Mathematical modelling for antibiotic resistance control policy: do we know enough?

Transmission of drug-resistant bacteria in a hospital-community model stratified by patient risk

A susceptible-infectious-susceptible (SIS) model for simulating healthcare-acquired infection spread within a hospital and associated community is proposed. The model accounts for the stratification of in-patients into two susceptibility-based risk groups. The model is formulated as a system of first-order ordinary differential equations (ODEs) with appropriate initial conditions. The mathematical analysis of this system is demonstrated. It is shown that the system has unique global solutions, which are bounded and non-negative. The basic reproduction number ([Formula: see text]) for the considered model is derived. The existence and the stability of the stationary solutions are analysed. The disease-free stationary solution is always present and is globally asymptotically stable for [Formula: see text], while for [Formula: see text] it is unstable. The presence of an endemic stationary solution depends on the model parameters and when it exists, it is globally asymptotically stable. The endemic state encompasses both risk groups. The endemic state within only one group only is not possible. In addition, for [Formula: see text] a forward bifurcation takes place. Numerical simulations, based on the anonymised insurance data, are also presented to illustrate theoretical results.

Challenges in Forecasting Antimicrobial Resistance

Antimicrobial resistance is a major threat to human health. Since the 2000s, computational tools for predicting infectious diseases have been greatly advanced; however, efforts to develop real-time forecasting models for antimicrobial-resistant organisms (AMROs) have been absent. In this perspective, we discuss the utility of AMRO forecasting at different scales, highlight the challenges in this field, and suggest future research priorities. We also discuss challenges in scientific understanding, access to high-quality data, model calibration, and implementation and evaluation of forecasting models. We further highlight the need to initiate research on AMRO forecasting using currently available data and resources to galvanize the research community and address initial practical questions.

The potential of genomics for infectious disease forecasting

Genomic technologies have led to tremendous gains in understanding how pathogens function, evolve and interact. Pathogen diversity is now measurable at high precision and resolution, in part because over the past decade, sequencing technologies have increased in speed and capacity, at decreased cost. Alongside this, the use of models that can forecast emergence and size of infectious disease outbreaks has risen, highlighted by the coronavirus disease 2019 pandemic but also due to modelling advances that allow for rapid estimates in emerging outbreaks to inform monitoring, coordination and resource deployment. However, genomics studies have remained largely retrospective. While they contain high-resolution views of pathogen diversification and evolution in the context of selection, they are often not aligned with designing interventions. This is a missed opportunity because pathogen diversification is at the core of the most pressing infectious public health challenges, and interventions need to take the mechanisms of virulence and understanding of pathogen diversification into account. In this Perspective, we assess these converging fields, discuss current challenges facing both surveillance specialists and modellers who want to harness genomic data, and propose next steps for integrating longitudinally sampled genomic data with statistical learning and interpretable modelling to make reliable predictions into the future.

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.

Impact of the WHO Integrated Stewardship Policy on the Control of Methicillin-Resistant Staphyloccus aureus and Third-Generation Cephalosporin-Resistant Escherichia coli: Using a Mathematical Modeling Approach

Methicillin-resistant Staphylococcus aureus (MRSA) and third-generation cephalosporin-resistant Escherichia coli (3GCREc) are community and hospital-associated pathogens causing serious infections among populations by infiltrating into hospitals and surrounding environment. These main multi-drug resistant or antimicrobial resistance (AMR) bacterial pathogens are threats to human health if not properly tackled and controlled. Tackling antimicrobial resistance (AMR) is one of the issues for the World Health Organization (WHO) to design a comprehensive set of interventions which also helps to achieve the end results of the developing indicators proposed by the same organization. A deterministic mathematical model is developed and studied to investigate the impact of the WHO policy on integrated antimicrobial stewardship activities to use effective protection measures to control the spread of AMR diseases such as MRSA and 3GCREc in hospital settings by incorporating the contribution of the healthcare workers in a hospital and the environment in the transmission dynamics of the diseases. The model also takes into account the parameters describing various intervention measures and is used to quantify their contribution in containing the diseases. The impact of combinations of various possible control measures on the overall dynamics of the disease under study is investigated. The model analysis suggests that the contribution of the interventions: screening and isolating the newly admitted patients, improving the hygiene in hospital settings, decolonizing the pathogen carriers, and increasing the frequency of disinfecting the hospital environment are effective tools to contain the disease from invading the population. The study revealed that without any intervention, the diseases will continue to be a major cause of morbidity and mortality in the affected communities. In addition, the study indicates that a coordinated implementation of the integrated control measures suggested by WHO is more effective in curtailing the spread of the diseases than piecemeal strategies. Numerical experiments are provided to support the theoretical analysis.

