Modeling antibiotic resistance in hospitals: the impact of minimizing treatment duration

Modelling the implementation of narrow versus broader spectrum antibiotics in the empiric treatment of E. coli bacteraemia

The implementation of new antimicrobial resistance stewardship programs is crucial in optimizing antibiotic use. However, prescription choices can be difficult during empiric therapy; clinicians must balance the survival benefits of broader spectrum antibiotics with associated increases in resistance. The aim of this study was to evaluate the overall feasibility of switching to narrow spectrum antibiotics during the empiric treatment of E. coli bacteraemia by quantifying changes in resistance rates, antibiotic usage, and mortality using a deterministic state-transition model. Three unique model scenarios (A, B, and C), each representing a progressively broader spectrum empiric treatment regimen, were used to compare outcomes at 5 years. We show that the empiric use of the narrowest spectrum (first-line) antibiotics can lead to reductions in resistance to second-line antibiotics and the use of third-line antibiotics, but they also lead to increases in resistance to first-line therapy and higher mortality. Crucially, we find that shortening the duration of empiric and overall treatment, as well as reducing the baseline mortality rate, are important for increasing the feasibility of switching to narrow spectrum antibiotics in the empiric treatment of E. coli bacteraemia. We provide a flexible model design to investigate optimal treatment approaches for other bacterial infections.

An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design

Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa-a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years-a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.

Most hospital patients at risk for bacterial infection undergo an anesthetic: implications for infection control practices related to the anesthesia workspace

PURPOSE

Even with nearly 100% compliance with prophylactic antibiotic protocols, many surgical patients (> 5%) develop surgical site infections, some caused by pathogens transmitted from the anesthesia workspace (e.g., anesthesia machine), including multidrug-resistant Staphylococcus aureus. Reducing contamination of the anesthesia workspace substantively reduces the risk of surgical site infections. We estimated the percentage of hospital patients at risk for health care-associated infections who may benefit from the application of basic preventive measures under the control of anesthesia practitioners (e.g., their hand hygiene).

METHODS

We conducted a retrospective cohort study which included every patient admitted to the University of Miami Health System from April 2021 through March 2022 for hospitalization, surgery, emergency department visits, or outpatient visits. Lists were created for the start date and times of every parenteral antibiotic administered and every anesthetic.

RESULTS

Among 28,213 patient encounters including parenteral antibiotic(s), more than half (64.3%) also included an anesthetic (99% confidence interval, 62.2 to 66.6). The hypothesis that most antibiotics were administered during encounters when a patient underwent an anesthetic was accepted (P < 0.001). This observation may seem counterintuitive because parenteral antibiotics were administered for fewer than half of the 53,235 anesthetics (34.2%). The result was a consequence of most anesthetics (63.5%) at the health system being conducted in nonoperating room locations, and only 7.2% of such patients received a parenteral antibiotic.

CONCLUSIONS

Because approximately two-thirds of patients who receive an intravenous antibiotic also undergo an anesthetic, greater use of effective infection control measures in the anesthesia operating room workspace has the potential to substantively reduce overall rates of hospital infections.

Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

Implications of reducing antibiotic treatment duration for antimicrobial resistance in hospital settings: A modelling study and meta-analysis

BACKGROUND

Reducing antibiotic treatment duration is a key component of hospital antibiotic stewardship interventions. However, its effectiveness in reducing antimicrobial resistance is uncertain and a clear theoretical rationale for the approach is lacking. In this study, we sought to gain a mechanistic understanding of the relation between antibiotic treatment duration and the prevalence of colonisation with antibiotic-resistant bacteria in hospitalised patients.

METHODS AND FINDINGS

We constructed 3 stochastic mechanistic models that considered both between- and within-host dynamics of susceptible and resistant gram-negative bacteria, to identify circumstances under which shortening antibiotic duration would lead to reduced resistance carriage. In addition, we performed a meta-analysis of antibiotic treatment duration trials, which monitored resistant gram-negative bacteria carriage as an outcome. We searched MEDLINE and EMBASE for randomised controlled trials published from 1 January 2000 to 4 October 2022, which allocated participants to varying durations of systemic antibiotic treatments. Quality assessment was performed using the Cochrane risk-of-bias tool for randomised trials. The meta-analysis was performed using logistic regression. Duration of antibiotic treatment and time from administration of antibiotics to surveillance culture were included as independent variables. Both the mathematical modelling and meta-analysis suggested modest reductions in resistance carriage could be achieved by reducing antibiotic treatment duration. The models showed that shortening duration is most effective at reducing resistance carriage in high compared to low transmission settings. For treated individuals, shortening duration is most effective when resistant bacteria grow rapidly under antibiotic selection pressure and decline rapidly when stopping treatment. Importantly, under circumstances whereby administered antibiotics can suppress colonising bacteria, shortening antibiotic treatment may increase the carriage of a particular resistance phenotype. We identified 206 randomised trials, which investigated antibiotic duration. Of these, 5 reported resistant gram-negative bacteria carriage as an outcome and were included in the meta-analysis. The meta-analysis determined that a single additional antibiotic treatment day is associated with a 7% absolute increase in risk of resistance carriage (80% credible interval 3% to 11%). Interpretation of these estimates is limited by the low number of antibiotic duration trials that monitored carriage of resistant gram-negative bacteria, as an outcome, contributing to a large credible interval.

CONCLUSIONS

In this study, we found both theoretical and empirical evidence that reducing antibiotic treatment duration can reduce resistance carriage, though the mechanistic models also highlighted circumstances under which reducing treatment duration can, perversely, increase resistance. Future antibiotic duration trials should monitor antibiotic-resistant bacteria colonisation as an outcome to better inform antibiotic stewardship policies.

