Send more data: a systematic review of mathematical models of antimicrobial resistance

Invariant set theory for predicting potential failure of antibiotic cycling

Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies. Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections.

The neglected model validation of antimicrobial resistance transmission models - a systematic review

BACKGROUND

In the fight against antimicrobial resistance, mathematical transmission models have been shown as a valuable tool to guide intervention strategies in public health.

OBJECTIVE

This review investigates the persistence of modelling gaps identified in earlier studies. It expands the scope to include a broader range of control measures, such as monoclonal antibodies, and examines the impact of secondary infections.

METHODS

This review was conducted according to the PRISMA guidelines. Gaps in model focus areas, dynamics, and reporting were identified and described. The TRACE paradigm was applied to selected models to discuss model development and documentation to guide future modelling efforts.

RESULTS

We identified 170 transmission studies from 2010 to May 2022; Mycobacterium tuberculosis (n = 39) and Staphylococcus aureus (n = 27) resistance transmission were most commonly modelled, focusing on multi-drug and methicillin resistance, respectively. Forty-one studies examined multiple interventions, predominantly drug therapy and vaccination, showing an increasing trend. Most studies were population-based compartmental models (n = 112). The TRACE framework was applied to 39 studies, showing a general lack of description of test and verification of modelling software and comparison of model outputs with external data.

CONCLUSION

Despite efforts to model antimicrobial resistance and prevention strategies, significant gaps in scope, geographical coverage, drug-pathogen combinations, and viral-bacterial dynamics persist, along with inadequate documentation, hindering model updates and consistent outcomes for policymakers. This review highlights the need for robust modelling practices to enable model refinement as new data becomes available. Particularly, new data for validating modelling outcomes should be a focal point in future modelling research.

Estimated undertreatment of carbapenem-resistant Gram-negative bacterial infections in eight low-income and middle-income countries: a modelling study

BACKGROUND

Carbapenem-resistant Gram-negative (CRGN) bacterial infections are an urgent health threat, especially in low-income and middle-income countries (LMICs), where they are rarely detected and might not be treated appropriately given inadequate health system capacity. To understand this treatment gap, we estimated the total number of CRGN bacterial infections requiring an active agent and the number of individuals potentially initiated on appropriate treatment in eight large LMICs.

METHODS

For eight selected countries (Bangladesh, Brazil, Egypt, India, Kenya, Mexico, Pakistan, and South Africa), we estimated deaths associated with CRGN bacterial infections (that were not susceptible to other antibiotics) in 2019 using data from the Global Burden of Disease 2021 study on antimicrobial resistance. We used estimates from the literature to establish infection type-specific case fatality rates and an overall case fatality rate for CRGN bacterial infections. The total number of CRGN bacterial infections requiring an active agent could then be calculated by dividing the total number of CRGN bacterial infection-related deaths by the overall case fatality rate. We estimated the treatment gap (ie, the number of individuals with CRGN bacterial infections who were not appropriately treated) by subtracting from the total number of infections the number of individuals who initiated appropriate treatment, which was estimated using 2019 IQVIA sales data for six antibiotics active against CRGN bacteria, corrected to account for IQVIA's partial data coverage for each country and dose-adjusted by age.

FINDINGS

In 2019, in the eight selected countries, we estimated that there were 1 496 219 CRGN bacterial infections (95% CI 1 365 392-1 627 047) but that only 103 647 treatment courses were procured. The resulting treatment gap (1 392 572 cases [95% CI 1 261 745-1 523 400]) meant that only 6·9% of patients were treated appropriately. The treatment gap persisted even when we used more restrictive assumptions. The most-procured antibiotic was tigecycline (intravenous; 47 531 [45·9%] of 103 647 courses). India procured most of the treatment courses (83 468 [80·5%] courses), with 7·8% of infections treated appropriately (treatment gap 982 848 cases [95% CI 909 291-1 056 405]). The rates of appropriate treatment coverage were highest in Mexico (5634 [5·4%] courses procured; treatment gap 32 141 cases [30 416-33 867]) and Egypt (7572 [7·3%] courses procured; treatment gap 43 258 cases [38 742-47 774]), both with 14·9% of infections treated appropriately.

INTERPRETATION

Infections caused by CRGN bacteria are likely to be significantly undertreated in LMICs. To close this treatment gap, improved access to diagnostics and antibiotics, strengthening of health systems, and research to identify gaps in the treatment pathway are needed.

FUNDING

Global Antibiotic Research and Development Partnership, supported by the Governments of Canada, Germany, Japan, Monaco, the Netherlands, Switzerland, and the UK, and by the Canton of Geneva, the EU, the Bill & Melinda Gates Foundation, Global Health EDCTP3, GSK, the RIGHT Foundation, the South African Medical Research Council, and Wellcome.

Cost-effectiveness of screening, decolonisation and isolation strategies for carbapenem-resistant Enterobacterales and methicillin-resistant Staphylococcus aureus infections in hospitals: a sex-stratified mathematical modelling study

Background

Methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacterales (CRE) impose the greatest burden among critical bacterial pathogens. Evidence for sex differences among antibiotic resistant bacterial infections is increasing but a focus on policy implications is needed. We assessed impact of CRE/MRSA on excess length of hospital stay, intensive care unit admission, and mortality by sex from a retrospective cohort study (n = 873) of patients in three Chilean hospitals, 2018-2021.

