999 resultados para Infectious Diseases


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We consider a hybrid model, created by coupling a continuum and an agent-based model of infectious disease. The framework of the hybrid model provides a mechanism to study the spread of infection at both the individual and population levels. This approach captures the stochastic spatial heterogeneity at the individual level, which is directly related to deterministic population level properties. This facilitates the study of spatial aspects of the epidemic process. A spatial analysis, involving counting the number of infectious agents in equally sized bins, reveals when the spatial domain is nonhomogeneous.

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Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply an untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ~2300 molecular features. Principal component analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts.

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Emerging infectious diseases present a complex challenge to public health officials and governments; these challenges have been compounded by rapidly shifting patterns of human behaviour and globalisation. The increase in emerging infectious diseases has led to calls for new technologies and approaches for detection, tracking, reporting, and response. Internet-based surveillance systems offer a novel and developing means of monitoring conditions of public health concern, including emerging infectious diseases. We review studies that have exploited internet use and search trends to monitor two such diseases: influenza and dengue. Internet-based surveillance systems have good congruence with traditional surveillance approaches. Additionally, internet-based approaches are logistically and economically appealing. However, they do not have the capacity to replace traditional surveillance systems; they should not be viewed as an alternative, but rather an extension. Future research should focus on using data generated through internet-based surveillance and response systems to bolster the capacity of traditional surveillance systems for emerging infectious diseases.

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This paper seeks to explain how the selective securitization of infectious disease arose, and to analyze the policy successes from this move. It is argued that despite some success, such as the revised International Health Regulations (IHR) in 2005, there remain serious deficiencies in the political outputs from the securitization of infectious disease.

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Review question/objective The objective of this review is to identify the effectiveness of surveillance systems and community-based interventions in identifying and responding to emerging and re-emerging zoonotic infections in Southeast Asia (SE Asia). More specifically the research questions are: 1. What is the effectiveness of community-based surveillance interventions designed to identify emerging zoonotic infectious diseases? 2. What is the effectiveness of non-pharmaceutical community-based interventions designed to prevent transmission of emerging zoonotic infectious diseases? 3. How do factors related to the emergence and management of emerging zoonotic infectious diseases impact the effectiveness of interventions designed to identify and respond to them?

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Climate change and solar ultraviolet radiation may affect vaccine-preventable infectious diseases (VPID), the human immune response process and the immunization service delivery system. We systematically reviewed the scientific literature and identified 37 relevant publications. Our study shows that climate variability and ultraviolet radiation may potentially affect VPID and the immunization delivery system through modulating vector reproduction and vaccination effectiveness, possibly influencing human immune response systems to the vaccination, and disturbing immunization service delivery. Further research is needed to determine these affects on climate-sensitive VPID and on human immune response to common vaccines. Such research will facilitate the development and delivery of optimal vaccination programs for target populations, to meet the goal of disease control and elimination.

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The lack of adequate disease surveillance systems in Ebola-affected areas has both reduced the ability to respond locally and has increased global risk. There is a need to improve disease surveillance in vulnerable regions, and digital surveillance could present a viable approach.

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Background: The development of a horse vaccine against Hendra virus has been hailed as a good example of a One Health approach to the control of human disease. Although there is little doubt that this is true, it is clear from the underwhelming uptake of the vaccine by horse owners to date (approximately 10%) that realisation of a One Health approach requires more than just a scientific solution. As emerging infectious diseases may often be linked to the development and implementation of novel vaccines this presentation will discuss factors influencing their uptake; using Hendra virus in Australia as a case study. Methods: This presentation will draw on data collected from the Horse owners and Hendra virus: A Longitudinal cohort study To Evaluate Risk (HHALTER) study. The HHALTER study is a mixed methods research study comprising a two-year survey-based longitudinal cohort study and qualitative interview study with horse owners in Australia. The HHALTER study has investigated and tracked changes in a broad range of issues around early uptake of vaccination, horse owner uptake of other recommended disease risk mitigation strategies, and attitudes to government policy and disease response. Interviews provide further insights into attitudes towards risk and decision-making in relation to vaccine uptake. A combination of quantitative and qualitative data analysis will be reported. Results: Data collected from more than 1100 horse owners shortly after vaccine introduction indicated that vaccine uptake and intention to vaccinate was associated with a number of risk perception factors and financial cost factors. In addition, concerns about side effects and veterinarians refusing to treat unvaccinated horses were linked to uptake. Across the study period vaccine uptake in the study cohort increased to more than 50%, however, concerns around side effects, equine performance and breeding impacts, delays to full vaccine approvals, and attempts to mandate vaccination by horse associations and event organisers have all impacted acceptance. Conclusion: Despite being provided with a safe and effective vaccine for Hendra virus that can protect horses and break the transmission cycle of the virus to humans, Australian horse owners have been reluctant to commit to it. General issues pertinent to novel vaccines, combined with challenges in the implementation of the vaccine have led to issues of mistrust and misconception with some horse owners. Moreover, factors such as cost, booster dose schedules, complexities around perceived risk, and ulterior motives attributed to veterinarians have only served to polarise attitudes to vaccine acceptance.