Short treatment duration for community-acquired pneumonia

PURPOSE OF REVIEW

Lower respiratory tract infections are one of the most common indications for antibiotic use in community and hospital settings. Usual guidelines for adults with community-acquired pneumonia (CAP) recommend 5-7 days of antibiotic treatment. In daily practice, physicians often prescribe 9-10 days of antibiotic treatment. Among available strategies to decrease antibiotic use, possibly preventing the emergence of bacterial resistance, reducing treatment durations is the safest and the most acceptable to clinicians. We aim to review data evaluating the efficacy of short antibiotic duration in adult CAP and which criteria can help clinicians to reduce antibiotic treatment.

RECENT FINDINGS

Several studies and meta-analyses demonstrated that the treatment duration of 7 days or less was sufficient for CAP. Two trials found that 3-day treatments were effective, even in hospitalized CAP.To customize and shorten duration, clinical and biological criteria have been studied and reflect patient's response. Indeed, stability criteria were recently shown to be effective to discontinue antibiotic treatment. Procalcitonin was also studied but never compared with clinical criteria.

SUMMARY

Treatment duration for CAP is still under debate, but several studies support short durations. Clinical criteria could be possibly used to discontinue antibiotic treatment.

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.

Antibiotic De-Escalation in Emergency General Surgery

Background. Antibiotic treatment in emergency general surgery (EGS) is a major challenge for surgeons, and a multidisciplinary approach is necessary in order to improve outcomes. Intra-abdominal infections are at high risk of increased morbidity and mortality, and prolonged hospitalization. An increase in multi-drug resistance bacterial infections and a tendency to an antibiotic overuse has been described in surgical settings. In this clinical scenario, antibiotic de-escalation (ADE) is emerging as a strategy to improve the management of antibiotic therapy. The objective of this article is to summarize the available evidence, current strategies and unsolved problems for the optimization of ADE in EGS. Methods. A literature search was performed on PubMed and Cochrane using “de-escalation”, “antibiotic therapy” and “antibiotic treatment” as research terms. Results. There is no universally accepted definition for ADE. Current evidence shows that ADE is a feasible strategy in the EGS setting, with the ability to optimize antibiotic use, to reduce hospitalization and health care costs, without compromising clinical outcome. Many studies focus on Intensive Care Unit patients, and a call for further studies is required in the EGS community. Current guidelines already recommend ADE when surgery for uncomplicated appendicitis and cholecystitis reaches a complete source control. Conclusions. ADE in an effective and feasible strategy in EGS patients, in order to optimize antibiotic management without compromising clinical outcomes. A collaborative effort between surgeons, intensivists and infectious disease specialists is mandatory. There is a strong need for further studies selectively focusing in the EGS ward setting.

Modelling of the transmission dynamics of carbapenem-resistant Klebsiella pneumoniae in hospitals and design of control strategies

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has emerged as a major threat to global public health. Epidemiological and infection controls associated with CRKP are challenging because of several potential elements involved in a complicated cycle of transmission. Here, we proposed a comprehensive mathematical model to investigate the transmission dynamics of CRKP, determine factors affecting the prevalence, and evaluate the impact of interventions on transmission. The model includes the essential compartments, which are uncolonized, asymptomatic colonized, symptomatic colonized, and relapsed patients. Additionally, symptomatic colonized and relapsed patients were further classified into subpopulations according to their number of treatment failures or relapses. We found that the admission of colonized patients and use of antibiotics significantly impacted the endemic transmission in health care units. Thus, we introduced the treatment efficacy, defined by combining the treatment duration and probability of successful treatment, to characterize and describe the effects of antibiotic treatment on transmission. We showed that a high antibiotic treatment efficacy results in a significantly reduced likelihood of patient readmission in the health care unit. Additionally, our findings demonstrate that CRKP transmission with different epidemiological characteristics must be controlled using distinct interventions.

Cost-Effectiveness of Molecular Method Diagnostic for Rapid Detection of Antibiotic-Resistant Bacteria

OBJECTIVES

This study aimed to perform a cost-effectiveness analysis (CEA) of the molecular diagnostic method (MM) associated with conventional diagnostic method (CM) compared with the CM alone, for the detection of resistant profile in bacteremia, from the perspective of the Brazilian Public Health System, in intensive care units setting.

METHODS

The clinical parameters regarding methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Gram-negative bacteria (CRGNB), and vancomycin-resistant Enterococcus spp. (VRE) infections were collected from searches on PubMed, Scopus, and SciELO, using specific keywords. Data on direct medical costs to treat these infections were collected according to Brazilian Public Health System perspective from Brazilian databases, in tables of 2018 to 2019. CEA was performed after building a dynamic model, which was calibrated and validated according to international recommendations. The incremental cost-effectiveness ratio of the MM + CM compared with the CM was calculated using the outcomes "avoided death" and "avoided resistant infections." One-way sensitivity analyses were performed.

RESULTS

This CEA demonstrated that the MM + CM was dominant in all scenarios. Estimates showed that for MRSA, CRGNB, and VRE infections, every avoided death would lead to savings of Brazilian real (R$) 4.9 million ($937 301), R$2.2 million ($419 899), and R$1.3 million ($248 919), respectively. The same infections assessed by avoided resistant infections savings were projected to be R$24 964 ($4686), R$40 260 ($7558), and R$23 867 ($4480).

CONCLUSIONS

MM leads to cost reduction and increased benefits, optimizing the use of financial resources on the health system in the intensive care unit setting, in bacteremia caused by MRSA, CRGNB, and VRE.

SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study

Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions. We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss. The use of high-efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% credible interval (CrI): 73.1-85.7%) and 87% (CrI: 80.0-92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation. Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.

An agent-based model to simulate the transmission of vancomycin-resistant enterococci according different prevention and control measures

OBJECTIVE

Despite the existence of various levels of infection prevention and control (IPC) measures aimed at limiting the transmission of vancomycin-resistant enterococci (VRE) in hospitals, these measures are sometimes difficult to implement. Using an agent-based model (ABM), we simulated the transmission of VRE within and between 3 care units according to different IPC measures.