Methods

We used inverse-probability weighting combined with descriptive, logistic, and competing-risks analyses. We developed a sex-stratified deterministic compartmental model to analyse hospital transmission dynamics and the cost-effectiveness of nine interventions. We compared interventions based on the incremental cost-effectiveness ratio (ICER) per quality-adjusted life year (QALY) gained and estimated net benefits.

Findings

The adjusted odds of women acquiring CRE and MRSA were 0.44 (0.28-0.70; p = 0.0013) and 0.73 (95% CI = 0.48-1.01; p = 0.050), respectively. Competing-risk models indicated higher mortality rates among women, compared to men. Mathematical model projections showed that pre-emptive isolation across all newly admitted high-risk men was the most cost-effective intervention (ICER = $1366/QALY and $1083/QALY for CRE and MRSA, respectively). Chromogenic agar coupled with MRSA decolonisation was the second most cost-effective intervention ($2099/QALY), followed by screening plus isolation or pre-emptive isolation strategies (ICER ranged between $2411/QALY and $4216/QALY across CRE and MRSA models). Probabilistic sensitivity analysis showed that strategies were ICER < willingness-to-pay in 80% of simulations, except for testing plus digestive decolonisation for CRE. At a 20% national hospital coverage at least $12.2 million could be saved.

Interpretation

Our model suggests that targeted infection control strategies would effectively address rising CRE and MRSA infections. Maximising health-economic gains may be achieved by focusing on control measures for men as primary drivers for transmission, thereby reducing the disproportionate disease burden borne by women.

Funding

Agencia Nacional de Investigación y Desarrollo ANID, Chile.

Antimicrobial risk assessment-Aggregating aquatic chemical and resistome emissions

Urban water systems receive and emit antimicrobial chemicals, resistant bacterial strains, and resistance genes (ARGs), thus representing "antimicrobial hotspots". Currently, regional environmental risk assessment (ERA) is carried out using drug consumption data and threshold concentrations derived based on chemical-specific minimum inhibitory concentration values. A legislative proposal by the European Commission released in 2022 addresses the need to include selected ARGs besides the chemical concentration-based ERAs. The questions arise as to (A) how to improve chemical concentration-based risk assessment and (B) how to integrate resistome-related information with chemical-based risk - the main focal areas of this study. A tiered chemical risk prediction method is proposed by considering effluents of sewer networks and water resource recovery facilities (WRRFs). To improve predicted environmental concentrations (PEC in recipient water bodies), the impact of antimicrobial bio- and re-transformation in WRRFs is assessed using reliable global data. To combine chemical and genetic risks, a new parameter, i.e., the gene response efficiency (GRE) is proposed. A regression analysis show four orders of magnitude differences in GRE values amongst the seven antimicrobial classes studied. Higher GRE values in wastewater are obtained for antimicrobials with relatively low consumption rate levels.

A mechanistic approach to optimize combination antibiotic therapy

Antimicrobial resistance is one of the most significant healthcare challenges of our times. Multidrug or combination therapies are sometimes required to treat severe infections; for example, the current protocols to treat pulmonary tuberculosis combine several antibiotics. However, combination therapy is usually based on lengthy empirical trials, and it is difficult to predict its efficacy. We propose a new tool to identify antibiotic synergy or antagonism and optimize combination therapies. Our model explicitly incorporates the mechanisms of individual drug action and estimates their combined effect using a mechanistic approach. By quantifying the impact on growth and death of a bacterial population, we can identify optimal combinations of multiple drugs. Our approach also allows for the investigation of the drugs' actions and the testing of theoretical hypotheses. We demonstrate the utility of this tool with in vitro Escherichia coli data using a combination of ampicillin and ciprofloxacin. In contrast to previous interpretations, our model finds a slight synergy between the antibiotics. Our mechanistic model allows investigating possible causes of the synergy.

Contagious acquisition of antimicrobial resistance is critical for explaining emergence in western Canadian feedlots-insights from an agent-based modelling tool

Introduction

Antimicrobial resistance (AMR) is a growing threat to the efficacy of antimicrobials in humans and animals, including those used to control bovine respiratory disease (BRD) in high-risk calves entering western Canadian feedlots. Successful mitigation strategies require an improved understanding of the epidemiology of AMR. Specifically, the relative contributions of antimicrobial use (AMU) and contagious transmission to AMR emergence in animal populations are unknown.

Materials and methods

A stochastic, continuous-time agent-based model (ABM) was developed to explore the dynamics of population-level AMR in Mannheimia haemolytica in pens of high-risk cattle on a typical western Canadian feedlot. The model was directly informed and parameterized with proprietary data from partner veterinary practices and AMU/AMR surveillance data where possible. Hypotheses about how AMR emerges in the feedlot environment were represented by model configurations in which detectable AMR was impacted by (1) only selection arising from AMU; (2) only transmission between animals in the same pen; and (3) both AMU-linked selection and transmission. Automated calibration experiments were used to estimate unknown parameters of interest for select antimicrobial classes. Calibrated parameter values were used in a series of Monte Carlo experiments to generate simulated outputs at both the pen and feedlot levels. Key model outputs included the prevalence of AMR by class at multiple time points across the feeding period. This study compared the relative performances of these model configurations with respect to reproducing empirical AMR data.

