A Simulation-Based Assessment of Strategies to Control Clostridium Difficile Transmission and Infection

Background

Clostridium difficile is one of the most common and important nosocomial pathogens, causing severe gastrointestinal disease in hospitalized patients. Although "bundled" interventions have been proposed and promoted, optimal control strategies remain unknown.

Methods

We designed an agent-based computer simulation of nosocomial C. difficile transmission and infection, which included components such as: patients and health care workers, and their interactions; room contamination via C. difficile shedding; C. difficile hand carriage and removal via hand hygiene; patient acquisition of C. difficile via contact with contaminated rooms or health care workers; and patient antimicrobial use. We then introduced six interventions, alone and "bundled" together: aggressive C. difficile testing; empiric isolation and treatment of symptomatic patients; improved adherence to hand hygiene and contact precautions; improved use of soap and water for hand hygiene; and improved environmental cleaning. All interventions were tested using values representing base-case, typical intervention, and optimal intervention scenarios.

Findings

In the base-case scenario, C. difficile infection rates ranged from 8–21 cases/10,000 patient-days, with a case detection fraction between 32%–50%. Implementing the "bundle" at typical intervention levels had a large impact on C. difficile acquisition and infection rates, although intensifying the intervention to optimal levels had much less additional impact. Most of the impact came from improved hand hygiene and empiric isolation and treatment of suspected C. difficile cases.

Conclusion

A "bundled" intervention is likely to reduce nosocomial C. difficile infection rates, even under typical implementation conditions. Real-world implementation of the "bundle" should focus on those components of the intervention that are likely to produce the greatest impact on C. difficile infection rates, such as hand hygiene and empiric isolation and treatment of suspected cases.     

Cost-Effectiveness Analysis of Six Strategies to Treat Recurrent Clostridium difficile Infection

Objective

To assess the cost-effectiveness of six treatment strategies for patients diagnosed with recurrent Clostridium difficile infection (CDI) in Canada: 1. oral metronidazole; 2. oral vancomycin; 3.oral fidaxomicin; 4. fecal transplantation by enema; 5. fecal transplantation by nasogastric tube; and 6. fecal transplantation by colonoscopy.

Perspective

Public insurer for all hospital and physician services.

Setting

Ontario, Canada.

Methods

A decision analytic model was used to model costs and lifetime health effects of each strategy for a typical patient experiencing up to three recurrences, over 18 weeks. Recurrence data and utilities were obtained from published sources. Cost data was obtained from published sources and hospitals in Toronto, Canada. The willingness-to-pay threshold was $50,000/QALY gained.

Results

Fecal transplantation by colonoscopy dominated all other strategies in the base case, as it was less costly and more effective than all alternatives. After accounting for uncertainty in all model parameters, there was an 87% probability that fecal transplantation by colonoscopy was the most beneficial strategy. If colonoscopy was not available, fecal transplantation by enema was cost-effective at $1,708 per QALY gained, compared to metronidazole. In addition, fecal transplantation by enema was the preferred strategy if the probability of recurrence following this strategy was below 8.7%. If fecal transplantation by any means was unavailable, fidaxomicin was cost-effective at an additional cost of $25,968 per QALY gained, compared to metronidazole.

Conclusion

Fecal transplantation by colonoscopy (or enema, if colonoscopy is unavailable) is cost-effective for treating recurrent CDI in Canada. Where fecal transplantation is not available, fidaxomicin is also cost-effective.     

Optimal Population-Level Infection Detection Strategies for Malaria Control and Elimination in a Spatial Model of Malaria Transmission