METHODS

The ABM was modelled on short-stay medical wards, represented by 2 conventional care units and 1 intensive care unit. The scenarios consisted of the simulation of various compliance rates of caregivers with regard to hand hygiene (HH) in different contexts of IPC measures: (1) standard precautions for all patients, (2) additional contact precautions for VRE-carrier patients, (3) geographical cohorting of carrier patients, and (4) creation of an isolation unit with dedicated staff.

RESULTS

With <50% HH compliance, the dissemination of VRE was not adequately controlled. With 80% compliance for all patients (ie, standard precautions scenario), there were no secondary VRE cases in 50% of the simulations, which represented the best scenario. A more realistic rate, 60% HH compliance for all patients, revealed interesting results. Implementing an isolation unit was effective only if the level of HH compliance was low. Patient cohorting was less effective.

CONCLUSIONS

The present ABM showed that while contact precautions, geographic cohorting, and an isolation unit may represent good complements to standard precautions, they may theoretically not be necessary if HH is followed at a high level of compliance.

Antibiotics in early life associate with specific gut microbiota signatures in a prospective longitudinal infant cohort

BACKGROUND

The effects of antibiotics on infant gut microbiota are unclear. We hypothesized that the use of common antibiotics results in long-term aberration in gut microbiota.

METHODS

Antibiotic-naive infants were prospectively recruited when hospitalized because of a respiratory syncytial virus infection. Composition of fecal microbiota was compared between those receiving antibiotics during follow-up (prescribed at clinicians' discretion because of complications such as otitis media) and those with no antibiotic exposure. Fecal sampling started on day 1, then continued at 2-day intervals during the hospital stay, and at 1, 3 and 6 months at home.

RESULTS

One hundred and sixty-three fecal samples from 40 patients (median age 2.3 months at baseline; 22 exposed to antibiotics) were available for microbiota analyses. A single course of amoxicillin or macrolide resulted in aberration of infant microbiota characterized by variation in the abundance of bifidobacteria, enterobacteria and clostridia, lasting for several months. Recovery from the antibiotics was associated with an increase in clostridia. Occasionally, antibiotic use resulted in microbiota profiles associated with inflammatory conditions.

CONCLUSIONS

Antibiotic use in infants modifies especially bifidobacterial levels. Further studies are warranted whether administration of bifidobacteria will provide health benefits by normalizing the microbiota in infants receiving antibiotics.

Simulation models for transmission of health care-associated infection: A systematic review

BACKGROUND

Health care-associated infections (HAIs) are a global health burden because of their significant impact on patient health and health care systems. Mechanistic simulation modeling that captures the dynamics between patients, pathogens, and the environment is increasingly being used to improve understanding of epidemiological patterns of HAIs and to facilitate decisions on infection prevention and control (IPC). The purpose of this review is to present a systematic review to establish (1) how simulation models have been used to investigate HAIs and their mitigation and (2) how these models have evolved over time, as well as identify (3) gaps in their adoption and (4) useful directions for their future development.

METHODS

The review involved a systematic search and identification of studies using system dynamics, discrete event simulation, and agent-based model to study HAIs.

RESULTS

The complexity of simulation models developed for HAIs significantly increased but heavily concentrated on transmission dynamics of methicillin-resistant Staphylococcus aureus in the hospitals of high-income countries. Neither HAIs in other health care settings, the influence of contact networks within a health care facility, nor patient sharing and referring networks across health care settings were sufficiently understood.

CONCLUSIONS

This systematic review provides a broader overview of existing simulation models in HAIs to identify the gaps and to direct and facilitate further development of appropriate models in this emerging field.

VERA: agent-based modeling transmission of antibiotic resistance between human pathogens and gut microbiota

MOTIVATION

The resistance of bacterial pathogens to antibiotics is one of the most important issues of modern health care. The human microbiota can accumulate resistance determinants and transfer them to pathogenic microbiota by means of horizontal gene transfer. Thus, it is important to develop methods of prediction and monitoring of antibiotics resistance in human populations.

RESULTS

We present the agent-based VERA model, which allows simulation of the spread of pathogens, including the possible horizontal transfer of resistance determinants from a commensal microbiota community. The model considers the opportunity of residents to stay in the town or in a medical institution, have incorrect self-treatment, treatment with several antibiotics types and transfer and accumulation of resistance determinants from commensal microorganism to a pathogen. In this model, we have also created an assessment of optimum observation frequency of infection spread among the population. Investigating model behavior, we show a number of non-linear dependencies, including the exponential nature of the dependence of the total number of those infected on the average resistance of a pathogen. As the model infection, we chose infection with Shigella spp., though it could be applied to a wide range of other pathogens.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries VERA and VERA.viewer are freely available for download at github.com/lpenguin/microbiota-resistome. The code is written in Java, JavaScript and R for Linux platform.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Duration of antibiotic therapy in critically ill patients: a randomized controlled trial of a clinical and C-reactive protein-based protocol versus an evidence-based best practice strategy without biomarkers

Background

The rational use of antibiotics is one of the main strategies to limit the development of bacterial resistance. We therefore sought to evaluate the effectiveness of a C-reactive protein-based protocol in reducing antibiotic treatment time in critically ill patients.

Methods

A randomized, open-label, controlled clinical trial conducted in two intensive care units of a university hospital in Brazil. Critically ill infected adult patients were randomly allocated to (i) intervention to receive antibiotics guided by daily monitoring of CRP levels and (ii) control to receive antibiotics according to the best practices for rational use of antibiotics.