Results

Across all antimicrobial classes of interest, model configurations which included the potential for contagious acquisition of AMR offered stronger fits to the empirical data. Notably, sensitivity analyses demonstrated that model outputs were more robust to changes in the assumptions underscoring AMU than to those affecting the likelihood of transmission.

Discussion

This study establishes a feedlot simulation tool that can be used to explore questions related to antimicrobial stewardship in the context of BRD management. The ABM stands out for its unique hierarchical depiction of AMR in a commercial feedlot and its grounding in robust epidemiological data. Future experiments will allow for both AMU-linked selection and transmission of AMR and can accommodate parameter modifications as required.

Application of MIDD to accelerate the development of anti-infectives: Current status and future perspectives

This review examines the role of model-informed drug development (MIDD) in advancing antibacterial and antiviral drug development, with an emphasis on the inclusion of host system dynamics into modeling efforts. Amidst the growing challenges of multidrug resistance and diminishing market returns, innovative methodologies are crucial for continuous drug discovery and development. The MIDD approach, with its robust capacity to integrate diverse data types, offers a promising solution. In particular, the utilization of appropriate modeling and simulation techniques for better characterization and early assessment of drug resistance are discussed. The evolution of MIDD practices across different infectious disease fields is also summarized, and compared to advancements achieved in oncology. Moving forward, the application of MIDD should expand into host system dynamics as these considerations are critical for the development of "live drugs" (e.g. chimeric antigen receptor T cells or bacteriophages) to address issues like antibiotic resistance or latent viral infections.

Within-Host Mathematical Models of Antibiotic Resistance

Mathematical models have been used to study the spread of infectious diseases from person to person. More recently studies are developing within-host modeling which provides an understanding of how pathogens-bacteria, fungi, parasites, or viruses-develop, spread, and evolve inside a single individual and their interaction with the host's immune system.Such models have the potential to provide a more detailed and complete description of the pathogenesis of diseases within-host and identify other influencing factors that may not be detected otherwise. Mathematical models can be used to aid understanding of the global antibiotic resistance (ABR) crisis and identify new ways of combating this threat.ABR occurs when bacteria respond to random or selective pressures and adapt to new environments through the acquisition of new genetic traits. This is usually through the acquisition of a piece of DNA from other bacteria, a process called horizontal gene transfer (HGT), the modification of a piece of DNA within a bacterium, or through. Bacteria have evolved mechanisms that enable them to respond to environmental threats by mutation, and horizontal gene transfer (HGT): conjugation; transduction; and transformation. A frequent mechanism of HGT responsible for spreading antibiotic resistance on the global scale is conjugation, as it allows the direct transfer of mobile genetic elements (MGEs). Although there are several MGEs, the most important MGEs which promote the development and rapid spread of antimicrobial resistance genes in bacterial populations are plasmids and transposons. Each of the resistance-spread-mechanisms mentioned above can be modeled allowing us to understand the process better and to define strategies to reduce resistance.

Innovations in genomic antimicrobial resistance surveillance

Whole-genome sequencing of antimicrobial-resistant pathogens is increasingly being used for antimicrobial resistance (AMR) surveillance, particularly in high-income countries. Innovations in genome sequencing and analysis technologies promise to revolutionise AMR surveillance and epidemiology; however, routine adoption of these technologies is challenging, particularly in low-income and middle-income countries. As part of a wider series of workshops and online consultations, a group of experts in AMR pathogen genomics and computational tool development conducted a situational analysis, identifying the following under-used innovations in genomic AMR surveillance: clinical metagenomics, environmental metagenomics, gene or plasmid tracking, and machine learning. The group recommended developing cost-effective use cases for each approach and mapping data outputs to clinical outcomes of interest to justify additional investment in capacity, training, and staff required to implement these technologies. Harmonisation and standardisation of methods, and the creation of equitable data sharing and governance frameworks, will facilitate successful implementation of these innovations.

An agent-based model for collaborative learning to combat antimicrobial resistance: proof of concept based on broiler production in Senegal

Antimicrobial resistance (AMR) is a substantial global One Health problem. This paper reports on initial, proof-of-concept development of an agent-based model (ABM) as part of wider modelling efforts to support collaborations between groups interested in policy development for animal health and food systems. The model simulates AMR in poultry production in Senegal. It simultaneously addresses current policy issues, builds on existing modelling in the domain and describes AMR in the broiler chicken production cycle as seen by producers and veterinarians. This enables implementation and assessment of producer antimicrobial use and infection prevention and control strategies in terms of immediate economic incentives, potentially helping to advance conversations by addressing national policy priorities. Our model is presented as a flexible tool with promise for extension as part of AMR policy development in Senegal and West Africa, using participatory approaches. This work indicates that ABM can potentially play a useful role in fostering counter-AMR initiatives driven by food system actor behaviour in lower- and middle-income countries more generally.

Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study

Infection control programs and antimicrobial stewardship have been proven effective in reducing the burden of diseases due to multidrug-resistant organisms, but quantifying the effect of each intervention is an open issue. For this aim, we propose a model to characterize the effect of interventions at single ward level. We adapted the Ross-Macdonald model to describe hospital cross-transmission dynamics of carbapenem resistant Klebsiella pneumoniae (CRKP), considering healthcare workers as the vectors transmitting susceptible and resistant pathogens among admitted patients. The model parameters were estimated from a literature review, further adjusted to reproduce observed clinical outcomes, and validated using real life data from a 2-year study in a university hospital. The model has been further explored through extensive sensitivity analysis, in order to assess the relevance of single interventions as well as their synergistic effects. Our model has been shown to be an effective tool to describe and predict the impact of interventions in reducing the prevalence of CRKP colonisation and infection, and can be extended to other specific hospital and pathological scenarios to produce tailored estimates of the most effective strategies.

Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research

BACKGROUND

Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context.

METHODS

This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR.

MAIN FINDINGS

Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants' statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context.

CONCLUSION

Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking.

Cost-effectiveness of testing for Mycoplasma genitalium among men who have sex with men in Australia

OBJECTIVES

Mycoplasma genitalium (MG) disproportionately affects men who have sex with men (MSM). We determined the cost-effectiveness of different testing strategies for MG in MSM, taking a healthcare provider perspective.

METHODS

We used inputs from a dynamic transmission model of MG among MSM living in Australia in a decision tree model to evaluate the impact of four testing scenarios on MG incidence: (1) no one tested; (2) symptomatic MSM; (3) symptomatic and high-risk asymptomatic MSM; (4) all MSM. We calculated the incremental cost-effectiveness ratios (ICERs) using a willingness-to-pay threshold of $A30 000 per quality-adjusted life year (QALY) gained. We explored the impact of adding an antimicrobial resistance (AMR) tax (ie, additional cost per antibiotic consumed) to identify the threshold, whereby any testing for MG is no longer cost-effective.

RESULTS

Testing only symptomatic MSM is the most cost-effective (ICER $3677 per QALY gained) approach. Offering testing to all MSM is dominated (ie, higher costs and lower QALYs gained compared with other strategies). When the AMR tax per antibiotic given was above $150, any testing for MG was no longer cost-effective.

CONCLUSION

Testing only symptomatic MSM is the most cost-effective option, even when the potential costs associated with AMR are accounted for (up to $150 additional cost per antibiotic given). For pathogens like MG, where there are anticipated future costs related to AMR, we recommend models that test the impact of incorporating an AMR tax as they can change the results and conclusions of cost-effectiveness studies.

Infectious Disease Modelling of HIV Prevention Interventions: A Systematic Review and Narrative Synthesis of Compartmental Models

BACKGROUND

The HIV epidemic remains a major public health problem. Critical to transmission control are HIV prevention strategies with new interventions continuing to be developed. Mathematical models are important for understanding the potential impact of these interventions and supporting policy decisions. This systematic review aims to answer the following question: when a new HIV prevention intervention is being considered or designed, what information regarding it is necessary to include in a compartmental model to provide useful insights to policy makers? The primary objective of this review is therefore to assess suitability of current compartmental HIV prevention models for informing policy development.

METHODS

Articles published in EMBASE, Medline, Econlit, and Global Health were screened. Included studies were identified using permutations of (i) HIV, (ii) pre-exposure prophylaxis (PrEP), circumcision (both voluntary male circumcision [VMMC] and early-infant male circumcision [EIMC]), and vaccination, and (iii) modelling. Data extraction focused on study design, model structure, and intervention incorporation into models. Article quality was assessed using the TRACE (TRAnsparent and Comprehensive Ecological modelling documentation) criteria for mathematical models.

RESULTS

Of 837 articles screened, 48 articles were included in the review, with 32 unique mathematical models identified. The substantial majority of studies included PrEP (83%), whilst fewer modelled circumcision (54%), and only a few focussed on vaccination (10%). Data evaluation, implementation verification, and model output corroboration were identified as areas of poorer model quality. Parameters commonly included in the mathematical models were intervention uptake and effectiveness, with additional intervention-specific common parameters identified. We identified key modelling gaps; critically, models insufficiently incorporate multiple interventions acting simultaneously. Additionally, population subgroups were generally poorly represented-with future models requiring improved incorporation of ethnicity and sexual risk group stratification-and many models contained inappropriate data in parameterisation which will affect output accuracy.

CONCLUSIONS

This review identified gaps in compartmental models to date and suggests areas of improvement for models focusing on new prevention interventions. Resolution of such gaps within future models will ensure greater robustness and transparency, and enable more accurate assessment of the impact that new interventions may have, thereby providing more meaningful guidance to policy makers.

Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review

INTRODUCTION

Antimicrobial resistance (AMR) is a complex problem that requires the One Health approach, that is, a collaboration among various disciplines working in different sectors (animal, human and environment) to resolve it. Mathematical and statistical models have been used to understand AMR development, emergence, dissemination, prediction and forecasting. A review of the published models of AMR will help consolidate our knowledge of the dynamics of AMR and will also facilitate decision-makers and researchers in evaluating the credibility, generalisability and interpretation of the results and aspects of AMR models. The study objective is to identify and synthesise knowledge on mathematical and statistical models of AMR among bacteria in animals, humans and environmental compartments.