Mass campaigns with antimalarial drugs are potentially a powerful tool for local elimination of malaria, yet current diagnostic technologies are insufficiently sensitive to identify all individuals who harbor infections. At the same time, overtreatment of uninfected individuals increases the risk of accelerating emergence of drug resistance and losing community acceptance. Local heterogeneity in transmission intensity may allow campaign strategies that respond to index cases to successfully target subpatent infections while simultaneously limiting overtreatment. While selective targeting of hotspots of transmission has been proposed as a strategy for malaria control, such targeting has not been tested in the context of malaria elimination. Using household locations, demographics, and prevalence data from a survey of four health facility catchment areas in southern Zambia and an agent-based model of malaria transmission and immunity acquisition, a transmission intensity was fit to each household based on neighborhood age-dependent malaria prevalence. A set of individual infection trajectories was constructed for every household in each catchment area, accounting for heterogeneous exposure and immunity. Various campaign strategies—mass drug administration, mass screen and treat, focal mass drug administration, snowball reactive case detection, pooled sampling, and a hypothetical serological diagnostic—were simulated and evaluated for performance at finding infections, minimizing overtreatment, reducing clinical case counts, and interrupting transmission. For malaria control, presumptive treatment leads to substantial overtreatment without additional morbidity reduction under all but the highest transmission conditions. Compared with untargeted approaches, selective targeting of hotspots with drug campaigns is an ineffective tool for elimination due to limited sensitivity of available field diagnostics. Serological diagnosis is potentially an effective tool for malaria elimination but requires higher coverage to achieve similar results to mass distribution of presumptive treatment.     

Non-Lethal Control of the Cariogenic Potential of an Agent-Based Model for Dental Plaque

Dental caries or tooth decay is a prevalent global disease whose causative agent is the oral biofilm known as plaque. According to the ecological plaque hypothesis, this biofilm becomes pathogenic when external challenges drive it towards a state with a high proportion of acid-producing bacteria. Determining which factors control biofilm composition is therefore desirable when developing novel clinical treatments to combat caries, but is also challenging due to the system complexity and the existence of multiple bacterial species performing similar functions. Here we employ agent-based mathematical modelling to simulate a biofilm consisting of two competing, distinct types of bacterial populations, each parameterised by their nutrient uptake and aciduricity, periodically subjected to an acid challenge resulting from the metabolism of dietary carbohydrates. It was found that one population was progressively eliminated from the system to give either a benign or a pathogenic biofilm, with a tipping point between these two fates depending on a multiplicity of factors relating to microbial physiology and biofilm geometry. Parameter sensitivity was quantified by individually varying the model parameters against putative experimental measures, suggesting non-lethal interventions that can favourably modulate biofilm composition. We discuss how the same parameter sensitivity data can be used to guide the design of validation experiments, and argue for the benefits of in silico modelling in providing an additional predictive capability upstream from in vitro experiments.     

An Agent-Based Model of the Response to Angioplasty and Bare-Metal Stent Deployment in an Atherosclerotic Blood Vessel

Purpose

While animal models are widely used to investigate the development of restenosis in blood vessels following an intervention, computational models offer another means for investigating this phenomenon. A computational model of the response of a treated vessel would allow investigators to assess the effects of altering certain vessel- and stent-related variables. The authors aimed to develop a novel computational model of restenosis development following an angioplasty and bare-metal stent implantation in an atherosclerotic vessel using agent-based modeling techniques. The presented model is intended to demonstrate the body’s response to the intervention and to explore how different vessel geometries or stent arrangements may affect restenosis development.

Methods

The model was created on a two-dimensional grid space. It utilizes the post-procedural vessel lumen diameter and stent information as its input parameters. The simulation starting point of the model is an atherosclerotic vessel after an angioplasty and stent implantation procedure. The model subsequently generates the final lumen diameter, percent change in lumen cross-sectional area, time to lumen diameter stabilization, and local concentrations of inflammatory cytokines upon simulation completion. Simulation results were directly compared with the results from serial imaging studies and cytokine levels studies in atherosclerotic patients from the relevant literature.

Results

The final lumen diameter results were all within one standard deviation of the mean lumen diameters reported in the comparison studies. The overlapping-stent simulations yielded results that matched published trends. The cytokine levels remained within the range of physiological levels throughout the simulations.

Conclusion

We developed a novel computational model that successfully simulated the development of restenosis in a blood vessel following an angioplasty and bare-metal stent deployment based on the characteristics of the vessel cross-section and stent. A further development of this model could ultimately be used as a predictive tool to depict patient outcomes and inform treatment options.     

