A systematic review of reported reassortant viral lineages of influenza A

BACKGROUND: Most previous evolutionary studies of influenza A have focussed on genetic drift, or reassortment of specific gene segments, hosts or subtypes. We conducted a systematic literature review to identify reported claimed reassortant influenza A lineages with genomic data available in GenBank, to obtain 646 unique first-report isolates out of a possible 20,781 open-access genomes. RESULTS: After adjusting for correlations, only: swine as host, China, Europe, Japan and years between 1997 and 2002; remained as significant risk factors for the reporting of reassortant viral lineages. For swine H1, more reassortants were observed in the North American H1 clade compared with the Eurasian avian-like H1N1 clade. Conversely, for avian H5 isolates, a higher number of reported reassortants were observed in the European H5N2/H3N2 clade compared with the H5N2 North American clade. CONCLUSIONS: Despite unavoidable biases (publication, database choice and upload propensity) these results synthesize a large majority of the current literature on novel reported influenza A reassortants and are a potentially useful prerequisite to inform further algorithmic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12879-015-1298-9) contains supplementary material, which is available to authorized users

Managing and reducing uncertainty in an emerging influenza pandemic

published_or_final_versio

Linear-scaling time-dependent density-functional theory in the linear response formalism

We present an implementation of time-dependent density-functional theory (TDDFT) in the linear response formalism enabling the calculation of low energy optical absorption spectra for large molecules and nanostructures. The method avoids any explicit reference to canonical representations of either occupied or virtual Kohn-Sham states and thus achieves linear-scaling computational effort with system size. In contrast to conventional localised orbital formulations, where a single set of localised functions is used to span the occupied and unoccupied state manifold, we make use of two sets of in situ optimised localised orbitals, one for the occupied and one for the unoccupied space. This double representation approach avoids known problems of spanning the space of unoccupied Kohn-Sham states with a minimal set of localised orbitals optimised for the occupied space, while the in situ optimisation procedure allows for efficient calculations with a minimal number of functions. The method is applied to a number of medium sized organic molecules and a good agreement with traditional TDDFT methods is observed. Furthermore, linear scaling of computational cost with system size is demonstrated on a system of carbon nanotubes

The impact of contact tracing in clustered populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease

The Impact of the Unstructured Contacts Component in Influenza Pandemic Modeling

Individual based models have become a valuable tool for modeling the spatiotemporal dynamics of epidemics, e.g. influenza pandemic, and for evaluating the effectiveness of intervention strategies. While specific contacts among individuals into diverse environments (family, school/workplace) can be modeled in a standard way by employing available socio-demographic data, all the other (unstructured) contacts can be dealt with by adopting very different approaches. This can be achieved for instance by employing distance-based models or by choosing unstructured contacts in the local communities or by employing commuting data.Here we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies. Sensitivity analysis has been conducted for different values of the first generation index G(0), which is the average number of secondary infections generated by the first infectious individual in a completely susceptible population and by varying the seeding municipality. Among the different considered models, attack rate ranges from 19.1% to 25.7% for G(0) = 1.1, from 47.8% to 50.7% for G(0) = 1.4 and from 62.4% to 67.8% for G(0) = 1.7. Differences of about 15 to 20 days in the peak day have been observed. As regards spatial diffusion, a difference of about 100 days to cover 200 km for different values of G(0) has been observed.To reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts. Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices

Effectiveness of a structured, framework-based approach to implementation: the Researching Effective Approaches to Cleaning in Hospitals (REACH) Trial.

BACKGROUND:Implementing sustainable practice change in hospital cleaning has proven to be an ongoing challenge in reducing healthcare associated infections. The purpose of this study was to develop a reliable framework-based approach to implement and quantitatively evaluate the implementation of evidence-based practice change in hospital cleaning. DESIGN/METHODS:The Researching Effective Approaches to Cleaning in Hospitals (REACH) trial was a pragmatic, stepped-wedge randomised trial of an environmental cleaning bundle implemented in 11 Australian hospitals from 2016 to 2017. Using a structured multi-step approach, we adapted the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to support rigorous and tailored implementation of the cleaning bundle intervention in eleven diverse and complex settings. To evaluate the effectiveness of this strategy we examined post-intervention cleaning bundle alignment calculated as a score (an implementation measure) and cleaning performance audit data collected using ultraviolet (UV) gel markers (an outcome measure). RESULTS:We successfully implemented the bundle and observed improvements in cleaning practice and performance, regardless of hospital size, intervention duration and contextual issues such as staff and organisational readiness at baseline. There was a positive association between bundle alignment scores and cleaning performance at baseline. This diminished over the duration of the intervention, as hospitals with lower baseline scores were able to implement practice change successfully. CONCLUSION:Using a structured framework-based approach allows for pragmatic and successful implementation of clinical trials across diverse settings, and assists with quantitative evaluation of practice change. TRIAL REGISTRATION:Australia New Zealand Clinical Trial Registry ACTRN12615000325505, registered on 4 September 2015

Estimating Individual and Household Reproduction Numbers in an Emerging Epidemic

Reproduction numbers, defined as averages of the number of people infected by a typical case, play a central role in tracking infectious disease outbreaks. The aim of this paper is to develop methods for estimating reproduction numbers which are simple enough that they could be applied with limited data or in real time during an outbreak. I present a new estimator for the individual reproduction number, which describes the state of the epidemic at a point in time rather than tracking individuals over time, and discuss some potential benefits. Then, to capture more of the detail that micro-simulations have shown is important in outbreak dynamics, I analyse a model of transmission within and between households, and develop a method to estimate the household reproduction number, defined as the number of households infected by each infected household. This method is validated by numerical simulations of the spread of influenza and measles using historical data, and estimates are obtained for would-be emerging epidemics of these viruses. I argue that the household reproduction number is useful in assessing the impact of measures that target the household for isolation, quarantine, vaccination or prophylactic treatment, and measures such as social distancing and school or workplace closures which limit between-household transmission, all of which play a key role in current thinking on future infectious disease mitigation

Controlling infectious disease outbreaks: Lessons from mathematical modelling.

Accepted versio

Assessing the impact of indoor plants towards physical indoor office building environment in hot and humid climates

Several studies have shown that plants with a variety of performance categories can improve indoor air quality (IAQ). In addition, it has positive impacts on occupants’ concentration ability, work efficiency, job satisfaction, mental health, stress reduction, and sense of well-being. However, there is few research that have focused on the impact of indoor plants towards physical indoor environment. Therefore, this study performs several experiments to examine the impacts of indoor plants on physical indoor environment in Malaysia. This study further observes the use of five different species of potted plants and its effects on the IAQ concerning the physical parameters of air temperature and relative humidity in a classroom and an office room setting environment. Moreover, this paper explores the function of plants as a natural air purifying agent and temperature regulation, as it helps to cool down the internal temperature of a building while providing areas of the building with an aesthetic element. This study involves the assessment of plant and indoor space benefit studies, as well as the description of plant varieties and characteristics. Finally, the overall findings from several experiments show significant differences in relative humidity and total target volatile organic compound (TVOC) in the room. With intervention, it can be concluded that the inclusion of potted plants in a room have improved all parameters measured compared with that in normal condition. For future works, it is proposed that further study is done on the form of species, including the importance of understanding the need for positioning the potted plants in indoor environments