METHODS AND ANALYSIS

Eligibility criteria: Original research studies reporting mathematical and statistical models of AMR among bacteria in animal, human and environmental compartments that were published until 2022 in English, French and Spanish will be included in this study.

SOURCES OF EVIDENCE

Database of PubMed, Agricola (Ovid), Centre for Agriculture and Bioscience Direct (CABI), Web of Science (Clarivate), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MathScinet. Data charting: Metadata of the study, the context of the study, model structure, model process and reporting quality will be extracted. A narrative summary of this information, gaps and recommendations will be prepared and reported in One Health decision-making context.

ETHICS AND DISSEMINATION

Research ethics board approval was not obtained for this study as neither human participation nor unpublished human data were used in this study. The study findings will be widely disseminated among the One Health Modelling Network for Emerging Infections network and stakeholders by means of conferences, and publication in open-access peer-reviewed journals.

Potential of nanocarriers using ABC transporters for antimicrobial resistance

Some existing therapies such as antimicrobial regimens, drug combinations, among others, are employed for the treatment of infections that are a threat to the healthcare industry owing to low drug efficacy, increasing dosage regimes, mutation in bacteria and poor pharmacokinetics/pharmacodynamics properties of drugs. Overuse of antibiotics is fostering the emergence and spread of inherent microorganisms that confer temporary and permanent resistance. Nanocarriers accompanying the ABC transporter efflux mechanism are considered 'magic bullets' (i.e., effective antibacterial agents) and can traverse the multidrug-resistant obstacle owing to their multifunctional capabilities (e.g., nanostructure, variability in in vivo functions, etc.) by interfering with normal cell activity. This review focuses on novel applications of the ABC transporter pump by nanocarriers to overcome the resistance caused by the various organs of the body. Teaser: Nanocarriers, the ABC transporter and overcoming multidrug resistance.

Antifungal alternation can be beneficial for durability but at the cost of generalist resistance

The evolution of resistance to pesticides is a major burden in agriculture. Resistance management involves maximizing selection pressure heterogeneity, particularly by combining active ingredients with different modes of action. We tested the hypothesis that alternation may delay the build-up of resistance not only by spreading selection pressure over longer periods, but also by decreasing the rate of evolution of resistance to alternated fungicides, by applying an experimental evolution approach to the economically important crop pathogen Zymoseptoria tritici. Our results show that alternation is either neutral or slows the overall resistance evolution rate, relative to continuous fungicide use, but results in higher levels of generalism in evolved lines. We demonstrate that the nature of the fungicides, and therefore their relative intrinsic risk of resistance may underly this trade-off, more so than the number of fungicides and the rhythm of alternation. This trade-off is also dynamic over the course of resistance evolution. These findings open up new possibilities for tailoring resistance management effectively while optimizing interplay between alternation components.

The Opportunity of Point-of-Care Diagnostics in General Practice: Modelling the Effects on Antimicrobial Resistance

OBJECTIVES

Antimicrobial resistance (AMR) is a public health threat associated with antibiotic consumption. Community-acquired acute respiratory tract infections (CA-ARTIs) are a major driver of antibiotic consumption in primary care. We aimed to quantify the investments required for a large-scale rollout of point-of care (POC) diagnostic testing in Dutch primary care, and the impact on AMR due to reduced use of antibiotics.

METHODS

We developed an individual-based model that simulates consultations for CA-ARTI at GP practices in the Netherlands and compared a scenario where GPs test all CA-ARTI patients with a hypothetical diagnostic strategy to continuing the current standard-of-care for the years 2020-2030. We estimated differences in costs and future AMR rates caused by testing all patients consulting for CA-ARTI with a hypothetical diagnostic strategy, compared to the current standard-of-care in GP practices.

RESULTS

Compared to the current standard-of-care, the diagnostic algorithm increases the total costs of GP consultations for CA-ARTI by 9% and 19%, when priced at €5 and €10, respectively. The forecast increase in Streptococcus pneumoniae resistance against penicillins can be partly restrained by the hypothetical diagnostic strategy from 3.8 to 3.5% in 2030, albeit with considerable uncertainty.

CONCLUSIONS

Our results show that implementing a hypothetical diagnostic strategy for all CA-ARTI patients in primary care raises the costs of consultations, while lowering antibiotic consumption and AMR. Novel health-economic methods to assess and communicate the potential benefits related to AMR may be required for interventions with limited gains for individual patients, but considerable potential related to antibiotic consumption and AMR.

Modelling the Future Clinical and Economic Burden of Antimicrobial Resistance: The Feasibility and Value of Models to Inform Policy

Due to the increasing threat to public health and the economy, governments internationally are interested in models to estimate the future clinical and economic burden of antimicrobial resistance (AMR) and to evaluate the cost-effectiveness of interventions to prevent or control resistance and to inform resource-allocation decision making. A widely cited UK report estimated that 10 million additional deaths will occur globally per annum due to AMR by 2050; however, the utility and accuracy of this prediction has been challenged. The precision of models predicting the future economic burden of AMR is dependent upon the accuracy of predicting future resistance rates. This paper reviews the feasibility and value of modelling to inform policy and resource allocation to manage and curb AMR. Here we describe methods used to estimate future resistance in published burden-of-disease models; the sources of uncertainty are highlighted, which could potentially mislead policy decision-making. While broad assumptions can be made regarding some predictable factors contributing to future resistance rates, the unexpected emergence, establishment and spread of new resistance genes introduces substantial uncertainty into estimates of future economic burden, and in models evaluating the effectiveness of interventions or policies to address AMR. Existing reporting standards for best practice in modelling should be adapted to guide the reporting of AMR economic models, to ensure model transparency and validation for interpretation by policymakers.