Economic Analysis of Vaccination Strategies for PRRS Control

Porcine reproductive and respiratory syndrome virus (PRRSv) is a swine-specific pathogen that causes significant increases in production costs. When a breeding herd becomes infected, in an attempt to hasten control and elimination of PRRSv, some veterinarians have adopted a strategy called load-close-expose which consists of interrupting replacement pig introductions into the herd for several weeks (herd closure) and exposing the whole herd to a replicating PRRSv to boost herd immunity. Either modified-live virus (MLV) vaccine or live field-virus inoculation (FVI) is used. This study consisted of partial budget analyses to compare MLV to FVI as the exposure method of load-close-expose program to control and eliminate PRRSv from infected breeding herds, and secondly to estimate benefit / cost of vaccinating sow herds preventatively. Under the assumptions used in this study, MLV held economic advantage over FVI. However, sensitivity analysis revealed that decreasing margin over variable costs below $ 47.32, or increasing PRRSv-attributed cost above $18.89 or achieving time-to-stability before 25 weeks resulted in advantage of FVI over MLV. Preventive vaccination of sow herds was beneficial when the frequency of PRRSv infection was at least every 2.1 years. The economics of preventative vaccination was minimally affected by cost attributed to field-type PRRSv infection on growing pigs or by the breeding herd productivity level. The models developed and described in this paper provide valuable tools to assist veterinarians in their efforts to control PRRSv.     

An Agent-Based Model to study the epidemiological and evolutionary dynamics of Influenza viruses

Background  

Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.     

Optimal Control Strategies and Sensitivity Analysis of an HIV/AIDS-Resistant Model with Behavior Change

Acta Biotheoretica - Despite several research on HIV/AIDS, it is still incumbent to investigate more effective control measures to mitigate its infection level. Therefore, we introduce an...     

An Agent-Based Model of Private Woodland Owner Management Behavior Using Social Interactions,Information Flow,and Peer-To-Peer Networks

Privately owned woodlands are an important source of timber and ecosystem services in North America and worldwide. Impacts of management on these ecosystems and timber supply from these woodlands are difficult to estimate because complex behavioral theory informs the owner’s management decisions. The decision-making environment consists of exogenous market factors, internal cognitive processes, and social interactions with fellow landowners, foresters, and other rural community members. This study seeks to understand how social interactions, information flow, and peer-to-peer networks influence timber harvesting behavior using an agent-based model. This theoretical model includes forested polygons in various states of ‘harvest readiness’ and three types of agents: forest landowners, foresters, and peer leaders (individuals trained in conservation who use peer-to-peer networking). Agent rules, interactions, and characteristics were parameterized with values from existing literature and an empirical survey of forest landowner attitudes, intentions, and demographics. The model demonstrates that as trust in foresters and peer leaders increases, the percentage of the forest that is harvested sustainably increases. Furthermore, peer leaders can serve to increase landowner trust in foresters. Model output and equations will inform forest policy and extension/outreach efforts. The model also serves as an important testing ground for new theories of landowner decision making and behavior.     

Need-Based Transfers Enhance Resilience to Shocks: An Agent-Based Model of a Maasai Risk-Pooling System

Human Ecology - Maasai and other Maa-speaking pastoralists in Kenya and Tanzania have a risk-pooling system that they refer to by their word for the umbilical cord (osotua). Gifts from one osotua...     

Risk-Pooling and Herd Survival: An Agent-Based Model of a Maasai Gift-Giving System

We use agent-based modeling to study osotua, a gift giving system used by the Maasai of East Africa. Osotua’s literal meaning is “umbilical cord,” but it is used metaphorically to refer to a specific type of gift-giving relationship. Osotua relationships are characterized by respect, responsibility and restraint. Osotua partners ask each other for help only if they are in need and provide help only when asked and only if they are able. We hypothesize that under the ecologically volatile conditions in which Maasai pastoralists have traditionally lived, such a system is particularly suited to risk pooling. Here we explore whether osotua increases the viability of herds by comparing herd survivorship and stability under osotua rules to a) no exchange and b) probabilistic rules for requesting and giving livestock. Results from this model suggest that this gift-giving system can dramatically increase herd longevity through a limited pooling of risk.     