Results

One hundred thirty patients were included in the CRP (n = 64) and control (n = 66) groups. In the intention-to-treat analysis, the median duration of antibiotic therapy for the index infectious episode was 7.0 (5.0–8.8) days in the CRP and 7.0 (7.0–11.3) days in the control (p = 0.011) groups. A significant difference in the treatment time between the two groups was identified in the curve of cumulative suspension of antibiotics, with less exposure in the CRP group only for the index infection episode (p = 0.007). In the per protocol analysis, involving 59 patients in each group, the median duration of antibiotic treatment was 6.0 (5.0–8.0) days for the CRP and 7.0 (7.0–10.0) days for the control (p = 0.011) groups. There was no between-group difference regarding the total days of antibiotic exposure and antibiotic-free days.

Conclusions

Daily monitoring of CRP levels may allow early interruption of antibiotic therapy in a higher proportion of patients, without an effect on total antibiotic consumption. The clinical and microbiological relevance of this finding remains to be demonstrated.

Trial registry

ClinicalTrials.gov Identifier: NCT02987790. Registered 09 December 2016.

Modeling Antibiotic Use Strategies in Intensive Care Units: Comparing De-escalation and Continuation

Antimicrobial de-escalation refers to the treatment mechanism of switching from empiric antibiotics with good coverage to alternatives based on laboratory susceptibility test results, with the aim of avoiding unnecessary use of broad-spectrum antibiotics. In a previous study, we have developed multi-strain and multi-drug models in an intensive care unit setting, to evaluate the benefits and trade-offs of de-escalation in comparison with the conventional strategy called antimicrobial continuation. Our simulation results indicated that for a large portion of credible parameter combinations, de-escalation reduces the use of the empiric antibiotic but increases the probabilities of colonization and infections. In this paper, we first simplify the previous models to compare the long-term dynamical behaviors between de-escalation and continuation systems under a two-strain scenario. The analytical results coincide with our previous findings in the complex models, indicating the benefits and unintended consequences of de-escalation strategy result from the nature of this treatment mechanism, not from the complexity of the high-dimensional systems. By extending the models to three-strain scenarios, we find that de-escalation is superior than continuation in preventing outbreaks of invading strains that are resistant to empiric antibiotics. Thus decisions on antibiotic use strategies should be made specifically according to ICU conditions and intervention objectives.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested "membrane-surrounded entities" able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes.IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples-just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power.

Rationalizing antimicrobial therapy in the ICU: a narrative review

The massive consumption of antibiotics in the ICU is responsible for substantial ecological side effects that promote the dissemination of multidrug-resistant bacteria (MDRB) in this environment. Strikingly, up to half of ICU patients receiving empirical antibiotic therapy have no definitively confirmed infection, while de-escalation and shortened treatment duration are insufficiently considered in those with documented sepsis, highlighting the potential benefit of implementing antibiotic stewardship programs (ASP) and other quality improvement initiatives. The objective of this narrative review is to summarize the available evidence, emerging options, and unsolved controversies for the optimization of antibiotic therapy in the ICU. Published data notably support the need for better identification of patients at risk of MDRB infection, more accurate diagnostic tools enabling a rule-in/rule-out approach for bacterial sepsis, an individualized reasoning for the selection of single-drug or combination empirical regimen, the use of adequate dosing and administration schemes to ensure the attainment of pharmacokinetics/pharmacodynamics targets, concomitant source control when appropriate, and a systematic reappraisal of initial therapy in an attempt to minimize collateral damage on commensal ecosystems through de-escalation and treatment-shortening whenever conceivable. This narrative review also aims at compiling arguments for the elaboration of actionable ASP in the ICU, including improved patient outcomes and a reduction in antibiotic-related selection pressure that may help to control the dissemination of MDRB in this healthcare setting.

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

Background

Antimicrobial resistance is a global health problem that demands all possible means to control it. Mathematical modelling is a valuable tool for understanding the mechanisms of AMR development and spread, and can help us to investigate and propose novel control strategies. However, it is of vital importance that mathematical models have a broad utility, which can be assured if good modelling practice is followed.

Objective

The objective of this study was to provide a comprehensive systematic review of published models of AMR development and spread. Furthermore, the study aimed to identify gaps in the knowledge required to develop useful models.

Methods

The review comprised a comprehensive literature search with 38 selected studies. Information was extracted from the selected papers using an adaptation of previously published frameworks, and was evaluated using the TRACE good modelling practice guidelines.

Results

None of the selected papers fulfilled the TRACE guidelines. We recommend that future mathematical models should: a) model the biological processes mechanistically, b) incorporate uncertainty and variability in the system using stochastic modelling, c) include a sensitivity analysis and model external and internal validation.

Conclusion

Many mathematical models of AMR development and spread exist. There is still a lack of knowledge about antimicrobial resistance, which restricts the development of useful mathematical models.

Modeling the seasonality of Methicillin-resistant Staphylococcus aureus infections in hospitals with environmental contamination

A deterministic mathematical model with periodic antibiotic prescribing rate is constructed to study the seasonality of Methicillin-resistant Staphylococcus aureus (MRSA) infections taking antibiotic exposure and environmental contamination into consideration. The basic reproduction number R0 for the periodic model is calculated under the assumption that there are only uncolonized patients with antibiotic exposure at admission. Sensitivity analysis of R0 with respect to some essential parameters is performed. It is shown that the infection would go to extinction if the basic reproduction number is less than unity and would persist if it is greater than unity. Numerical simulations indicate that environmental cleaning is the most important intervention to control the infection, which emphasizes the effect of environmental contamination in MRSA infections. It is also important to highlight the importance of effective antimicrobial stewardship programmes, increase active screening at admission and subsequent isolation of positive cases, and treat patients quickly and efficiently.

