Modeling nosocomial transmission of rotavirus in pediatric wards

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.

Modelling the transmission of healthcare associated infections: a systematic review

Background

Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time.

Methods

MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings.

Results

In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.

The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data.

Conclusions

Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.

Infecciones nosocomiales en pediatría

Numerosas son las particularidades de la población pediátrica que deben intervenir, en nuestra opinión, en el control de las infecciones nosocomiales. Los riesgos y los agentes patógenos responsables son diferentes en función del tipo de población (prematuros, recién nacidos, otros). Además, esta población, que comparte los mismos factores de riesgo de infecciones nosocomiales que la población adulta (hospitalización en reanimación, cateterismos, etc.), se distingue no sólo por la inmadurez del sistema inmunitario de los recién nacidos, sino también por la multiplicidad de los participantes, desde los sanitarios hasta los padres, pasando por los educadores y los acompañantes (visitantes de todo tipo, etc.) necesarios para el desarrollo conductual y emocional del niño. Además, es importante subrayar el aumento del riesgo ligado a los contactos frecuentes, cercanos e íntimos que están parcial e incluso totalmente ausentes en el ámbito hospitalario «adulto». Así como existen riesgos de transmisión cruzada a través del principal vector constituido por los sanitarios, el control del riesgo no puede excluir a los educadores, a los acompañantes, a los padres y a los mismos niños. Si el riesgo en la esfera adulta está limitado a las actividades médicas, en la esfera pediátrica se comparte con las demás actividades (juegos, enseñanza, etc.), que con frecuencia son comunes. Todos estos riesgos son todavía mayores debido a la prevalencia de los agentes patógenos como los virus (respiratorios y digestivos), la frecuencia de las antibioticoterapias y la dificultad de los diagnósticos etiológicos, dada la inespecificidad de los signos clínicos y la actitud diagnóstica poco o nada invasiva. De esta manera, el control del riesgo infeccioso nosocomial se resume en los siguientes elementos: un reservorio importante y difícilmente identificable, numerosos vectores potenciales, una población expuesta de manera variable al riesgo, todo ello sin olvidar las necesidades emocionales de los niños y los comportamientos «culturales».

The Regional Healthcare Ecosystem Analyst (RHEA): a simulation modeling tool to assist infectious disease control in a health system

OBJECTIVE

As healthcare systems continue to expand and interconnect with each other through patient sharing, administrators, policy makers, infection control specialists, and other decision makers may have to take account of the entire healthcare 'ecosystem' in infection control.

MATERIALS AND METHODS

We developed a software tool, the Regional Healthcare Ecosystem Analyst (RHEA), that can accept user-inputted data to rapidly create a detailed agent-based simulation model (ABM) of the healthcare ecosystem (ie, all healthcare facilities, their adjoining community, and patient flow among the facilities) of any region to better understand the spread and control of infectious diseases.

RESULTS

To demonstrate RHEA's capabilities, we fed extensive data from Orange County, California, USA, into RHEA to create an ABM of a healthcare ecosystem and simulate the spread and control of methicillin-resistant Staphylococcus aureus. Various experiments explored the effects of changing different parameters (eg, degree of transmission, length of stay, and bed capacity).

DISCUSSION

Our model emphasizes how individual healthcare facilities are components of integrated and dynamic networks connected via patient movement and how occurrences in one healthcare facility may affect many other healthcare facilities.

CONCLUSIONS

A decision maker can utilize RHEA to generate a detailed ABM of any healthcare system of interest, which in turn can serve as a virtual laboratory to test different policies and interventions.