嗅觉系统神经网络模型的模拟与动力学特性分析

在哺乳动物嗅觉系统的拓扑结构及生理实验的基础上建立了一套非线性动力学神经网络模型．此模型在模拟嗅觉神经系统方面有着突出的优点,同时在信号处理以及模式识别中表现出了奇异的混沌特性．着重描述了K系列模型的非线性动力学特性,并通过数值模拟进行分析．     

An Agent-Based Model of Centralized Institutions,Social Network Technology,and Revolution

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.     

Context Matters—Using an Agent-Based Model to Investigate the Influence of Market Context on the Supply of Local Biomass for Anaerobic Digestion

Biogas plant managers often face difficulties in obtaining feedstock at stable and affordable prices. The context in which the biogas plant manager needs to purchase the feedstock could be important when the biomass is also used in valorization trajectories besides anaerobic digestion. Using a combination of qualitative research and agent-based modelling, we investigated the effect of market context on the purchase of local biomass for anaerobic digestion. This paper details the institutional arrangements of our case study, the silage maize market in Flanders and the results of a scenario analysis, simulating nine different market contexts. Silage maize is an interesting case study, as it is both used for feed by farmers and as an input in biogas plants. The results show that mainly the time of entry into the market explains the difficulties in obtaining a stable supply of silage maize to biogas plants. Furthermore, we found a silage maize price increase for farmers in competition with a biogas plant, especially in case of a silage maize deficit in the market. The different institutional arrangements used have no significant effect. Our findings may guide biogas plant managers in assessing and reducing the consequences of the establishment of a biogas plant, competing for local biomass resources.     

Examining the Relationship between Pre-Malignant Breast Lesions,Carcinogenesis and Tumor Evolution in the Mammary Epithelium Using an Agent-Based Model

Introduction

Breast cancer, the product of numerous rare mutational events that occur over an extended time period, presents numerous challenges to investigators interested in studying the transformation from normal breast epithelium to malignancy using traditional laboratory methods, particularly with respect to characterizing transitional and pre-malignant states. Dynamic computational modeling can provide insight into these pathophysiological dynamics, and as such we use a previously validated agent-based computational model of the mammary epithelium (the DEABM) to investigate the probabilistic mechanisms by which normal populations of ductal cells could transform into states replicating features of both pre-malignant breast lesions and a diverse set of breast cancer subtypes.

Methods

The DEABM consists of simulated cellular populations governed by algorithms based on accepted and previously published cellular mechanisms. Cells respond to hormones, undergo mitosis, apoptosis and cellular differentiation. Heritable mutations to 12 genes prominently implicated in breast cancer are acquired via a probabilistic mechanism. 3000 simulations of the 40-year period of menstrual cycling were run in wild-type (WT) and BRCA1-mutated groups. Simulations were analyzed by development of hyperplastic states, incidence of malignancy, hormone receptor and HER-2 status, frequency of mutation to particular genes, and whether mutations were early events in carcinogenesis.

Results

Cancer incidence in WT (2.6%) and BRCA1-mutated (45.9%) populations closely matched published epidemiologic rates. Hormone receptor expression profiles in both WT and BRCA groups also closely matched epidemiologic data. Hyperplastic populations carried more mutations than normal populations and mutations were similar to early mutations found in ER+ tumors (telomerase, E-cadherin, TGFB, RUNX3, p < .01). ER- tumors carried significantly more mutations and carried more early mutations in BRCA1, c-MYC and genes associated with epithelial-mesenchymal transition.

Conclusions

The DEABM generates diverse tumors that express tumor markers consistent with epidemiologic data. The DEABM also generates non-invasive, hyperplastic populations, analogous to atypia or ductal carcinoma in situ (DCIS), via mutations to genes known to be present in hyperplastic lesions and as early mutations in breast cancers. The results demonstrate that agent-based models are well-suited to studying tumor evolution through stages of carcinogenesis and have the potential to be used to develop prevention and treatment strategies.     