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

Selective pressure exerted by the widespread use of antibacterial drugs is accelerating the development of resistant bacterial populations. The purpose of this scoping review was to summarise the range of studies that use dynamic models to analyse the problem of bacterial resistance in relation to antibacterial use in human and animal populations. A comprehensive search of the peer-reviewed literature was performed and non-duplicate articles (n = 1486) were screened in several stages. Charting questions were used to extract information from the articles included in the final subset (n = 81). Most studies (86%) represent the system of interest with an aggregate model; individual-based models are constructed in only seven articles. There are few examples of inter-host models outside of human healthcare (41%) and community settings (38%). Resistance is modelled for a non-specific bacterial organism and/or antibiotic in 40% and 74% of the included articles, respectively. Interventions with implications for antibacterial use were investigated in 67 articles and included changes to total antibiotic consumption, strategies for drug management and shifts in category/class use. The quality of documentation related to model assumptions and uncertainty varies considerably across this subset of articles. There is substantial room to improve the transparency of reporting in the antibacterial resistance modelling literature as is recommended by best practice guidelines.

THE MATHEMATICAL ANALYSIS OF THE MODEL OF ANTIBIOTIC-RESISTANT BACTERIA AND THE IMMUNE CELLS

Currently, World Health Organization (WHO) report confirms that the antibiotic-resistant infections are the greatest threat to health. Despite the new therapeutic strategies, the bacteria develop mechanisms to defend themselves against antibiotics. In this paper, we propose a mathematical model describing the dynamics of resistant bacteria, non-resistant bacteria and immune cells exposed to an antibiotic. The global stability of equilibria is performed by using Lyapunov functions.

The impact of antibiotic use on transmission of resistant bacteria in hospitals: Insights from an agent-based model

Extensive antibiotic use over the years has led to the emergence and spread of antibiotic resistant bacteria (ARB). Antibiotic resistance poses a major threat to public health since for many infections antibiotic treatment is no longer effective. Hospitals are focal points for ARB spread. Antibiotic use in hospitals exerts selective pressure, accelerating the spread of ARB. We used an agent-based model to explore the impact of antibiotics on the transmission dynamics and to examine the potential of stewardship interventions in limiting ARB spread in a hospital. Agents in the model consist of patients and health care workers (HCW). The transmission of ARB occurs through contacts between patients and HCW and between adjacent patients. In the model, antibiotic use affects the risk of transmission by increasing the vulnerability of susceptible patients and the contagiousness of colonized patients who are treated with antibiotics. The model shows that increasing the proportion of patients receiving antibiotics increases the rate of acquisition non-linearly. The effect of antibiotics on the spread of resistance depends on characteristics of the antibiotic agent and the density of antibiotic use. Antibiotic's impact on the spread increases when the bacterial strain is more transmissible, and decreases as resistance prevalence rises. The individual risk for acquiring ARB increases in parallel with antibiotic density both for patients treated and not treated with antibiotics. Antibiotic treatment in the hospital setting plays an important role in determining the spread of resistance. Interventions to limit antibiotic use have the potential to reduce the spread of resistance, mainly by choosing an agent with a favorable profile in terms of its impact on patient's vulnerability and contagiousness. Methods to measure these impacts of antibiotics should be developed, standardized, and incorporated into drug development programs and approval packages.

OPTIMAL DOSING STRATEGIES AGAINST SUSCEPTIBLE AND RESISTANT BACTERIA

Mathematical models can be very useful in determining efficient and successful antibiotic dosing techniques against bacterial infections. There are several challenging issues involved, the presence of drug resistant bacteria being one. Recent rise in antibiotic resistant strains of bacteria is a grave public health hazard, hence there is a need to develop dosing protocols taking into account the presence of these resistant strains. In this study, we consider a model for antibiotic treatment of a bacterial infection where the bacteria are divided into two sub-populations: susceptible and resistant. The mechanism of acquisition of resistance by the susceptible bacteria considered is via the process of conjugation. We find the steady-state solutions under an antibiotic protocol of discrete periodic doses and analyze their stability. We also prove an extension of a result that pertains to the persistence of bacteria. In addition, we perform the bifurcation analysis under this dosing protocol and show that bi-stability exists for the bacterial population. Furthermore, efficient treatment strategies are devised that ensure bacterial elimination while minimizing the quantity of antibiotic used. Such treatments are necessary to decrease the chances of further development of resistance in bacteria and to minimize the overall treatment cost. We consider the cases of varying antibiotic costs, different initial bacterial densities and bacterial attachment to solid surfaces, and obtain the optimal strategies for all the cases. The results show that the optimal treatments ensure disinfection for a wide range of scenarios.

Multiplicity of Mathematical Modeling Strategies to Search for Molecular and Cellular Insights into Bacteria Lung Infection

Even today two bacterial lung infections, namely pneumonia and tuberculosis, are among the 10 most frequent causes of death worldwide. These infections still lack effective treatments in many developing countries and in immunocompromised populations like infants, elderly people and transplanted patients. The interaction between bacteria and the host is a complex system of interlinked intercellular and the intracellular processes, enriched in regulatory structures like positive and negative feedback loops. Severe pathological condition can emerge when the immune system of the host fails to neutralize the infection. This failure can result in systemic spreading of pathogens or overwhelming immune response followed by a systemic inflammatory response. Mathematical modeling is a promising tool to dissect the complexity underlying pathogenesis of bacterial lung infection at the molecular, cellular and tissue levels, and also at the interfaces among levels. In this article, we introduce mathematical and computational modeling frameworks that can be used for investigating molecular and cellular mechanisms underlying bacterial lung infection. Then, we compile and discuss published results on the modeling of regulatory pathways and cell populations relevant for lung infection and inflammation. Finally, we discuss how to make use of this multiplicity of modeling approaches to open new avenues in the search of the molecular and cellular mechanisms underlying bacterial infection in the lung.