Characterization of symbiotic and nitrogen fixing bacteria

Symbiotic nitrogen fixing bacteria comprise of diverse species associated with the root nodules of leguminous plants. Using an appropriate taxonomic method to confirm the identity of superior and elite strains to fix nitrogen in legume crops can improve sustainable global food and nutrition security. The current review describes taxonomic methods preferred and commonly used to characterize symbiotic bacteria in the rhizosphere. Peer reviewed, published and unpublished articles on techniques used for detection, classification and identification of symbiotic bacteria were evaluated by exploring their advantages and limitations. The findings showed that phenotypic and cultural techniques are still affordable and remain the primary basis of species classification despite their challenges. Development of new, robust and informative taxonomic techniques has really improved characterization and identification of symbiotic bacteria and discovery of novel and new species that are effective in biological nitrogen fixation (BNF) in diverse conditions and environments.

Uses of mathematical modeling to estimate the impact of mass drug administration of antibiotics on antimicrobial resistance within and between communities

Background

Antibiotics are a key part of modern healthcare, but their use has downsides, including selecting for antibiotic resistance, both in the individuals treated with antibiotics and in the community at large. When evaluating the benefits and costs of mass administration of azithromycin to reduce childhood mortality, effects of antibiotic use on antibiotic resistance are important but difficult to measure, especially when evaluating resistance that “spills over” from antibiotic-treated individuals to other members of their community. The aim of this scoping review was to identify how the existing literature on antibiotic resistance modeling could be better leveraged to understand the effect of mass drug administration (MDA) on antibiotic resistance.

Main text

Mathematical models of antibiotic use and resistance may be useful for estimating the expected effects of different MDA implementations on different populations, as well as aiding interpretation of existing data and guiding future experimental design. Here, strengths and limitations of models of antibiotic resistance are reviewed, and possible applications of those models in the context of mass drug administration with azithromycin are discussed.

Conclusions

Statistical models of antibiotic use and resistance may provide robust and relevant estimates of the possible effects of MDA on resistance. Mechanistic models of resistance, while able to more precisely estimate the effects of different implementations of MDA on resistance, may require more data from MDA trials to be accurately parameterized.

Graphical Abstract

Prevention of antimicrobial prescribing among infants following maternal vaccination against respiratory syncytial virus

Strategies to reduce consumption of antimicrobial drugs are needed to contain the growing burden of antimicrobial resistance. Respiratory syncytial virus (RSV) is a prominent cause of upper and lower respiratory tract infections, as a single agent and in conjunction with bacterial pathogens, and may thus contribute to the burden of both inappropriately treated viral infections and appropriately treated polymicrobial infections involving bacteria. In a double-blind, randomized, placebo-controlled trial, administering an RSV vaccine to pregnant mothers reduced antimicrobial prescribing among their infants by 12.9% over the first 3 mo of life. Our findings implicate RSV as an important contributor to antimicrobial exposure among infants and demonstrate that this exposure is preventable by use of effective maternal vaccines against RSV.