The Development of an Age-Structured Model for Trachoma Transmission Dynamics,Pathogenesis and Control

Background

Trachoma, the worldwide leading infectious cause of blindness, is due to repeated conjunctival infection with Chlamydia trachomatis. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular sequelae require long-term monitoring. We present an age-structured mathematical model of trachoma transmission and disease to predict the impact of interventions on the prevalence of blinding trachoma.

Methodology/Principal Findings

The model is based on the concept of multiple reinfections leading to progressive conjunctival scarring, trichiasis, corneal opacity and blindness. It also includes aspects of trachoma natural history, such as an increasing rate of recovery from infection and a decreasing chlamydial load with subsequent infections that depend upon a (presumed) acquired immunity that clears infection with age more rapidly. Parameters were estimated using maximum likelihood by fitting the model to pre-control infection prevalence data from hypo-, meso- and hyperendemic communities from The Gambia and Tanzania. The model reproduces key features of trachoma epidemiology: 1) the age-profile of infection prevalence, which increases to a peak at very young ages and declines at older ages; 2) a shift in this prevalence peak, toward younger ages in higher force of infection environments; 3) a raised overall profile of infection prevalence with higher force of infection; and 4) a rising profile, with age, of the prevalence of the ensuing severe sequelae (trachomatous scarring, trichiasis), as well as estimates of the number of infections that need to occur before these sequelae appear.

Conclusions/Significance

We present a framework that is sufficiently comprehensive to examine the outcomes of the A (antibiotic) component of the SAFE strategy on disease. The suitability of the model for representing population-level patterns of infection and disease sequelae is discussed in view of the individual processes leading to these patterns.     

具有年龄结构的HIV感染模型稳定性分析

研究了一类具有年龄结构的HIV感染模型,得到了未感染平衡点、感染平衡点局部渐近稳定的充分条件.     

Using Hypnotic Suggestion to Model Loss of Control and Awareness of Movements: An Exploratory fMRI Study

The feeling of voluntary control and awareness of movement is fundamental to our notions of selfhood and responsibility for actions, yet can be lost in neuropsychiatric syndromes (e.g. delusions of control, non-epileptic seizures) and culturally influenced dissociative states (e.g. attributions of spirit possession). The brain processes involved remain poorly understood. We used suggestion and functional magnetic resonance imaging (fMRI) to investigate loss of control and awareness of right hand movements in 15 highly hypnotically suggestible subjects. Loss of perceived control of movements was associated with reduced connectivity between supplementary motor area (SMA) and motor regions. Reduced awareness of involuntary movements was associated with less activation in parietal cortices (BA 7, BA 40) and insula. Collectively these results suggest that the sense of voluntary control of movement may critically depend on the functional coupling of SMA with motor systems, and provide a potential neural basis for the narrowing of awareness reported in pathological and culturally influenced dissociative phenomena.     

Assortative Mating and the Reversal of Gender Inequality in Education in Europe: An Agent-Based Model

While men have always received more education than women in the past, this gender imbalance in education has turned around in large parts of the world. In many countries, women now excel men in terms of participation and success in higher education. This implies that, for the first time in history, there are more highly educated women than men reaching the reproductive ages and looking for a partner. We develop an agent-based computational model that explicates the mechanisms that may have linked the reversal of gender inequality in education with observed changes in educational assortative mating. Our model builds on the notion that individuals search for spouses in a marriage market and evaluate potential candidates based on preferences. Based on insights from earlier research, we assume that men and women prefer partners with similar educational attainment and high earnings prospects, that women tend to prefer men who are somewhat older than themselves, and that men prefer women who are in their mid-twenties. We also incorporate the insight that the educational system structures meeting opportunities on the marriage market. We assess the explanatory power of our model with systematic computational experiments, in which we simulate marriage market dynamics in 12 European countries among individuals born between 1921 and 2012. In these experiments, we make use of realistic agent populations in terms of educational attainment and earnings prospects and validate model outcomes with data from the European Social Survey. We demonstrate that the observed changes in educational assortative mating can be explained without any change in male or female preferences. We argue that our model provides a useful computational laboratory to explore and quantify the implications of scenarios for the future.