91 resultados para Infectious disease dynamics
em BORIS: Bern Open Repository and Information System - Berna - Suiça
Resumo:
Objective. To identify current outpatient parenteral antibiotic therapy practice patterns and complications. Methods. We administered an 11-question survey to adult infectious disease physicians participating in the Emerging Infections Network (EIN), a Centers for Disease Control and Prevention-sponsored sentinel event surveillance network in North America. The survey was distributed electronically or via facsimile in November and December 2012. Respondent demographic characteristics were obtained from EIN enrollment data. Results. Overall, 555 (44.6%) of EIN members responded to the survey, with 450 (81%) indicating that they treated 1 or more patients with outpatient parenteral antimicrobial therapy (OPAT) during an average month. Infectious diseases consultation was reported to be required for a patient to be discharged with OPAT by 99 respondents (22%). Inpatient (282 [63%] of 449) and outpatient (232 [52%] of 449) infectious diseases physicians were frequently identified as being responsible for monitoring laboratory results. Only 26% (118 of 448) had dedicated OPAT teams at their clinical site. Few infectious diseases physicians have systems to track errors, adverse events, or "near misses" associated with OPAT (97 [22%] of 449). OPAT-associated complications were perceived to be rare. Among respondents, 80% reported line occlusion or clotting as the most common complication (occurring in 6% of patients or more), followed by nephrotoxicity and rash (each reported by 61%). Weekly laboratory monitoring of patients who received vancomycin was reported by 77% of respondents (343 of 445), whereas 19% of respondents (84 of 445) reported twice weekly laboratory monitoring for these patients. Conclusions. Although use of OPAT is common, there is significant variation in practice patterns. More uniform OPAT practices may enhance patient safety.
Resumo:
Infectious diseases result from the interactions of host, pathogens, and, in the case of vector-borne diseases, also vectors. The interactions involve physiological and ecological mechanisms and they have evolved under a given set of environmental conditions. Environmental change, therefore, will alter host-pathogen-vector interactions and, consequently, the distribution, intensity, and dynamics of infectious diseases. Here, we review how climate change may impact infectious diseases of aquatic and terrestrial wildlife. Climate change can have direct impacts on distribution, life cycle, and physiological status of hosts, pathogens and vectors. While a change in either host, pathogen or vector does not necessarily translate into an alteration of the disease, it is the impact of climate change on the interactions between the disease components which is particularly critical for altered disease risks. Finally, climate factors can modulate disease through modifying the ecological networks host-pathogen-vector systems are belonging to, and climate change can combine with other environmental stressors to induce cumulative effects on infectious diseases. Overall, the influence of climate change on infectious diseases involves different mechanisms, it can be modulated by phenotypic acclimation and/or genotypic adaptation, it depends on the ecological context of the host-pathogen-vector interactions, and it can be modulated by impacts of other stressors. As a consequence of this complexity, non-linear responses of disease systems under climate change are to be expected. To improve predictions on climate change impacts on infectious disease, we suggest that more emphasis should be given to the integration of biomedical and ecological research for studying both the physiological and ecological mechanisms which mediate climate change impacts on disease, and to the development of harmonized methods and approaches to obtain more comparable results, as this would support the discrimination of case-specific versus general mechanisms
Resumo:
Infectious disease outbreaks can be devastating because of their sudden occurrence, as well as the complexity of monitoring and controlling them. Outbreaks in wildlife are even more challenging to observe and describe, especially when small animals or secretive species are involved. Modeling such infectious disease events is relevant to investigating their dynamics and is critical for decision makers to accomplish outbreak management. Tularemia, caused by the bacterium Francisella tularensis, is a potentially lethal zoonosis. Of the few animal outbreaks that have been reported in the literature, only those affecting zoo animals have been closely monitored. Here, we report the first estimation of the basic reproduction number R0 of an outbreak in wildlife caused by F. tularensis using quantitative modeling based on a susceptible-infected-recovered framework. We applied that model to data collected during an extensive investigation of an outbreak of tularemia caused by F. tularensis subsp. holarctica (also designated as type B) in a closely monitored, free-roaming house mouse (Mus musculus domesticus) population in Switzerland. Based on our model and assumptions, the best estimated basic reproduction number R0 of the current outbreak is 1.33. Our results suggest that tularemia can cause severe outbreaks in small rodents. We also concluded that the outbreak self-exhausted in approximately three months without administrating antibiotics.