Reductions in antimicrobial consumption are needed to mitigate the burden of antimicrobial resistance. Vaccines may have an important role to play in reducing antimicrobial consumption by preventing infections for which treatment is often prescribed, whether appropriately or inappropriately. However, limited understanding of the volume of antimicrobial treatment attributable to specific pathogens—and to viruses, in particular—presently hinders efforts to prioritize vaccines with the greatest potential to reduce antimicrobial consumption. In a double-blind trial undertaken across 11 countries, infants born to mothers who were randomized to receive an experimental vaccine against respiratory syncytial virus (RSV) experienced 12.9% (95% CI: 1.3 to 23.1%) lower incidence of antimicrobial prescribing over the first 3 mo of life than infants whose mothers were randomized to receive placebo. Vaccine efficacy against antimicrobial prescriptions associated with acute lower respiratory tract infections (LRTIs) was 16.9% (95% CI: 1.4 to 29.4%). Over the first 3 mo of life, maternal vaccination prevented 3.6 antimicrobial prescription courses for every 100 infants born in high-income countries and 5.1 courses per 100 infants in low- and middle-income countries, representing 20.2 and 10.9% of all antimicrobial prescribing in these settings, respectively. While LRTI episodes accounted for 69 to 73% of all antimicrobial prescribing prevented by maternal vaccination, striking vaccine efficacy (71.3% [95% CI: 28.1 to 88.6%]) was also observed against acute otitis media–associated antimicrobial prescription among infants in high-income countries. Our findings implicate RSV as a cause of substantial volumes of antimicrobial prescribing among young infants and demonstrate the potential for prevention of such prescribing through use of maternal vaccines against RSV.

From Pathophysiological Hypotheses to Case–Control Study Design: Resistance from Antibiotic Exposure in Community-Onset Infections

Antimicrobial resistance is a global public health concern, at least partly due to the misuse of antibiotics. The increasing prevalence of antibiotic-resistant infections in the community has shifted at-risk populations into the general population. Numerous case–control studies attempt to better understand the link between antibiotic use and antibiotic-resistant community-onset infections. We review the designs of such studies, focusing on community-onset bloodstream and urinary tract infections. We highlight their methodological heterogeneity in the key points related to the antibiotic exposure, the population and design. We show the impact of this heterogeneity on study results, through the example of extended-spectrum β-lactamases producing Enterobacteriaceae. Finally, we emphasize the need for the greater standardization of such studies and discuss how the definition of a pathophysiological hypothesis specific to the bacteria–resistance pair studied is an important prerequisite to clarify the design of future studies.

Global research activity on mathematical modeling of transmission and control of 23 selected infectious disease outbreak

BACKGROUND

Mathematical analysis and modeling allow policymakers to understand and predict the dynamics of an infectious disease under several different scenarios. The current study aimed to analyze global research activity on mathematical modeling of transmission and control of several infectious diseases with a known history of serious outbreaks.

METHODS

Relevant publications were retrieved using a comprehensive validated search query. The database used was SciVerse Scopus. Indicators related to evolution, growth of publications, infectious diseases encountered, key players, citations, and international research collaboration were presented.

RESULTS

The search strategy found 5606. The growth of publications started in 1967 and showed a sharp rise in 2020 and 2021. The retrieved articles received relatively high citations (h-index = 158). Despite being multidisciplinary, Plos One journal made the highest contribution to the field. The main findings of the study are summarized as follows: (a) COVID-19 had a strong impact on the number of publications in the field, specifically during the years 2020 and 2021; (b) research in the field was published in a wide range of journals, mainly those in the field of infectious diseases and mathematical sciences; (c) research in the field was mainly published by scholars in the United States and the United Kingdom; (d) international research collaboration between active countries and less developed countries was poor; (e) research activity relied on research groups with a large number of researchers per group indicative of good author-author collaboration; (f) HIV/AIDS, coronavirus disease, influenza, and malaria were the most frequently researched diseases; (g) recently published articles on COVID-19 received the highest number of citations; and (h) researchers in the Eastern Mediterranian and South-East Asian regions made the least contribution to the retrieved articles.

CONCLUSION

Mathematical modeling is gaining popularity as a tool for understanding the dynamics of infectious diseases. The application of mathematical modeling on new emerging infectious disease outbreaks is a priority. Research collaboration with less developed countries in the field of mathematical epidemiology is needed and should be prioritized and funded.

