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Nonspatial theory on pathogen evolution generally predicts selection for maximal number of secondary infections, constrained only by supposed physiological trade-offs between pathogen infectiousness and virulence. Spread of diseases in human populations can, however, exhibit large scale patterns, underlining the need for spatially explicit approaches to pathogen evolution. Here, we show, in a spatial model where all pathogen traits are allowed to evolve independently, that evolutionary trajectories follow a single relationship between transmission and clearance. This tradeoff relation is an emergent system property, as opposed to being a property of pathogen physiology, and maximizes outbreak frequency instead of the number of secondary infections. We conclude that spatial pattern formation in contact networks can act to link infectiousness and clearance during pathogen evolution in the absence of any physiological trade-off. Selection for outbreak frequency offers an explanation for the evolution of pathogens that cause mild but frequent infections. evolution | pathogen | spatial model | spatial patterns

Background: Effectiveness of psychological treatment is often assessed using patient-reported health evaluations. However, comparison of such scores over time can be hampered due to a change in the meaning of self-evaluations, called 'response shift'. Insight into the occurrence of response shift seems especially relevant in the context of psychological interventions, as they often purposefully intend to change patients' frames of reference. Aims: The overall aim is to gain insight into the general relevance of response shift for psychological health intervention research. Specifically, the aim is to re-analyse data of published randomized controlled trials (RCTs) investigating the effectiveness of psychological interventions targeting different health aspects, to assess (1) the occurrence of response shift, (2) the impact of response shift on interpretation of treatment effectiveness, and (3) the predictive role of clinical and background variables for detected response shift. Method: We re-analysed data from RCTs on guided internet delivered cognitive behavioural treatment (CBT) for insomnia in the general population with and without elevated depressive symptoms, an RCT on meaning-centred group psychotherapy targeting personal meaning for cancer survivors, and an RCT on internet-based CBT treatment for persons with diabetes with elevated depressive symptoms. Structural equation modelling was used to test the three objectives. Results: We found indications of response shift in the intervention groups of all analysed datasets. However, results were mixed, as response shift was also indicated in some of the control groups, albeit to a lesser extent or in opposite direction. Overall, the detected response shifts only marginally impacted trial results. Relations with selected clinical and background variables helped the interpretation of detected effects and their possible mechanisms. Conclusion: This study showed that response shift effects can occur as a result of psychological health interventions. Response shift did not influence the overall interpretation of trial results, but provide insight into differential treatment effectiveness for specific symptoms and/or domains that can be clinically meaningful. [ABSTRACT FROM AUTHOR]

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology. [ABSTRACT FROM AUTHOR]

Surveillance systems of contagious diseases record information on cases to monitor incidence of disease and to evaluate effectiveness of interventions. These systems focus on a well-defined population; a key question is whether observed cases are infected through local transmission within the population or whether cases are the result of importation of infection into the population. Local spread of infection calls for different intervention measures than importation of infection. Besides standardized information on time of symptom onset and location of cases, pathogen genotyping or sequencing offers essential information to address this question. Here we introduce a method that takes full advantage of both the genetic and epidemiological data to distinguish local transmission from importation of infection, by comparing inter-case distances in temporal, spatial and genetic data. Cases that are part of a local transmission chain will have shorter distances between their geographical locations, shorter durations between their times of symptom onset and shorter genetic distances between their pathogen sequences as compared to cases that are due to importation. In contrast to generic clustering algorithms, the proposed method explicitly accounts for the fact that during local transmission of a contagious disease the cases are caused by other cases. No pathogen-specific assumptions are needed due to the use of ordinal distances, which allow for direct comparison between the disparate data types. Using simulations, we test the performance of the method in identifying local transmission of disease in large datasets, and assess how sensitivity and specificity change with varying size of local transmission chains and varying overall disease incidence. [ABSTRACT FROM AUTHOR]

Abstract: Following the emergence of a novel strain of influenza A(H1N1) in Mexico and the United States in April 2009, its epidemiology in Europe during the summer was limited to sporadic and localised outbreaks. Only the United Kingdom experienced widespread transmission declining with school holidays in late July. Using statistical modelling where applicable we explored the following causes that could explain this surprising difference in transmission dynamics: extinction by chance, differences in the susceptibility profile, age distribution of the imported cases, differences in contact patterns, mitigation strategies, school holidays and weather patterns. No single factor was able to explain the differences sufficiently. Hence an additive mixed model was used to model the country-specific weekly estimates of the effective reproductive number using the extinction probability, school holidays and weather patterns as explanatory variables. The average extinction probability, its trend and the trend in absolute humidity were found to be significantly negatively correlated with the effective reproduction number — although they could only explain about 3% of the variability in the model. By comparing the initial epidemiology of influenza A (H1N1) across different European countries, our analysis was able to uncover a possible role for the timing of importations (extinction probability), mixing patterns and the absolute humidity as underlying factors. However, much uncertainty remains. With better information on the role of these epidemiological factors, the control of influenza could be improved. [Copyright &y& Elsevier]

An effective vaccine is available for the hepatitis B virus (HBV), which is a very contagious human pathogen. The prevalence of chronic HBV infection is very low in the Netherlands (<0.5%), and no universal vaccination is in place. Instead, a program of vaccination for targeted groups at high risk of HBV exposure has been implemented. Because transmission of HBV can occur by various routes, the effectiveness of this targeted vaccination strategy is difficult to assess. Molecular typing data for the surface protein encoding gene of HBV isolates, in combination with epidemiological data, provide some insight into the main transmission routes. Due to the low mutation rate of the HBV genome, many isolates have identical S region sequences, which hampers phylogenetic analysis and identification of transmission chains. The molecular epidemiological analysis of acute HBV isolates based on the surface and core protein encoding regions were compared. The nucleotide diversity found in the C region was statistically significant greater (1.5 times) than in the S region, and phylogenetic analysis based on the C region showed a higher resolution. C region analysis resulted in an almost 50% reduction of genotype A isolates with identical sequences. C region analysis also indicated that no long-chain transmission of genotype D strains is occurring in the Netherlands, as all genotype D isolates have unique C region sequences. Defining the goals of molecular typing of HBV isolates should precede the choice for phylogenetic analysis on the basis of either C or S region sequences. J. Med. Virol. 80:233-241, 2008. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Abstract Background Internet based self-help for panic disorder (PD) has proven to be effective. However, studies so far have focussed on treating a full-blown disorder. Panic symptoms that do not meet DSM-IV criteria are more prevalent than the full-blown disorder and patients with sub-clinical panic symptoms are at risk of developing PD. This study is a randomised controlled trial aimed to evaluate an Internet based self-help intervention for sub-clinical and mild PD compared to a waiting list control group. Methods Participants with mild or sub-clinical PD (N = 128) will be recruited in the general population. Severity of panic and anxiety symptoms are the primary outcome measures. Secondary outcomes include depressive symptoms, quality of life, loss of production and health care consumption. Assessments will take place on the Internet at baseline and three months after baseline. Discussion Results will indicate the effectiveness of Internet based self-help for sub-clinical and mild PD. Strengths of this design are the external validity and the fact that it is almost completely conducted online. Trial registration Netherlands Trial Register (NTR): NTR1639 The Netherlands Trial Register is part of the Dutch Cochrane Centre.