Resumo:
Chlamydia trachomatis is the most common bacterial sexually transmitted infection (STI) in many developed countries. The highest prevalence rates are found among young adults who have frequent partner change rates. Three published individual-based models have incorporated a detailed description of age-specific sexual behaviour in order to quantify the transmission of C. trachomatis in the population and to assess the impact of screening interventions. Owing to varying assumptions about sexual partnership formation and dissolution and the great uncertainty about critical parameters, such models show conflicting results about the impact of preventive interventions. Here, we perform a detailed evaluation of these models by comparing the partnership formation and dissolution dynamics with data from Natsal 2000, a population-based probability sample survey of sexual attitudes and lifestyles in Britain. The data also allow us to describe the dispersion of C. trachomatis infections as a function of sexual behaviour, using the Gini coefficient. We suggest that the Gini coefficient is a useful measure for calibrating infectious disease models that include risk structure and highlight the need to estimate this measure for other STIs.
Resumo:
Recently, the Centre for Immunity, Infection and Evolution sponsored a one-day symposium entitled "Wild Immunology." The CIIE is a new Wellcome Trust-funded initiative with the remit to connect evolutionary biology and ecology with research in immunology and infectious diseases in order to gain an interdisciplinary perspective on challenges to global health. The central question of the symposium was, "Why should we try to understand infection and immunity in wild systems?" Specifically, how does the immune response operate in the wild and how do multiple coinfections and commensalism affect immune responses and host health in these wild systems? The symposium brought together a broad program of speakers, ranging from laboratory immunologists to infectious disease ecologists, working on wild birds, unmanaged animals, wild and laboratory rodents, and on questions ranging from the dynamics of coinfection to how commensal bacteria affect the development of the immune system. The meeting on wild immunology, organized by Amy Pedersen, Simon Babayan, and Rick Maizels, was held at the University of Edinburgh on 30 June 2011.
Resumo:
In this study, we describe the isolation of Laribacter hongkongensis, a recently described genus and species of bacterium, in pure culture on charcoal cefoperazone deoxycholate agar from the stool of six patients with diarrhea. Three patients were residents of Hong Kong, and three of Switzerland. In none of the stool samples obtained from these six patients was Salmonella, Shigella, enterohemorrhagic Escherichia coli, Vibrio, Aeromonas, Plesiomonas, or Campylobacter recovered. Rotavirus antigen detection, electron microscopic examination for viruses, and microscopic examinations for ova and cysts were all negative for the stool samples obtained from the three patients in Hong Kong. Enterotoxigenic E. coli was recovered from one of the patients in Hong Kong. Unlike L. hongkongensis type strain HKU1, all the six strains were motile with bipolar flagellae. Sequencing of the 16S ribosomal RNA genes of the six strains showed that they all had sequences with only 0-2 base differences to that of the type strain. Pulsed field gel electrophoresis of the SpeI digested genomic DNA of the six isolates and that of the type strain revealed that the seven isolates were genotypically unrelated strains. More extensive epidemiologic studies should be carried out to ascertain the causative association between L. hongkongensis and diarrhea and to define the reservoir and modes of transmission of L. hongkongensis.
Resumo:
Molecular data are now widely used in epidemiological studies to investigate the transmission, distribution, biology, and diversity of pathogens. Our objective was to establish recommendations to support good scientific reporting of molecular epidemiological studies to encourage authors to consider specific threats to valid inference. The statement Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID) builds upon the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative. The STROME-ID statement was developed by a working group of epidemiologists, statisticians, bioinformaticians, virologists, and microbiologists with expertise in control of infection and communicable diseases. The statement focuses on issues relating to the reporting of epidemiological studies of infectious diseases using molecular data that were not addressed by STROBE. STROME-ID addresses terminology, measures of genetic diversity within pathogen populations, laboratory methods, sample collection, use of molecular markers, molecular clocks, timeframe, multiple-strain infections, non-independence of infectious-disease data, missing data, ascertainment bias, consistency between molecular and epidemiological data, and ethical considerations with respect to infectious-disease research. In total, 20 items were added to the 22 item STROBE checklist. When used, the STROME-ID recommendations should advance the quality and transparency of scientific reporting, with clear benefits for evidence reviews and health-policy decision making.