Evaluation of the Impact of Antimicrobial Use Protocols in Porcine Reproductive and Respiratory Syndrome Virus-Infected Swine on Phenotypic Antimicrobial Resistance Patterns

A longitudinal study was conducted to assess the impact of different antimicrobial exposures of nursery-phase pigs on patterns of phenotypic antimicrobial resistance (AMR) in fecal indicator organisms throughout the growing phase. Based on practical approaches used to treat moderate to severe porcine reproductive and respiratory syndrome virus (PRRSV)-associated secondary bacterial infections, two antimicrobial protocols of differing intensities of exposure [44.1 and 181.5 animal-treatment days per 1000 animal days at risk (ATD)] were compared with a control group with minimal antimicrobial exposure (2.1 ATD). Litter-matched pigs (n = 108) with no prior antimicrobial exposure were assigned randomly to the treatment groups. Pen fecal samples were collected nine times during the wean-to-finish period and cultured for Escherichia coli and Enterococcus spp. Antimicrobial-susceptibility testing was conducted using NARMS Gram-negative and Gram-positive antibiotic panels. Despite up to 65-fold difference in ATD, few and modest differences were observed between groups and over time. Resistance patterns at marketing overall remained similar to those observed at weaning, prior to any antimicrobial exposures. Those differences observed could not readily be reconciled with the patterns of antimicrobial exposure. Resistance of E. coli to streptomycin was higher in the group exposed to 44.1 ATD, but no aminoglycosides were used. In all instances where resistances differed between time points, the higher resistance occurred early in the trial prior to any antimicrobial exposures. These minimal impacts on AMR despite substantially different antimicrobial exposures point to the lack of understanding of the drivers of AMR at the population level and the likely importance of factors other than antimicrobial exposure. IMPORTANCE Despite a recognized need for more longitudinal studies to assess the effects of antimicrobial use on resistance in food animals, they remain sparse in the literature, and most longitudinal studies of pigs have been observational. The current experimental study had the advantages of greater control of potential confounding, precise measurement of antimicrobial exposures which differed markedly between groups and tracking of pigs until market age. Overall, resistance patterns were remarkably stable between the treatment groups over time, and the differences observed could not be readily reconciled with the antimicrobial exposures, indicating the likely importance of other determinants of AMR at the population level.

What Is the Role of the Environment in the Emergence of Novel Antibiotic Resistance Genes? A Modeling Approach

It is generally accepted that intervention strategies to curb antibiotic resistance cannot solely focus on human and veterinary medicine but must also consider environmental settings. While the environment clearly has a role in transmission of resistant bacteria, its role in the emergence of novel antibiotic resistance genes (ARGs) is less clear. It has been suggested that the environment constitutes an enormous recruitment ground for ARGs to pathogens, but its extent is practically unknown. We have constructed a model framework for resistance emergence and used available quantitative data on relevant processes to identify limiting steps in the appearance of ARGs in human pathogens. We found that in a majority of possible scenarios, the environment would only play a minor role in the emergence of novel ARGs. However, the uncertainty is enormous, highlighting an urgent need for more quantitative data. Specifically, more data is most needed on the fitness costs of ARG carriage, the degree of dispersal of resistant bacteria from the environment to humans, and the rates of mobilization and horizontal transfer of ARGs. This type of data is instrumental to determine which processes should be targeted for interventions to curb development and transmission of ARGs in the environment.

Trends, relationships and case attribution of antibiotic resistance between children and environmental sources in rural India

Bacterial antibiotic resistance is an important global health threat and the interfaces of antibiotic resistance between humans, animals and the environment are complex. We aimed to determine the associations and overtime trends of antibiotic resistance between humans, animals and water sources from the same area and time and estimate attribution of the other sources to cases of human antibiotic resistance. A total of 125 children (aged 1-3 years old) had stool samples analysed for antibiotic-resistant bacteria at seven time points over two years, with simultaneous collection of samples of animal stools and water sources in a rural Indian community. Newey-West regression models were used to calculate temporal associations, the source with the most statistically significant relationships was household drinking water. This is supported by use of SourceR attribution modelling, that estimated the mean attribution of cases of antibiotic resistance in the children from animals, household drinking water and wastewater, at each time point and location, to be 12.6% (95% CI 4.4-20.9%), 12.1% (CI 3.4-20.7%) and 10.3% (CI 3.2-17.3%) respectively. This underlines the importance of the 'one health' concept and requires further research. Also, most of the significant trends over time were negative, suggesting a possible generalised improvement locally.

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.

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

Antibiotic-Resistant Genes and Bacteria as Evolving Contaminants of Emerging Concerns (e-CEC): Is It Time to Include Evolution in Risk Assessment?

