proven efficacy in order to successfully tackle COVID-19.

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.

BACKGROUND: In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful. METHODS: We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (RCTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts. FINDINGS: For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7-0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (RCTS 0·8, 95% CI 0·7-1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep RCTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach RCTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay. INTERPRETATION: In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage. FUNDING: ZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER.

gplas: a comprehensive tool for plasmid analysis using short-read graphs.

SUMMARY: Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data. AVAILABILITY AND IMPLEMENTATION: Gplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Author Correction: Global phylogenetic analysis of Escherichia coli and plasmids carrying the mcr-1 gene indicates bacterial diversity but plasmid restriction.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Prevalence and risk factors for carriage of ESBL-producing Enterobacteriaceae in a population of Dutch travellers: A cross-sectional study.

BACKGROUND: We investigated prevalence and predictive factors for ESBL-E carriage in a population of mostly travellers prior to their travel (n = 2216). In addition, we examined ESBL genotype before travel and compared these to returning travellers. METHOD: A questionnaire and faecal sample were collected before travel, and a second faecal sample was collected immediately after travel. Faecal samples were analysed for ESBL-E, with genotypic characterization by PCR and sequencing. Risk factors for ESBL-E carriage prior to travel were identified by logistic regression analyses. RESULTS: Before travel, 136 participants (6.1%) were colonized with ESBL-E. Antibiotic use in the past three months (ORadjusted 2.57; 95% CI 1.59-4.16) and travel outside of Europe in the past year (1.92, 1.28-2.87) were risk factors for ESBL-E colonisation prior to travel. Travel outside of Europe carried the largest attributable risk (39.8%). Prior to travel 31.3% (40/128) of participants carried blaCTX-M 15 and 21.9% (28/128) blaCTX-M 14/18. In returning travellers 633 acquired ESBL-E of who 53.4% (338/633) acquired blaCTX-M 15 and 17.7% (112/633) blaCTX-M 14/18. CONCLUSION: In our population of Dutch travellers we found a pre-travel ESBL-E prevalence of 6.1%. Prior to travel, previous antibiotic use and travel outside of Europe were the strongest independent predictors for ESBL-E carriage, with travel outside of Europe carrying the largest attributable risk. Our molecular results suggest ESBL genes found in our study population prior to travel were in large part travel related.

Dynamics of Intestinal Carriage of Extended-Spectrum Beta-lactamase-Producing Enterobacteriaceae in the Dutch General Population, 2014-2016.

BACKGROUND: In the Netherlands, the prevalence of intestinal extended-spectrum beta-lactamase-producing Enterobacteriaceae (ESBL-E) carriage in community-dwelling subjects is ~5%. Little is known about the dynamics of ESBL-E carriage. METHODS: In a nationwide, population-based study (2014-2016) with 4177 community-dwelling subjects, fecal samples from 656 subjects were collected after 1 (time point [T] = 1) and 6 (T = 2) months. The growth of ESBL-E was quantified and a whole-genome sequence analysis was performed. Subjects were categorized as either an incidental, short-term, or long-term carrier or as a noncarrier. Risk factors were determined by random forest models and logistic regression. The transmissibility and duration of ESBL-E carriage was quantified using a transmission model, which also incorporated previous study data. RESULTS: Out of 656 participants, 96 were ESBL-E carriers at T = 0. Of these, 66 (10.1%) subjects were incidental carriers, 22 (3.3%) were short-term carriers, and 38 (5.8%) were long-term carriers; the remaining 530 (80.8%) were noncarriers. The risk factors for long-term carriage were travelling to Asia, swimming in a sea/ocean, and not changing the kitchen towel daily. The log-transformed colony forming units ratio at T = 0 was predictive for ESBL-E carriage at T = 1 (odds ratio [OR], 1.3; 95% confidence interval [CI], 1.2-1.6) and T = 2 (OR, 1.2; 95% CI, 1.1-1.4). Model simulations revealed a median decolonization rate of 2.83/year, an average duration of carriage of 0.35 years, and an acquisition rate of 0.34/year. The trend of the acquisition rate during the study period was close to 0. CONCLUSIONS: The risk factors for long-term ESBL-E carriage were travel- and hygiene-related. The dynamics of ESBL-E carriage in the general Dutch population are characterized by balancing decolonization and acquisition rates.

Tracking Pseudomonas aeruginosa transmissions due to environmental contamination after discharge in ICUs using mathematical models.