Resumo:
Demographic composition and dynamics of animal and human populations are important determinants for the transmission dynamics of infectious disease and for the effect of infectious disease or environmental disasters on productivity. In many circumstances, demographic data are not available or of poor quality. Since 1999 Switzerland has been recording cattle movements, births, deaths and slaughter in an animal movement database (AMD). The data present in the AMD offers the opportunity for analysing and understanding the dynamic of the Swiss cattle population. A dynamic population model can serve as a building block for future disease transmission models and help policy makers in developing strategies regarding animal health, animal welfare, livestock management and productivity. The Swiss cattle population was therefore modelled using a system of ordinary differential equations. The model was stratified by production type (dairy or beef), age and gender (male and female calves: 0-1 year, heifers and young bulls: 1-2 years, cows and bulls: older than 2 years). The simulation of the Swiss cattle population reflects the observed pattern accurately. Parameters were optimized on the basis of the goodness-of-fit (using the Powell algorithm). The fitted rates were compared with calculated rates from the AMD and differed only marginally. This gives confidence in the fitted rates of parameters that are not directly deductible from the AMD (e.g. the proportion of calves that are moved from the dairy system to fattening plants).
Resumo:
Multidrug-resistant (MDR) cytomegalovirus (CMV) emerged after transient responses to ganciclovir, foscarnet, and cidofovir in a CMV-seropositive recipient who underwent allogeneic hematopoietic stem cell transplantation from a CMV-seronegative donor. Experimental treatments using leflunomide and artesunate failed. Re-transplantation from a CMV-seropositive donor supported by adoptive transfer of pp65-specific T cells and maribavir was followed by lasting suppression. This case illustrates that successful MDR CMV therapy may require individualized multidisciplinary approaches. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Resumo:
Foot-and-mouth disease (FMD) is a highly contagious disease that caused several large outbreaks in Europe in the last century. The last important outbreak in Switzerland took place in 1965/66 and affected more than 900 premises and more than 50,000 animals were slaughtered. Large-scale emergency vaccination of the cattle and pig population has been applied to control the epidemic. In recent years, many studies have used infectious disease models to assess the impact of different disease control measures, including models developed for diseases exotic for the specific region of interest. Often, the absence of real outbreak data makes a validation of such models impossible. This study aimed to evaluate whether a spatial, stochastic simulation model (the Davis Animal Disease Simulation model) can predict the course of a Swiss FMD epidemic based on the available historic input data on population structure, contact rates, epidemiology of the virus, and quality of the vaccine. In addition, the potential outcome of the 1965/66 FMD epidemic without application of vaccination was investigated. Comparing the model outcomes to reality, only the largest 10% of the simulated outbreaks approximated the number of animals being culled. However, the simulation model highly overestimated the number of culled premises. While the outbreak duration could not be well reproduced by the model compared to the 1965/66 epidemic, it was able to accurately estimate the size of the area infected. Without application of vaccination, the model predicted a much higher mean number of culled animals than with vaccination, demonstrating that vaccination was likely crucial in disease control for the Swiss FMD outbreak in 1965/66. The study demonstrated the feasibility to analyze historical outbreak data with modern analytical tools. However, it also confirmed that predicted epidemics from a most carefully parameterized model cannot integrate all eventualities of a real epidemic. Therefore, decision makers need to be aware that infectious disease models are useful tools to support the decision-making process but their results are not equal valuable as real observations and should always be interpreted with caution.
Resumo:
Syphilis (lues), a chronic infectious disease caused by Treponema pallidum, has been increasing in incidence during the last few years. Therefore, while clinically it is often not suspected, syphilis is increasingly becoming a differential diagnosis in routine pathology.
Resumo:
The objective of this study was to characterize empirically the association between vaccination coverage and the size and occurrence of measles epidemics in Germany. In order to achieve this we analysed data routinely collected by the Robert Koch Institute, which comprise the weekly number of reported measles cases at all ages as well as estimates of vaccination coverage at the average age of entry into the school system. Coverage levels within each federal state of Germany are incorporated into a multivariate time-series model for infectious disease counts, which captures occasional outbreaks by means of an autoregressive component. The observed incidence pattern of measles for all ages is best described by using the log proportion of unvaccinated school starters in the autoregressive component of the model.