The pressing issue of the abundance of antibiotic resistance genes and resistant bacteria in the environment (ARGs and ARB, respectively) requires procedures for assessing the risk to health. The chemo-centric environmental risk assessment models identify hazard(s) in a dose–response manner, obtaining exposure, toxicity, risk, impact and policy. However, this risk assessment approach based on ARGs/ARB evaluation from a quantitative viewpoint shows high unpredictability because ARGs/ARB cannot be considered as standard hazardous molecules: ARB duplicate and ARGs evolve within a biological host. ARGs/ARB are currently listed as Contaminants of Emerging Concern (CEC). In light of such characteristics, we propose to define ARGs/ARB within a new category of evolving CEC (or e-CEC). ARGs/ARB, like any other evolving determinants (e.g., viruses, bacteria, genes), escape environmental controls. When they do so, just one molecule left remaining at a control point can form the origin of a new dangerous and selection-responsive population. As a consequence, perhaps it is time to acknowledge this trait and to include evolutionary concepts within modern risk assessment of e-CEC. In this perspective we analyze the evolutionary responses most likely to influence risk assessment, and we speculate on the means by which current methods could measure evolution. Further work is required to implement and exploit such experimental procedures in future risk assessment protocols.

Beyond Antimicrobial Use: A Framework for Prioritizing Antimicrobial Resistance Interventions

Antimicrobial resistance (AMR) is a threat to animal and human health. Antimicrobial use has been identified as a major driver of AMR, and reductions in use are a focal point of interventions to reduce resistance. Accordingly, stakeholders in human health and livestock production have implemented antimicrobial stewardship programs aimed at reducing use. Thus far, these efforts have yielded variable impacts on AMR. Furthermore, scientific advances are prompting an expansion and more nuanced appreciation of the many nonantibiotic factors that drive AMR, as well as how these factors vary across systems, geographies, and contexts. Given these trends, we propose a framework to prioritize AMR interventions. We use this framework to evaluate the impact of interventions that focus on antimicrobial use. We conclude by suggesting that priorities be expanded to include greater consideration of host-microbial interactions that dictate AMR, as well as anthropogenic and environmental systems that promote dissemination of AMR.

Monitoring of distribution of antibiotic-resistant strains of microorganisms in patients with dysbiosis of the urogenital tract

Currently, the problem of the development of resistance to drugs among microorganisms that colonize the urogenital system is becoming especially relevant due to broadly distributed dysbiotic conditions of the reproductive system of men and women. Therefore, there should be constant monitoring of the qualitative and quantitative composition of microbiota of the urogential tract and determination of the levels of antibiotic-resistance of strains of conditionally pathogenic microorganisms in the reproductive system of various layers of the population. We monitored 774,375 people of various age and sex – patients of the independent diagnostic laboratory INVITRO in the city Dnipro in 2017–2019. Among the examined people, 640,783 of the patients were diagnosed with the development of dysbiotic disorders, accounting for 82.7% of the total amount of the applications for medical help. According to the results of identification of the range of dysbiotic conditions of the urogenital system of patients of different ages and sexes, we determined the dominating role of facultative anaerobes in the development of dysbiotic impairments caused by colonizations by large numbers of conditionally-pathogenic microorganisms: in women, Gardnerella accounted for 86.1%, Staphylococcus – 63.2%, Streptococcus – 54.1%, Candida – 69.3%; in men, Streptococcus were found in 83.0%, Staphylococcus – 79.4%, Corynebacterium – 54.2% and Candida – 37.6% of the cases. Share of obligate anaerobes was also quite large: women were diagnosed with Prevotella in 59.7%, Peptostreptococcus in 53.2%, Fusobacterium in 45.4% of the cases cases; men were observed to have Peptostreptococcus 62.4%, Clostridium in 54.3%, Bacteroides in 32.5% of the cases. We determined high parameters of frequency of diagnosing antibiotic-resistant isolates of conditionally pathogenic microorganisms that circulate in the urogenital tract of patients with dysbiotic impairments, belonging to the following families: Mycoplasmataceae – 78.6%, Enterobacteriaceae – 56.0% and genera – Staphylococcus – 76.1%, Gardnerella – 24.3%, Corynebacterium – 21.2%. The research revealed increase in the frequency of detection of strains of urapathogenic bacteria resistant to the applied antibiotic preparations in 2018–2019 compared with the data of 2017: increases of 10.3% and 6.4% in representatives of family Mycoplasmataceae resistant to ciprofloxacin and ofloxacin respectively, 4.8% and 4.0% in Enterobacteriaceae resistant to chloramphenicol and ampicillin respectively, and 8.9% in the genus Staphylococcus resistant to vancomycin.