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination<sup/>

In this work, we investigate the role of environmental contamination on the clinical epidemiology of antibiotic-resistant bacteria in hospitals. Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterium that causes infections in different parts of the body. It is tougher to treat than most strains of Staphylococcus aureus or staph, because it is resistant to some commonly used antibiotics. Both deterministic and stochastic models are constructed to describe the transmission characteristics of MRSA in hospital setting. The deterministic epidemic model includes five compartments: colonized and uncolonized patients, contaminated and uncontaminated health care workers (HCWs), and bacterial load in environment. The basic reproduction number R ₀ is calculated, and its numerical and sensitivity analysis has been performed to study the asymptotic behavior of the model, and to help identify factors responsible for observed patterns of infections. A stochastic epidemic model with stochastic simulations is also presented to supply a comprehensive analysis of its behavior. Data collected from Beijing Tongren Hospital will be used in the numerical simulations of our model. The results can be used to provide theoretical guidance for designing efficient control measures, such as increasing the hand hygiene compliance of HCWs and disinfection rate of environment, and decreasing the transmission rate between environment and patients and HCWs.

Duration of antibiotic therapy in the intensive care unit

There are certain well defined clinical situations where prolonged therapy is beneficial, but prolonged duration of antibiotic therapy is associated with increased resistance, medicalising effects, high costs and adverse drug reactions. The best way to decrease antibiotic duration is both to stop antibiotics when not needed (sterile invasive cultures with clinical improvement), not to start antibiotics when not indicated (treating colonization) and keep the antibiotic course as short as possible. The optimal duration of antimicrobial treatment for ventilator-associated pneumonia (VAP) is unknown, however, there is a growing evidence that reduction in the length of antibiotic courses to 7-8 days can minimize the consequences of antibiotic overuse in critical care, including antibiotic resistance, adverse effects, collateral damage and costs. Biomarkers like C-reactive protein (CRP) and procalcitonin (PCT) do have a valuable role in helping guide antibiotic duration but should be interpreted cautiously in the context of the clinical situation. On the other hand, microbiological criteria alone are not reliable and should not be used to justify a prolonged antibiotic course, as clinical cure does not equate to microbiological eradication. We do not recommend a 'one size fits all' approach and in some clinical situations, including infection with non-fermenting Gram-negative bacilli (NF-GNB) clinical evaluation is needed but shortening the antibiotic course is an effective and safe way to decrease inappropriate antibiotic exposure.

Antimicrobial Stewardship and Environmental Decontamination for the Control of Clostridium difficile Transmission in Healthcare Settings

We implement an agent-based model for Clostridium difficile transmission in hospitals that accounts for several processes and individual factors including environmental and antibiotic heterogeneity in order to evaluate the efficacy of various control measures aimed at reducing environmental contamination and mitigating the effects of antibiotic use on transmission. In particular, we account for local contamination levels that contribute to the probability of colonization and we account for both the number and type of antibiotic treatments given to patients. Simulations illustrate the relative efficacy of several strategies for the reduction of nosocomial colonizations and nosocomial diseases.

Guidance on the Selection of Appropriate Indicators for Quantification of Antimicrobial Usage in Humans and Animals

An increasing variety of indicators of antimicrobial usage has become available in human and veterinary medicine, with no consensus on the most appropriate indicators to be used. The objective of this review is therefore to provide guidance on the selection of indicators, intended for those aiming to quantify antimicrobial usage based on sales, deliveries or reimbursement data. Depending on the study objective, different requirements apply to antimicrobial usage quantification in terms of resolution, comprehensiveness, stability over time, ability to assess exposure and comparability. If the aim is to monitor antimicrobial usage trends, it is crucial to use a robust quantification system that allows stability over time in terms of required data and provided output; to compare usage between different species or countries, comparability must be ensured between the different populations. If data are used for benchmarking, the system comprehensiveness is particularly crucial, while data collected to study the association between usage and resistance should express the exposure level and duration as a measurement of the exerted selection pressure. Antimicrobial usage is generally described as the number of technical units consumed normalized by the population at risk of being treated in a defined period. The technical units vary from number of packages to number of individuals treated daily by adding different levels of complexity such as daily dose or weight at treatment. These technical units are then related to a description of the population at risk, based either on biomass or number of individuals. Conventions and assumptions are needed for all of these calculation steps. However, there is a clear lack of standardization, resulting in poor transparency and comparability. By combining study requirements with available approaches to quantify antimicrobial usage, we provide suggestions on the most appropriate indicators and data sources to be used for a given study objective.

Research Methods in Healthcare Epidemiology and Antimicrobial Stewardship-Mathematical Modeling

Mathematical modeling is a valuable methodology used to study healthcare epidemiology and antimicrobial stewardship, particularly when more traditional study approaches are infeasible, unethical, costly, or time consuming. We focus on 2 of the most common types of mathematical modeling, namely compartmental modeling and agent-based modeling, which provide important advantages-such as shorter developmental timelines and opportunities for extensive experimentation-over observational and experimental approaches. We summarize these advantages and disadvantages via specific examples and highlight recent advances in the methodology. A checklist is provided to serve as a guideline in the development of mathematical models in healthcare epidemiology and antimicrobial stewardship. Infect Control Hosp Epidemiol 2016;1-7.

Mathematical modelling of bacterial resistance to multiple antibiotics and immune system response

Resistance of developed bacteria to antibiotic treatment is a very important issue, because introduction of any new antibiotic is after a little while followed by the formation of resistant bacterial isolates in the clinic. The significant increase in clinical resistance to antibiotics is a troubling situation especially in nosocomial infections, where already defenseless patients can be unsuccessful to respond to treatment, causing even greater health issue. Nosocomial infections can be identified as those happening within 2 days of hospital acceptance, 3 days of discharge or 1 month of an operation. They influence 1 out of 10 patients admitted to hospital. Annually, this outcomes in 5000 deaths only in UK with a cost to the National Health Service of a billion pounds. Despite these problems, antibiotic therapy is still the most common method used to treat bacterial infections. On the other hand, it is often mentioned that immune system plays a major role in the progress of infections. In this context, we proposed a mathematical model defining population dynamics of both the specific immune cells produced according to the properties of bacteria by host and the bacteria exposed to multiple antibiotics synchronically, presuming that resistance is gained through mutations due to exposure to antibiotic. Qualitative analysis found out infection-free equilibrium point and other equilibrium points where resistant bacteria and immune system cells exist, only resistant bacteria exists and sensitive bacteria, resistant bacteria and immune system cells exist. As a result of this analysis, our model highlights the fact that when an individual’s immune system weakens, he/she suffers more from the bacterial infections which are believed to have been confined or terminated. Also, these results was supported by numerical simulations.