Modeling the Impact of Management Changes on the Infection Dynamics of Extended-Spectrum Beta-Lactamase-Producing Escherichia coli in the Broiler Production

Livestock animals, especially poultry, are a known reservoir for extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli (E. coli). They may enter the pen either via positive day-old chicks or via the environment. We developed a mathematical model to illustrate the entry and dissemination of resistant bacteria in a broiler pen during one fattening period in order to investigate the effectiveness of intervention measures on this infection process. Different management measures, such as varying amounts of litter, a slow-growing breed or lower stocking densities, were tested for their effects on broiler colonization. We also calculated the impact of products that may influence the microbiota in the chicks' digestive tract, such as pre- or probiotics, feed supplements or competitive exclusion products. Our model outcomes show that a contaminated pen or positive chicks at the beginning of the fattening period can infect the entire flock. Increasing the amount of litter and decreasing the stocking density were shown to be effective in our model. Differences in the route of entry were found: if the chicks are already positive, the litter quantity must be increased to at least six times the standard of 1000 g/m2, whereas, if the pen is contaminated on the first day, three times the litter quantity is sufficient. A reduced stocking density of 20 kg/m2 had a significant effect on the incidence of infection only in a previously contaminated pen. Combinations of two or three measures were effective in both scenarios; similarly, feed additives may be beneficial in reducing the growth rate of ESBL-producing E. coli. This model is a valuable tool for evaluating interventions to reduce the transmission and spread of resistant bacteria in broiler houses. However, data are still needed to optimize the model, such as growth rates or survival data of ESBL-producing E. coli in different environments.

Quantifying the Relationship between Antibiotic Use in Food-Producing Animals and Antibiotic Resistance in Humans

It is commonly asserted that agricultural production systems must use fewer antibiotics in food-producing animals in order to mitigate the global spread of antimicrobial resistance (AMR). In order to assess the cost-effectiveness of such interventions, especially given the potential trade-off with rural livelihoods, we must quantify more precisely the relationship between food-producing animal antimicrobial use and AMR in humans. Here, we outline and compare methods that can be used to estimate this relationship, calling on key literature in this area. Mechanistic mathematical models have the advantage of being rooted in epidemiological theory, but may struggle to capture relevant non-epidemiological covariates which have an uncertain relationship with human AMR. We advocate greater use of panel regression models which can incorporate these factors in a flexible way, capturing both shape and scale variation. We provide recommendations for future panel regression studies to follow in order to inform cost-effectiveness analyses of AMR containment interventions across the One Health spectrum, which will be key in the age of increasing AMR.

A Review on Occurrence and Spread of Antibiotic Resistance in Wastewaters and in Wastewater Treatment Plants: Mechanisms and Perspectives

This paper reviews current knowledge on sources, spread and removal mechanisms of antibiotic resistance genes (ARGs) in microbial communities of wastewaters, treatment plants and downstream recipients. Antibiotic is the most important tool to cure bacterial infections in humans and animals. The over- and misuse of antibiotics have played a major role in the development, spread, and prevalence of antibiotic resistance (AR) in the microbiomes of humans and animals, and microbial ecosystems worldwide. AR can be transferred and spread amongst bacteria via intra- and interspecies horizontal gene transfer (HGT). Wastewater treatment plants (WWTPs) receive wastewater containing an enormous variety of pollutants, including antibiotics, and chemicals from different sources. They contain large and diverse communities of microorganisms and provide a favorable environment for the spread and reproduction of AR. Existing WWTPs are not designed to remove micropollutants, antibiotic resistant bacteria (ARB) and ARGs, which therefore remain present in the effluent. Studies have shown that raw and treated wastewaters carry a higher amount of ARB in comparison to surface water, and such reports have led to further studies on more advanced treatment processes. This review summarizes what is known about AR removal efficiencies of different wastewater treatment methods, and it shows the variations among different methods. Results vary, but the trend is that conventional activated sludge treatment, with aerobic and/or anaerobic reactors alone or in series, followed by advanced post treatment methods like UV, ozonation, and oxidation removes considerably more ARGs and ARB than activated sludge treatment alone. In addition to AR levels in treated wastewater, it examines AR levels in biosolids, settled by-product from wastewater treatment, and discusses AR removal efficiency of different biosolids treatment procedures. Finally, it puts forward key-points and suggestions for dealing with and preventing further increase of AR in WWTPs and other aquatic environments, together with a discussion on the use of mathematical models to quantify and simulate the spread of ARGs in WWTPs. Mathematical models already play a role in the analysis and development of WWTPs, but they do not consider AR and challenges remain before models can be used to reliably study the dynamics and reduction of AR in such systems.

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.

Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater

Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of the development of antibiotic-resistant infections in wastewater, we have developed a mathematical model synthesizing many known drivers of antibiotic resistance in these settings to help predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug-resistant infection outbreak, including antibiotic residue concentration, antibiotic interaction, chromosomal mutation, and horizontal gene transfer, have not previously been integrated into a single computational model. We validated the outputs of the model with quantitative studies conducted on the eVOLVER continuous culture platform. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to increased development of resistant populations and that the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than acquisition of chromosomal mutations. Additionally, we found that synergistic antibiotics at low concentrations lead to increased resistant population growth. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic-resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low-income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts.

IMPORTANCE The rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and according to the Review on Antimicrobial Resistance in 2014, it is suggested that the current rate will lead to AMR-related deaths of several million people by 2050 (Review on Antimicrobial Resistance, Tackling a Crisis for the Health and Wealth of Nations, 2014). One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug-resistant bacterial infections but is not well understood is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.

Identifying global research gaps to mitigate antimicrobial resistance: A scoping review

OBJECTIVE

Identify research gaps relevant to the global effort to combat antimicrobial resistance.

METHODS

Web of Science, PubMed, Scopus, and Ovid MEDLINE were searched for reviews on antimicrobial resistance published between January 1, 2015 and December 31, 2019. Recommendations for future research were identified.

FINDINGS

Seventy-four reviews met inclusion criteria; 300 research gaps and recommendations were identified. The largest number were from the human health sector (105; 35%) followed by environmental health (72; 23%), animal health (66; 22%), food and feed (14; 5%), and plants and crops (8; 3%); 35 (12%) involved more than one sector. The largest number of gaps concerned surveillance of resistance (68; 23%), followed by study design or methodology (52; 17%), interventions (41; 14%), risk assessment and modeling (35; 12%), ecological (26; 9%) and biochemical (28; 9%) aspects of resistance, interface between reservoirs of resistant pathogens (24; 8%), and economic (15; 5%) and awareness- and behavior-related (11; 4%) aspects of antimicrobial resistance.

CONCLUSIONS

Important research gaps remain in our complete understanding of antimicrobial resistance, and more research is needed about its development, transmission, and impact across the interface of human, animal, and environmental reservoirs.

Developing Surrogate Markers for Predicting Antibiotic Resistance "Hot Spots" in Rivers Where Limited Data Are Available

Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment to identify potential "simple" surrogates that mirror elevated AR. We screened for resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. To understand relationships, we introduced standardized "effect sizes" (Cohen's D) for AR monitoring to improve comparability of field studies. Overall, water quality generally declined and environmental AR levels increased as one moved down the catchment without major seasonal variations, except total antibiotic concentrations that were higher in the dry season (Cohen's D > 0.8, P < 0.05). Among simple surrogates, dissolved oxygen (DO) most strongly correlated (inversely) with total AR gene concentrations (Spearman's ρ 0.81, P < 0.05). We suspect this results from minimally treated sewage inputs, which also contain AR bacteria and genes, depleting DO in the most impacted reaches. Thus, although DO is not a measure of AR, lower DO levels reflect wastewater inputs, flagging possible AR hot spots. DO measurement is inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, we propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data.

Prudent Use of Antibiotics in Dairy Cows: The Nordic Approach to Udder Health

Global concerns regarding bacterial antibiotic resistance demand prudent use of antibiotics in livestock production. Dairy production in the Nordic countries has a low consumption of antibiotics, while animal health, productivity and milk quality are at high levels. Here, we describe the basis of Nordic mastitis control and treatment strategies, as a model for production of high-quality milk with prudent use of antibiotics. We hope this will be beneficial for dairy producers and advisors in other countries and regions that consider limiting antibiotic use in cattle herds. In this perspectives paper we describe the dairy sector in the Nordic countries, and present regulatory aspects of antibiotic use, diagnostics and current guidelines for treatment of clinical mastitis as well as dry cow therapy. We also show summary statistics of udder health indicators in Denmark, Finland, Norway and Sweden, to illustrate the effects of the implemented udder health management practices.

Predicting the future distribution of antibiotic resistance using time series forecasting and geospatial modelling

Background: Increasing antibiotic resistance in a location may be mitigated by changes in treatment policy, or interventions to limit transmission of resistant bacteria. Therefore, accurate forecasting of the distribution of antibiotic resistance could be advantageous. Two previously published studies addressed this, but neither study compared alternative forecasting algorithms or considered spatial patterns of resistance spread. Methods: We analysed data describing the annual prevalence of antibiotic resistance per country in Europe from 2012 – 2016, and the quarterly prevalence of antibiotic resistance per clinical commissioning group in England from 2015 – 2018. We combined these with data on rates of possible covariates of resistance. These data were used to compare the previously published forecasting models, with other commonly used forecasting models, including one geospatial model. Covariates were incorporated into the geospatial model to assess their relationship with antibiotic resistance. Results: For the European data, which was recorded on a coarse spatiotemporal scale, a naïve forecasting model was consistently the most accurate of any of the forecasting models tested. The geospatial model did not improve on this accuracy. However, it did provide some evidence that antibiotic consumption can partially explain the distribution of resistance. The English data were aggregated at a finer scale, and expected-trend-seasonal (ETS) forecasts were the most accurate. The geospatial model did not significantly improve upon the median accuracy of the ETS model, but it appeared to be less sensitive to noise in the data, and provided evidence that rates of antibiotic prescription and bacteraemia are correlated with resistance. Conclusion: Annual, national-level surveillance data appears to be insufficient for fitting accurate antibiotic resistance forecasting models, but there is evidence that data collected at a finer spatiotemporal scale could be used to improve forecast accuracy. Additionally, incorporating antibiotic prescription or consumption data into the model could improve the predictive accuracy.