A MODEL ON BACTERIAL RESISTANCE CONSIDERING A GENERALIZED LAW OF MASS ACTION FOR PLASMID REPLICATION

Bacterial plasmids play a fundamental role in antibiotic resistance. However, a lack of knowledge about their biology is an obstacle in fully understanding the mechanisms and properties of plasmid-mediated resistance. This has motivated investigations of real systems in vitro to analyze the transfer and replication of plasmids. In this work, we address this issue with mathematical modeling. We formulate and perform a qualitative analysis of a nonlinear system of ordinary differential equations describing the competition dynamics between plasmids and sensitive and resistant bacteria. In addition, we estimated parameter values from empirical data. Our model predicts scenarios consistent with biological phenomena. The elimination or spread of infection depends on factors associated with bacterial reproduction and the transfer and replication of plasmids. From the estimated parameters, three bacterial growth experiments were analyzed in vitro. We determined the experiment with the highest bacterial growth rate and the highest rate of plasmid transfer. Moreover, numerical simulations were performed to predict bacterial growth.

Drivers of ESBL-producing Escherichia coli dynamics in calf fattening farms: A modelling study

The contribution of bacteria in livestock to the global burden of antimicrobial resistance raises concerns worldwide. However, the dynamics of selection and diffusion of antimicrobial resistance in farm animals are not fully understood. Here, we used veal calf fattening farms as a model system, as they are a known reservoir of Extended Spectrum β-Lactamase-producing Escherichia coli (ESBL-EC). Longitudinal data of ESBL-EC carriage and antimicrobial use (AMU) were collected from three veal calf farms during the entire fattening process. We developed 18 agent-based mechanistic models to assess different hypotheses regarding the main drivers of ESBL-EC dynamics in calves. The models were independently fitted to the longitudinal data using Markov Chain Monte Carlo and the best model was selected. Within-farm transmission between individuals and sporadic events of contamination were found to drive ESBL-EC dynamics on farms. In the absence of AMU, the median carriage duration of ESBL-EC was estimated to be 19.6 days (95% credible interval: [12.7; 33.3]). In the best model, AMU was found to influence ESBL-EC dynamics, by affecting ESBL-EC clearance rather than acquisition. This effect of AMU was estimated to decrease gradually after the end of exposure and to disappear after 62.5 days [50.0; 76.9]. Moreover, using a simulation study, we quantified the efficacy of ESBL-EC mitigation strategies. Decreasing ESBL-EC prevalence by 50% on arrival at the fattening farm reduced prevalence at slaughter age by 33.3%. Completely eliminating the use of selective antibiotics on arrival had a strong effect on average ESBL-EC prevalence (relative reduction of 77.0%), but the effect was mild if this use was only decreased by 50% compared to baseline (relative reduction of 3.3%).

To give or not to give antibiotics is not the only question

In a 1945 Nobel Lecture, Sir Alexander Fleming warned against the overuse of antibiotics, particularly in response to public pressure. In the subsequent decades, evidence has shown that bacteria can become resistant to almost any available molecule. One key question is how the emergence and dissemination of resistant bacteria or resistance genes can be delayed. Although some clinicians remain sceptical, in this Personal View, we argue that the prescription of fewer antibiotics and shorter treatment duration is just as effective as longer regimens that remain the current guideline. Additionally, we discuss the fact that shorter antibiotic treatments exert less selective pressure on microorganisms, preventing the development of resistance. By contrast, longer treatments associated with a strong selective pressure favour the emergence of resistant clones within commensal organisms. We also emphasise that more studies are needed to identify the optimal duration of antibiotic therapy for common infections, which is important for making changes to the current guidelines, and to identify clinical biomarkers to guide antibiotic treatment in both hospital and ambulatory settings.