MATHEMATICAL MODELING OF BACTERIAL RESISTANCE TO ANTIBIOTICS BY MUTATIONS AND PLASMIDS

Diversity of drugs against bacterial infections, and development of resistance to such drugs are increasing. We formulate and analyze a deterministic model for the population dynamics of sensitive and resistant bacteria to multiple bactericidal and bacteriostatic antibiotics, assuming that drug resistance is acquired through mutations and plasmid transmission. Model equilibria are determined from qualitative analysis, and numerical simulations are used to assess temporal dynamics of sensitive and drug-resistant bacteria. The model presents three possibilities: elimination of bacteria, persistence of only resistant bacteria, or coexistence of sensitive and resistant bacteria. Evolution to one of these scenarios depends on thresholds numbers involving sensitive and resistant bacteria.

How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance

Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.

DOI:http://dx.doi.org/10.7554/eLife.10559.001

Antibiotics are chemical compounds used to treat bacterial infections. The discovery of antibiotics, starting with penicillin in 1929, revolutionized medicine, making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia. However, many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs.

The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment. While the antibiotic will kill most bacteria, some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic. These bacteria then reproduce and pass on their resistant traits, eventually leading to the emergence of a new antibiotic-resistant strain of bacteria. Once a resistant strain exists it may be able to spread from one person to another.

There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading. The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing. However, other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections.

Here, Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken. Strains were modeled both within individual hosts and spreading in a community of individuals. In the models, aggressive antibiotic treatment is most effective (in that it minimizes antibiotic resistance) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor. However, a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor. Competition may mean that moderate treatment is best to avoid resistance at the community level, even in situations when aggressive treatment is likely best for individuals.

Two important future challenges are to better understand the diversity of strains in bacterial infections, and to develop tools to measure to what extent strains are effectively competing with each other.

DOI:http://dx.doi.org/10.7554/eLife.10559.002

Characterising the Transmission Dynamics of Acinetobacter baumannii in Intensive Care Units Using Hidden Markov Models

Little is known about the transmission dynamics of Acinetobacter baumannii in hospitals, despite such information being critical for designing effective infection control measures. In the absence of comprehensive epidemiological data, mathematical modelling is an attractive approach to understanding transmission process. The statistical challenge in estimating transmission parameters from infection data arises from the fact that most patients are colonised asymptomatically and therefore the transmission process is not fully observed. Hidden Markov models (HMMs) can overcome this problem. We developed a continuous-time structured HMM to characterise the transmission dynamics, and to quantify the relative importance of different acquisition sources of A. baumannii in intensive care units (ICUs) in three hospitals in Melbourne, Australia. The hidden states were the total number of patients colonised with A. baumannii (both detected and undetected). The model input was monthly incidence data of the number of detected colonised patients (observations). A Bayesian framework with Markov chain Monte Carlo algorithm was used for parameter estimations. We estimated that 96-98% of acquisition in Hospital 1 and 3 was due to cross-transmission between patients; whereas most colonisation in Hospital 2 was due to other sources (sporadic acquisition). On average, it takes 20 and 31 days for each susceptible individual in Hospital 1 and Hospital 3 to become colonised as a result of cross-transmission, respectively; whereas it takes 17 days to observe one new colonisation from sporadic acquisition in Hospital 2. The basic reproduction ratio (R0) for Hospital 1, 2 and 3 was 1.5, 0.02 and 1.6, respectively. Our study is the first to characterise the transmission dynamics of A. baumannii using mathematical modelling. We showed that HMMs can be applied to sparse hospital infection data to estimate transmission parameters despite unobserved events and imperfect detection of the organism. Our results highlight the need to optimise infection control in ICUs.

Limited sampling strategy and target attainment analysis for levofloxacin in patients with tuberculosis

There is an urgent need to improve and shorten the treatment of tuberculosis (TB) and multidrug resistant tuberculosis (MDR-TB). Levofloxacin, a newer fluoroquinolone, has potent activity against TB both in vitro and in vivo. Levofloxacin dosing can be optimized to improve the treatment of both TB and MDR-TB. Levofloxacin efficacy is linked primarily to the ratio of the area under the concentration-time curve for the free fraction of drug (fAUC) to the MIC. Since obtaining a full-time concentration profile is not feasible in the clinic, we developed a limited sampling strategy (LSS) to estimate the AUC. We also utilized Monte Carlo simulations to evaluate the dosing of levofloxacin. Pharmacokinetic data were obtained from 10 Brazilian TB patients. The pharmacokinetic data were fitted with a one-compartment model. LSSs were developed using two methods: linear regression and Bayesian approaches. Several LSSs predicted levofloxacin AUC with good accuracy and precision. The most accurate were the method using two samples collected at 4 and 6 h (R(2) = 0.91 using linear regression and 0.97 using Bayesian approaches) and that using samples collected at 2 and 6 h (R(2) = 0.90 using linear regression and 0.96 using Bayesian approaches). The 2-and-6-h approach also provides a good estimate of the maximum concentration of the drug in serum (Cmax). Our target attainment analysis showed that higher doses (17 to 20 mg/kg of body weight) of levofloxacin might be needed to improve its activity. Doses in the range of 17 to 20 mg/kg showed good target attainment for MICs from 0.25 to 0.50. At an MIC of 2, poor target attainment was observed across all doses. This LSS for levofloxacin can be used for therapeutic drug monitoring and for future pharmacokinetic/pharmacodynamic studies.

What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature

Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results.

