Modeling Nosocomial Transmission of Rotavirus in Pediatric Wards

Kribs-Zaleta, Christopher M.; Jusot, Jean François; Vanhems, Philippe; Charles, Sandrine

doi:10.1007/s11538-010-9570-z

Cited by 7 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is especially true for the description of the transmission dynamics of infectious diseases ranging from measles and pertussis to gonorrhea and in the prediction of the effects of public health interventions such as treatment and vaccination on these dynamics (Anderson and May 8 , Keeling and Rohani 9 ). In the last two decades, mathematical modeling has provided a very useful means to study the transmission dynamics of nosocomial pathogens in hospitals, including investigations of patient and health care worker (HCW) contact patterns, and HCW- and patient-mediated transmission, we refer to Austin and Anderson 10 , Bergstrom et al 11 , Bonten et al 12 , Chamchod and Ruan 13 , Cooper et al 14 , D’Agata et al 15 – 17 , Grundmann and Hellriegel 18 , Kribs-Zaleta et al 19 , Plipat et al 20 , Smith et al 21 , Webb et al. 22 , 23 , Lipsitch et al 24 , and the references cited therein.…”

Section: Introductionmentioning

confidence: 99%

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination*

Wang

Ruan

2017

Sci Rep

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination*

Wang

Ruan

2017

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Several studies developed both a stochastic and a deterministic version of a similar compartmental model to investigate whether projected intervention effects were partly a result of random fluctuation [18,35,40,[78][79][80]. Others use a deterministic model to interpret the findings of a stochastic model [81].…”

Section: Force Of Infectionmentioning

confidence: 99%

“…Metrics used to quantify goodness of fit include the least square criterion (minimisation of sums of squares between the observed data and the model predictions) [21,56,57,75], maximum likelihood estimation (identification of the parameter value(s) that makes the observed data most likely) [18,22,24,35,53,63,65,66] and since 2007, Bayesian methods; frequently using Markov Chain Monte Carlo (MCMC) approaches [19,32,40,41,50,58,64,76] or a combination of MCMC and maximum likelihood estimation [36,59]. A further seven studies reported fitting their models by comparing model predictions to observed epidemiological data but did not apply any formal quantitative approach [17,29,43,60,81,101,104].…”

Section: Model Fitting To Datamentioning

confidence: 99%

“…Of the 35 studies that have investigated parameter uncertainty, univariate sensitivity analysis (i.e. alteration of one parameter at a time whilst holding others at their base-case value) was conducted by 43% (15 studies) [18,28,29,43,44,46,60,63,69,77,81,83,89,91,99]. The more computationally expensive probabilistic sensitivity analysis (formulation of uncertainty in the model inputs by a joint probability distribution, and propagating this uncertainty to the outputs [106]) is in general considered a rigorous method to account for uncertainty in the joint distribution of the parameters.…”

Section: Uncertainty In Model Predictionsmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the transmission of healthcare associated infections: a systematic review

et al. 2013

View full text Add to dashboard Cite

BackgroundDynamic 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.MethodsMEDLINE, 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.ResultsIn 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.ConclusionsTransmission 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.

show abstract

“…En este contexto, destacan dos limitaciones: organizativa, debida a la falta de una suficiente zona geográfica, y humana, por falta de personal. Como el riesgo de transmisión se debe no sólo a los pacientes sintomáticos sino también a los pacientes asintomáticos, cabe la posibilidad, en período epidémico, de que las medidas fracasen [33] .…”

Section: Virus Respiratorio Sincitialunclassified

Infecciones nosocomiales en pediatría

Burgard

Grall²,

Descamps

et al. 2013

EMC - Pediatría

View full text Add to dashboard Cite

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». © 2013 Elsevier Masson SAS. Todos los derechos reservados. Palabras clave: Infecciones víricas; Recién nacido; Vacunación Plan ■ Introducción 2 EMC -Pediatría

show abstract

Modeling Nosocomial Transmission of Rotavirus in Pediatric Wards

Cited by 7 publications

References 36 publications

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination*

Modeling Nosocomial Infections of Methicillin-Resistant Staphylococcus aureus with Environment Contamination*

Modelling the transmission of healthcare associated infections: a systematic review

Infecciones nosocomiales en pediatría

Contact Info

Product

Resources

About