Antimicrobial use in wean to market pigs in the United States assessed via voluntary sharing of proprietary data

Data on antimicrobial use were collected for the 2016 and 2017 calendar years from swine producers in the United States. Nine large systems, collectively producing over 20 million market pigs annually, voluntarily provided data to advance understanding of antimicrobial use in the industry and to support antimicrobial stewardship initiatives. The scope of the study was limited to growing pigs, and the granularity of data varied across the systems. Data were summarized both qualitatively and quantitatively by antimicrobial class, active ingredient and route of administration (injection, water and feed). Data on the purpose of administration, doses and durations of administration were not available, but some information was provided by the responsible veterinarians. Aggregate data were similar both qualitatively and quantitatively in 2016 and 2017, although marked changes between years were evident within systems for some antimicrobials. Antimicrobial use (by weight) was dominated by the tetracycline class (approximately 60% of total use). Antimicrobials in classes categorized as critically important constituted 4.5% and 5.3% of total use in 2016 and 2017, respectively. In both years, fluoroquinolone (0.23%, 0.46%) and 3rd generation cephalosporin (0.15%, 0.11%) use collectively accounted for <1% of total use. Administration was predominantly oral in feed and water, and injection comprised approximately 2% of use overall, but around 12% for critically important antimicrobials. There was considerable variability among systems in patterns of antimicrobial use. This pilot project demonstrates the feasibility of acquiring antimicrobial use data via voluntary sharing. It is currently being expanded among larger swine production systems, and further efforts to enable confidential data sharing and benchmarking for smaller producers are being pursued by the swine industry. Recognized biases in the data caution against over-interpretation of these data as an index of national use.

Antibiotic Resistance in Recreational Waters: State of the Science

Ambient recreational waters can act as both recipients and natural reservoirs for antimicrobial resistant (AMR) bacteria and antimicrobial resistant genes (ARGs), where they may persist and replicate. Contact with AMR bacteria and ARGs potentially puts recreators at risk, which can thus decrease their ability to fight infections. A variety of point and nonpoint sources, including contaminated wastewater effluents, runoff from animal feeding operations, and sewer overflow events, can contribute to environmental loading of AMR bacteria and ARGs. The overall goal of this article is to provide the state of the science related to recreational exposure and AMR, which has been an area of increasing interest. Specific objectives of the review include (1) a description of potential sources of antibiotics, AMR bacteria, and ARGs in recreational waters, as documented in the available literature; (2) a discussion of what is known about human recreational exposures to AMR bacteria and ARGs, using findings from health studies and exposure assessments; and (3) identification of knowledge gaps and future research needs. To better understand the dynamics related to AMR and associated recreational water risks, future research should focus on source contribution, fate and transport-across treatment and in the environment; human health risk assessment; and standardized methods.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Antimicrobial stewardship programs; a two-part narrative review of step-wise design and issues of controversy Part I: step-wise design of an antimicrobial stewardship program

Regardless of one's opinion of antimicrobial stewardship programs (ASPs), it is hardly possible to work in hospital care and not be exposed to the term or its practical effects. Despite the term being relatively new, the number of publications in the field is vast, including several excellent reviews of general and specific aspects. Work in antimicrobial stewardship is complex, and includes not only aspects of infectious disease and microbiology, but also of epidemiology, genetics, behavioural psychology, systems science, economics and ethics, to name a few. This review aims to take several of these aspects and the scientific evidence of antimicrobial stewardship studies and merge them into two questions: How should we design ASPs based on what we know today? And which are the most essential unanswered questions regarding antimicrobial stewardship on a broader scale? This narrative review is written in two separate parts aiming to provide answers to the two questions. This first part is written as a step-wise approach to designing a stewardship intervention based on the pillars of unmet need, feasibility, scientific evidence and necessary core elements. It is written mainly as a guide to someone new to the field. It is sorted into five distinct steps: (a) focusing on designing aims; (b) assessing performance and local barriers to rational antimicrobial use; (c) deciding on intervention technique; (d) practical, tailored design including core element inclusion; and (e) evaluation and sustainability. The second part, published separately, formulates ten critical questions on controversies in the field of antimicrobial stewardship. It is aimed at clinicians and researchers with stewardship experience and strives to promote discussion, not to provide answers.