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Indoor air quality is a critical factor in the classroom due to high people concentration in a unique space. Indoor air pollutant might increase the chance of both long and short-term health problems among students and staff, reduce the productivity of teachers and degrade the student’s learning environment and comfort. Adequate air distribution strategies may reduce risk of infection in classroom. So, the purpose of air distribution systems in a classroom is not only to maximize conditions for thermal comfort, but also to remove indoor contaminants. Natural ventilation has the potential to play a significant role in achieving improvements in IAQ. The present study compares the risk of airborne infection between Natural Ventilation (opening windows and doors) and a Split-System Air Conditioner in a university classroom. The Wells-Riley model was used to predict the risk of indoor airborne transmission of infectious diseases such as influenza, measles and tuberculosis. For each case, the air exchange rate was measured using a CO2 tracer gas technique. It was found that opening windows and doors provided an air exchange rate of 2.3 air changes/hour (ACH), while with the Split System it was 0.6 ACH. The risk of airborne infection ranged between 4.24 to 30.86 % when using the Natural Ventilation and between 8.99 to 43.19% when using the Split System. The difference of airborne infection risk between the Split System and the Natural Ventilation ranged from 47 to 56%. Opening windows and doors maximize Natural Ventilation so that the risk of airborne contagion is much lower than with Split System.

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An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

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Infectious diseases put an enormous burden on both children and the elderly in the forms of respiratory, gastrointestinal and oral infections. There is evidence suggesting that specific probiotics may be antagonistic to pathogens and may enhance the immune system, but the clinical evidence is still too sparce to make general conclusions on the disease-preventive effects of probiotics. This thesis, consisting of four independent, double-blind, placebo-controlled clinical trials, investigated whether Lactobacillus GG (LGG) or a specific probiotic combination containing LGG would reduce the risk of common infections or the prevalence of pathogens in healthy and infection-prone children and in independent and institutionalised elderly people. In healthy day-care children, the 7-month consumption of probiotic milk containing Lactobacillus GG appeared to postpone the first acute respiratory infection (ARI) by one week (p=0.03, adjusted p=0.16), and to reduce complicated infections (39% vs. 47%, p<0.05, adjusted p=0.13), as well as the need for antibiotic treatment (44% vs. 54%, p=0.03, adjusted p=0.08) and day-care absences (4.9 vs. 5.8 days, p=0.03, adjusted p=0.09) compared to the placebo milk. In infection-prone children, the 6-month consumption of a combination of four probiotic bacteria (LGG, L. rhamnosus LC705, Propionibacterium freudenreichii JS, Bifidobacterium breve 99) taken in capsules appeared to reduce recurrent ARIs (72% vs. 82%, p<0.05; adjusted p=0.06), and the effect was particularly noticeable in a subgroup of children with allergic diseases (12% vs. 33%, p=0.03), although no effect on the presence of nasopharyngeal rhinovirus or enterovirus was seen. The 5-month consumption of the same probiotic combination did not show any beneficial effects on the respiratory infections in frail, institutionalised elderly subjects. In healthy children receiving Lactobacillus GG, the reduction in complications resulted in a marginal reduction in the occurrence of acute otitis media (AOM) (31% vs. 39%, p=0.08; adjusted p=0.19), and the postponement of the first AOM episode by 12 days (p=0.04; adjusted p=0.09). However, in otitis-prone children, a probiotic combination did not reduce the occurrence of AOM or the total prevalence of common AOM pathogens (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis), except in the case of children with allergic diseases, in whom probiotics reduced recurrent AOM episodes (0% vs. 14%, p=0.03). In addition, interaction between probiotics and bacterial carriage was seen: probiot-ics reduced AOM in children who did not carry any bacterial pathogens (63% vs. 83%), but the effect was the reverse in children carrying bacteria in the nasopharynx (74% vs 62%) (p<0.05). Long-term probiotic treatment, either LGG given in milk to healthy children for 7 months or a combination of probiotics given in capsules to institutionalised elderly subjects for 5 months, did not reduce the occurrence of acute diarrhoea. However, when the probiotic combination (LGG, L. rhamnosus LC705, Propionibacterium JS) was given in cheese to independent elderly subjects for 4 months, the oral carriage of high Candida counts was reduced in the probiotic group vs. the placebo group (21% vs. 34%, p=0.01, adjusted p=0.004). The risk of hyposalivation was also reduced in the probiotic group (p=0.05). In conclusion, probiotics appear to slightly alleviate the severity of infections by postponing their appearance, by reducing complications and the need for antimicrobial treatments. In addition, they appear to prevent recurrent infections in certain subgroups of children, such as in infection-prone children with allergic diseases. Alleviating ARI by probiotics may lead to a marginal reduction in the occurrence of AOM in healthy children but not in infection-prone children with disturbed nasopharyngeal microbiota. On the basis of these results it could be supposed that Lactobacillus GG or a specific combination containing LGG are effective against viral but not against bacterial otitis, and the mechanism is probably mediated through the stimulation of the immune system. A specific probiotic combination does not reduce respiratory infections in frail elderly subjects. Acute diarrhoea, either in children or in the elderly, is not prevented by the continuous, long-term consumption of probiotics, but the consumption of a specific probiotic combination in a food matrix is beneficial to the oral health of the elderly, through the reduction of the carriage of Candida.

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A nonlinear adaptive system theoretic approach is presented in this paper for effective treatment of infectious diseases that affect various organs of the human body. The generic model used does not represent any specific disease. However, it mimics the generic immunological dynamics of the human body under pathological attack, including the response to external drugs. From a system theoretic point of view, drugs can be interpreted as control inputs. Assuming a set of nominal parameters in the mathematical model, first a nonlinear controller is designed based on the principle of dynamic inversion. This treatment strategy was found to be effective in completely curing "nominal patients". However, in some cases it is ineffective in curing "realistic patients". This leads to serious (sometimes fatal) damage to the affected organ. To make the drug dosage design more effective, a model-following neuro-adaptive control design is carried out using neural networks, which are trained (adapted) online. From simulation studies, this adaptive controller is found to be effective in killing the invading microbes and healing the damaged organ even in the presence of parameter uncertainties and continuing pathogen attack.

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A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.