Construction of the influenza A virus transmission tree in a college-based population: co-transmission and interactions between influenza A viruses.

BACKGROUND: Co-infection of different influenza A viruses is known to occur but how viruses interact within co-infection remains unknown. An outbreak in a college campus during the 2009 pandemic involved two subtypes of influenza A: persons infected with pandemic A/H1N1; persons infected with seasonal A/H3N2 viruses; and persons infected with both at the same time (co-infection). This provides data to analyse the possible interaction between influenza A viruses within co-infection. METHODS: We extend a statistical inference method designed for outbreaks caused by one virus to that caused by two viruses. The method uses knowledge of which subtype each case is infected with (and whether they were co-infected), contact information and symptom onset date of each case in the influenza outbreak. We then apply it to construct the most likely transmission tree during the outbreak in the college campus. RESULTS: Analysis of the constructed transmission tree shows that the simultaneous presence of the two influenza viruses increases the infectivity and the transmissibility of A/H1N1 virus but whether it changes the infectivity of A/H3N2 is unclear. The estimation also shows that co-transmission of both subtypes from co-infection is low and therefore co-infection cannot be sustained on its own. CONCLUSIONS: This study suggests that influenza A viruses within co-infected patients can interact in some ways rather than transmit independently, and this can enhance the spread of influenza A virus infection

Regime jurídico contemporâneo da função social da posse rural

Orientador: Luiz Edson FachinMonografia (graduação) - Universidade Federal do Paraná, Setor de Ciências Jurídicas, Curso de Graduação em Direit

Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis.

Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement

Atrophy, oxidative switching and ultrastructural defects in skeletal muscle of the ataxia telangiectasia mouse model

Ataxia telangiectasia is a rare, multi system disease caused by ATM kinase deficiency. Atm-knockout mice recapitulate premature aging, immunodeficiency, cancer predisposition, growth retardation and motor defects, but not cerebellar neurodegeneration and ataxia. We explored whether Atm loss is responsible for skeletal muscle defects by investigating myofiber morphology, oxidative/glycolytic activity, myocyte ultrastructural architecture and neuromuscular junctions. Atm-knockout mice showed reduced muscle and fiber size. Atrophy, protein synthesis impairment and a switch from glycolytic to oxidative fibers were detected, along with an increase of in expression of slow and fast myosin types (Myh7, and Myh2 and Myh4, respectively) in tibialis anterior and solei muscles isolated from Atm-knockout mice. Transmission electron microscopy of tibialis anterior revealed misalignments of Z-lines and sarcomeres and mitochondria abnormalities that were associated with an increase in reactive oxygen species. Moreover, neuromuscular junctions appeared larger and more complex than those in Atm wild-type mice, but with preserved presynaptic terminals. In conclusion, we report for the first time that Atm-knockout mice have clear morphological skeletal muscle defects that will be relevant for the investigation of the oxidative stress response, motor alteration and the interplay with peripheral nervous system in ataxia telangiectasia

Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach

Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a disparate body of evidence available to inform disease incidence and prevalence estimation. MPES is the statistical methodology adopted by the Health Protection Agency in the UK for its annual national assessment of the HIV epidemic, and is acknowledged by the World Health Organization and UNAIDS as a valuable technique for the estimation of adult HIV prevalence from surveillance data. This paper describes the results of utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands at the end of 2007, using an array of field data from different study designs on various population risk subgroups and with a varying degree of regional coverage. Auxiliary data and expert opinion were additionally incorporated to resolve issues arising from biased, insufficient or inconsistent evidence. This case study offers a demonstration of the ability of MPES to naturally integrate and critically reconcile disparate and heterogeneous sources of evidence, while producing reliable estimates of HIV prevalence used to support public health decision-making.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS488 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.

Decision-analytic models must often be informed using data that are only indirectly related to the main model parameters. The authors outline how to implement a Bayesian synthesis of diverse sources of evidence to calibrate the parameters of a complex model. A graphical model is built to represent how observed data are generated from statistical models with unknown parameters and how those parameters are related to quantities of interest for decision making. This forms the basis of an algorithm to estimate a posterior probability distribution, which represents the updated state of evidence for all unknowns given all data and prior beliefs. This process calibrates the quantities of interest against data and, at the same time, propagates all parameter uncertainties to the results used for decision making. To illustrate these methods, the authors demonstrate how a previously developed Markov model for the progression of human papillomavirus (HPV-16) infection was rebuilt in a Bayesian framework. Transition probabilities between states of disease severity are inferred indirectly from cross-sectional observations of prevalence of HPV-16 and HPV-16-related disease by age, cervical cancer incidence, and other published information. Previously, a discrete collection of plausible scenarios was identified but with no further indication of which of these are more plausible. Instead, the authors derive a Bayesian posterior distribution, in which scenarios are implicitly weighted according to how well they are supported by the data. In particular, we emphasize the appropriate choice of prior distributions and checking and comparison of fitted models