Impact of antibiotic exposure on occurrence of nosocomial carbapenem-resistant Acinetobacter baumannii infection: a case control study

Carbapenem-resistant Acinetobacter baumannii (CRAB) infection is one of the most important healthcare associated diseases worldwide. Although antibiotic use is recognized as a risk factor for CRAB infection, the impact of antibiotic class and length of use on CRAB infection is still unclear. A case-control study was conducted in adult intensive care units and general wards of Songklanagarind Hospital, a tertiary-care hospital in southern Thailand, to investigate the effect of different antibiotic exposure and the duration of use on the risk of developing CRAB infection. Cases were defined as patients with carbapenem-susceptible A. baumannii (CSAB) or CRAB infection. Controls were randomly selected from patients and matched 1:1 with cases using ward and date of admission. Multinomial logistic regression was used to compute relative risk ratios (RRR) and 95% confidence intervals (CI) for CRAB infection. Of 197 cases with A. baumannii infection, there were 139 with CRAB infection and 58 with CSAB infection. Compared to the control group, use of fluoroquinolones, broad-spectrum cephalosporins and carbapenems for more than three days increased the risk of CRAB infection with RRR (95% CI) of 81.2 (38.1-862.7), 31.3 (9.9-98.7) and 112.1 (7.1-1770.6), respectively. The RRR (95% CI) for one to three day treatment of fluoroquinolones, broad-spectrum cephalosporins and carbapenems were 5.4 (0.8-38.7), 6.2 (0.1-353.2) and 63.3 (15.6-256.9), respectively. Long-term use of certain antibiotics and even short term use of carbapenems increased the risk of CRAB infection. In this setting, use of these antibiotics, especially carbapenems, should be limited to reduce CRAB infection.

Optimizing hospital infection control: the role of mathematical modeling

Multidrug-resistant bacteria are major causes of nosocomial infections and are associated with considerable morbidity, mortality, and healthcare costs. Preventive strategies have therefore become increasingly important. Mathematical modeling has been widely used to understand the transmission dynamics of nosocomial infections and the quantitative effects of infection control measures. This review will explore the principles of mathematical modeling used in nosocomial infections and discuss the effectiveness of infection control measures investigated using mathematical modeling.

An individual-based model of transmission of resistant bacteria in a veterinary teaching hospital

Veterinary nosocomial infections caused by antibiotic resistant bacteria cause increased morbidity, higher cost and length of treatment and increased zoonotic risk because of the difficulty in treating them. In this study, an individual-based model was developed to investigate the effects of movements of canine patients among ten areas (transmission points) within a veterinary teaching hospital, and the effects of these movements on transmission of antibiotic susceptible and resistant pathogens. The model simulates contamination of transmission points, healthcare workers, and patients as well as the effects of decontamination of transmission points, disinfection of healthcare workers, and antibiotic treatments of canine patients. The model was parameterized using data obtained from hospital records, information obtained by interviews with hospital staff, and the published literature. The model suggested that transmission resulting from contact with healthcare workers was common, and that certain transmission points (housing wards, diagnostics room, and the intensive care unit) presented higher risk for transmission than others (lobby and surgery). Sensitivity analyses using a range of parameter values demonstrated that the risk of acquisition of colonization by resistant pathogens decreased with shorter patient hospital stays (P<0.0001), more frequent decontamination of transmission points and disinfection of healthcare workers (P<0.0001) and better compliance of healthcare workers with hygiene practices (P<0.0001). More frequent decontamination of heavily trafficked transmission points was especially effective at reducing transmission of the model pathogen.

Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections

The successful treatment of bacterial infections is the product of a collaboration between antibiotics and the host's immune defenses. Nevertheless, in the design of antibiotic treatment regimens, few studies have explored the combined action of antibiotics and the immune response to clearing infections. Here, we use mathematical models to examine the collective contribution of antibiotics and the immune response to the treatment of acute, self-limiting bacterial infections. Our models incorporate the pharmacokinetics and pharmacodynamics of the antibiotics, the innate and adaptive immune responses, and the population and evolutionary dynamics of the target bacteria. We consider two extremes for the antibiotic-immune relationship: one in which the efficacy of the immune response in clearing infections is directly proportional to the density of the pathogen; the other in which its action is largely independent of this density. We explore the effect of antibiotic dose, dosing frequency, and term of treatment on the time before clearance of the infection and the likelihood of antibiotic-resistant bacteria emerging and ascending. Our results suggest that, under most conditions, high dose, full-term therapy is more effective than more moderate dosing in promoting the clearance of the infection and decreasing the likelihood of emergence of antibiotic resistance. Our results also indicate that the clinical and evolutionary benefits of increasing antibiotic dose are not indefinite. We discuss the current status of data in support of and in opposition to the predictions of this study, consider those elements that require additional testing, and suggest how they can be tested.

Impact of visitors and hospital staff on nosocomial transmission and spread to community

We develop a deterministic meta-population model to qualitatively capture some key features of disease transmission between a community and its healthcare facility. We consider the disease transmission dynamics within a healthcare facility and between the healthcare facility and its community. The focus of this study is to quantify the roles of the healthcare workers at and visitors to this healthcare facility in shaping the transmission dynamics during a disease outbreak. We stratify the total population into the general population in the community and the healthcare workers and visitors in the healthcare facility, to account for nosocomial transmission in the case when an individual in the community may be exposed to an infection due to a visit to the healthcare facility. Equilibrium stability analysis is carried out to inform long-term outcomes of disease dynamics in the coupled community-health care facility system. The basic reproduction number is calculated and its dependence on the waiting time and various disease transmission rates is analyzed. Numerical simulations are performed with pertussis as the disease in question. The results show that waiting time only affects the peak number of infections in the waiting reception area. The results also indicate that transmission rate of infective residents in the community and the transmission rate of the infective visitors at the healthcare facility have decisive impact on disease eradication/persistence of the coupled system; while other modes of transmissions are less important, affecting the peak number of infections at best.