Pseudomonas aeruginosa (P. aeruginosa) is an important cause of healthcare-associated infections, particularly in immunocompromised patients. Understanding how this multi-drug resistant pathogen is transmitted within intensive care units (ICUs) is crucial for devising and evaluating successful control strategies. While it is known that moist environments serve as natural reservoirs for P. aeruginosa, there is little quantitative evidence regarding the contribution of environmental contamination to its transmission within ICUs. Previous studies on other nosocomial pathogens rely on deploying specific values for environmental parameters derived from costly and laborious genotyping. Using solely longitudinal surveillance data, we estimated the relative importance of P. aeruginosa transmission routes by exploiting the fact that different routes cause different pattern of fluctuations in the prevalence. We developed a mathematical model including background transmission, cross-transmission and environmental contamination. Patients contribute to a pool of pathogens by shedding bacteria to the environment. Natural decay and cleaning of the environment lead to a reduction of that pool. By assigning the bacterial load shed during an ICU stay to cross-transmission, we were able to disentangle environmental contamination during and after a patient's stay. Based on a data-augmented Markov Chain Monte Carlo method the relative importance of the considered acquisition routes is determined for two ICUs of the University hospital in Besançon (France). We used information about the admission and discharge days, screening days and screening results of the ICU patients. Both background and cross-transmission play a significant role in the transmission process in both ICUs. In contrast, only about 1% of the total transmissions were due to environmental contamination after discharge. Based on longitudinal surveillance data, we conclude that cleaning improvement of the environment after discharge might have only a limited impact regarding the prevention of P.A. infections in the two considered ICUs of the University hospital in Besançon. Our model was developed for P. aeruginosa but can be easily applied to other pathogens as well.

Attributable sources of community-acquired carriage of Escherichia coli containing ß-lactam antibiotic resistance genes: a population-based modelling study.

BACKGROUND: Extended-spectrum ß-lactamase-producing Escherichia coli (ESBL-EC), plasmid-mediated AmpC-producing E coli (pAmpC-EC), and other bacteria are resistant to important ß-lactam antibiotics. ESBL-EC and pAmpC-EC are increasingly reported in animals, food, the environment, and community-acquired and health-care-associated human infections. These infections are usually preceded by asymptomatic carriage, for which attributions to animal, food, environmental, and human sources remain unquantified. METHODS: In this population-based modelling study, we collected ESBL and pAmpC gene data on the Netherlands population for 2005-17 from published datasets of gene occurrences in E coli isolates from different sources, and from partners of the ESBL Attribution Consortium and the Dutch National Antimicrobial Surveillance System. Using these data, we applied an established source attribution model based on ESBL-EC and pAmpC-EC prevalence and gene data for humans, including high-risk populations (ie, returning travellers, clinical patients, farmers), farm and companion animals, food, surface freshwater, and wild birds, and human exposure data, to quantify the overall and gene-specific attributable sources of community-acquired ESBL-EC and pAmpC-EC intestinal carriage. We also used a simple transmission model to determine the basic reproduction number (R0) in the open community. FINDINGS: We identified 1220 occurrences of ESBL-EC and pAmpC-EC genes in humans, of which 478 were in clinical patients, 454 were from asymptomatic carriers in the open community, 103 were in poultry and pig farmers, and 185 were in people who had travelled out of the region. We also identified 6275 occurrences in non-human sources, including 479 in companion animals, 4026 in farm animals, 66 in wild birds, 1430 from food products, and 274 from surface freshwater. Most community-acquired ESBL-EC and pAmpC-EC carriage was attributed to human-to-human transmission within or between households in the open community (60·1%, 95% credible interval 40·0-73·5), and to secondary transmission from high-risk groups (6·9%, 4·1-9·2). Food accounted for 18·9% (7·0-38·3) of carriage, companion animals for 7·9% (1·4-19·9), farm animals (non-occupational contact) for 3·6% (0·6-9·9), and swimming in freshwater and wild birds (ie, environmental contact) for 2·6% (0·2-8·7). We derived an R0 of 0·63 (95% CI 0·42-0·77) for intracommunity transmission. INTERPRETATION: Although humans are the main source of community-acquired ESBL-EC and pAmpC-EC carriage, the attributable non-human sources underpin the need for longitudinal studies and continuous monitoring, because intracommunity ESBL-EC and pAmpC-EC spread alone is unlikely to be self-maintaining without transmission to and from non-human sources. FUNDING: 1Health4Food, Dutch Ministry of Economic Affairs, and the EU's Horizon-2020 through One-Health European Joint Programme.