Estimating the Value of New Antimicrobials in the Context of Antimicrobial Resistance: Development and Application of a Dynamic Disease Transmission Model

OBJECTIVES

Antimicrobial resistance (AMR) represents a significant threat to patient and population health. The study aim was to develop and validate a model of AMR that defines and quantifies the value of new antibiotics.

METHODS

A dynamic disease transmission and cost-effectiveness model of AMR consisting of three components (disease transmission, treatment pathway and optimisation) was developed to evaluate the health economic value of new antibiotics. The model is based on the relationship between AMR, antimicrobial availability and consumption. Model analysis explored the impact of different antibiotic treatment strategies on the development of AMR, patient and population estimates of health benefit, across three common treatment indications and pathogens in the UK.

RESULTS

Population-level resistance to existing antimicrobials was estimated to increase from 10.3 to 16.1% over 10 years based on current antibiotic availability and consumption. In comparison, the diversified use of a new antibiotic was associated with significant reduction in AMR (12.8% vs. 16.1%) and quality-adjusted life year (QALY) gains at a patient (7.7-10.3, dependent on antimicrobial efficacy) and population level (3657-8197, dependent on antimicrobial efficacy and the prevalence of AMR). Validation across several real-world data sources showed that the model output does not tend to systematically under- or over-estimate observed data.

CONCLUSIONS

The development of new antibiotics and the appropriate use of existing antibiotics are key to addressing the threat of AMR. This study presents a validated model that quantifies the value of new antibiotics through clinical and economic outcomes of relevance, and accounts for disease transmission of infection and development of AMR. In this context, the model may be a useful tool that could contribute to the decision-making process alongside other potential models and expert advice.

Resistance proportions for eight priority antibiotic-bacterium combinations in OECD, EU/EEA and G20 countries 2000 to 2030: a modelling study

Background

Antimicrobial resistance is widely considered an urgent global health issue due to associated mortality and disability, societal and healthcare costs.

Aim

To estimate the past, current and projected future proportion of infections resistant to treatment for eight priority antibiotic-bacterium combinations from 2000 to 2030 for 52 countries.

Methods

We collated data from a variety of sources including ResistanceMap and World Bank. Feature selection algorithms and multiple imputation were used to produce a complete historical dataset. Forecasts were derived from an ensemble of three models: exponential smoothing, linear regression and random forest. The latter two were informed by projections of antibiotic consumption, out-of-pocket medical spending, populations aged 64 years and older and under 15 years and real gross domestic product. We incorporated three types of uncertainty, producing 150 estimates for each country-antibiotic-bacterium-year.

Results

Average resistance proportions across antibiotic-bacterium combinations could grow moderately from 17% to 18% within the Organisation for Economic Co-operation and Development (OECD; growth in 64% of uncertainty sets), from 18% to 19% in the European Union/European Economic Area (EU/EEA; growth in 87% of uncertainty sets) and from 29% to 31% in Group of Twenty (G20) countries (growth in 62% of uncertainty sets) between 2015 and 2030. There is broad heterogeneity in levels and rates of change across countries and antibiotic-bacterium combinations from 2000 to 2030.

Conclusion

If current trends continue, resistance proportions are projected to marginally increase in the coming years. The estimates indicate there is significant heterogeneity in resistance proportions across countries and antibiotic-bacterium combinations.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

Utilising sigmoid models to predict the spread of antimicrobial resistance at the country level

BackgroundThe spread of antimicrobial resistance (AMR) is of worldwide concern. Public health policymakers and pharmaceutical companies pursuing antibiotic development require accurate predictions about the future spread of AMR.AimWe aimed to identify and model temporal and geographical patterns of AMR spread and to predict future trends based on a slow, intermediate or rapid rise in resistance.MethodsWe obtained data from five antibiotic resistance surveillance projects spanning the years 1997 to 2015. We aggregated the isolate-level or country-level data by country and year to produce country-bacterium-antibiotic class triads. We fitted both linear and sigmoid models to these triads and chose the one with the better fit. For triads that conformed to a sigmoid model, we classified AMR progression into one of three characterising paces: slow, intermediate or fast, based on the sigmoid slope. Within each pace category, average sigmoid models were calculated and validated.ResultsWe constructed a database with 51,670 country-year-bacterium-antibiotic observations, grouped into 7,440 country-bacterium-antibiotic triads. A total of 1,037 triads (14%) met the inclusion criteria. Of these, 326 (31.4%) followed a sigmoid (logistic) pattern over time. Among 107 triads for which both sigmoid and linear models could be fit, the sigmoid model was a better fit in 84%. The sigmoid model deviated from observed data by a median of 6.5%; the degree of deviation was related to the pace of spread.ConclusionWe present a novel method of describing and predicting the spread of antibiotic-resistant organisms.

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.