Proposal of a framework for evaluating military surveillance systems for early detection of outbreaks on duty areas

<p>Abstract</p> <p>Background</p> <p>In recent years a wide variety of epidemiological surveillance systems have been developed to provide early identification of outbreaks of infectious disease. Each system has had its own strengths and weaknesses. In 2002 a Working Group of the Centers for Disease Control and Prevention (CDC) produced a framework for evaluation, which proved suitable for many public health surveillance systems. However this did not easily adapt to the military setting, where by necessity a variety of different parameters are assessed, different constraints placed on the systems, and different objectives required. This paper describes a proposed framework for evaluation of military syndromic surveillance systems designed to detect outbreaks of disease on operational deployments.</p> <p>Methods</p> <p>The new framework described in this paper was developed from the cumulative experience of British and French military syndromic surveillance systems. The methods included a general assessment framework (CDC), followed by more specific methods of conducting evaluation. These included Knowledge/Attitude/Practice surveys (KAP surveys), technical audits, ergonomic studies, simulations and multi-national exercises. A variety of military constraints required integration into the evaluation. Examples of these include the variability of geographical conditions in the field, deployment to areas without prior knowledge of naturally-occurring disease patterns, the differences in field sanitation between locations and over the length of deployment, the mobility of military forces, turnover of personnel, continuity of surveillance across different locations, integration with surveillance systems from other nations working alongside each other, compatibility with non-medical information systems, and security.</p> <p>Results</p> <p>A framework for evaluation has been developed that can be used for military surveillance systems in a staged manner consisting of initial, intermediate and final evaluations. For each stage of the process parameters for assessment have been defined and methods identified.</p> <p>Conclusion</p> <p>The combined experiences of French and British syndromic surveillance systems developed for use in deployed military forces has allowed the development of a specific evaluation framework. The tool is suitable for use by all nations who wish to evaluate syndromic surveillance in their own military forces. It could also be useful for civilian mobile systems or for national security surveillance systems.</p

Situational Awareness of Influenza Activity Based on Multiple Streams of Surveillance Data Using Multivariate Dynamic Linear Model

BACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (rho = 0.82, p</=0.02) for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method. CONCLUSIONS: Our results demonstrate the potential use of multiple streams of influenza surveillance data to promote situational awareness about the level and trend of seasonal and pandemic influenza activity.published_or_final_versio

Extraordinarily high biomass benthic community on Southern Ocean seamounts

We describe a previously unknown assemblage of seamount-associated megabenthos that has by far the highest peak biomass reported in the deep-sea outside of vent communities. The assemblage was found at depths of 2–2.5 km on rocky geomorphic features off the southeast coast of Australia, in an area near the Sub-Antarctic Zone characterised by high rates of surface productivity and carbon export to the deep-ocean. These conditions, and the taxa in the assemblage, are widely distributed around the Southern mid-latitudes, suggesting the high-biomass assemblage is also likely to be widespread. The role of this assemblage in regional ecosystem and carbon dynamics and its sensitivities to anthropogenic impacts are unknown. The discovery highlights the lack of information on deep-sea biota worldwide and the potential for unanticipated impacts of deep-sea exploitation

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

Ecology and biogeography of megafauna and macrofauna at the first known deep-sea hydrothermal vents on the ultraslow-spreading Southwest Indian Ridge

0000-0002-9489-074X© The Author(s) 2016. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The attached file is the published version of the article

Morphotypes of virus-like particles in two hydrothermal vent fields on the East Scotia Ridge, Antarctica

Viruses from extreme environments are still largely unexplored and may harbor unseen genetic potential. Here, we present a first glance at the morphological diversity of virus like particles (VLPs) from an environment that is extreme in more than one respect: two recently discovered hydrothermal vent fields on the East Scotia Ridge in the Southern Ocean near Antarctica. They are the southernmost hydrothermal sites found to date and have been shown to present a new biogeographic province, containing several new macrofaunal species and associated microbial organisms. Transmission electron microscopy revealed a range of tailed and untailed VLPs of various morphologies as well as an unusual long rod-shaped VLP with three long filaments. Based on its distant similarity with several known archaeal viruses, we hypothesize that this presents a new viral morphology that most likely infects an archaeon. Notably absent in the samples we analyzed were lemon- or spindle-shaped VLPs that have previously been described in other hydrothermal vent settings

Self-organising Maps to Study the Effects of Urbanisation at Long Bay in New Zealand

Biologically inspired Artificial Neural Network (ANN) modelling methods provide a means of problem solving that incorporates heuristics with conventional algorithmic processing Over the last few decades, nell' techniques for neuron relationship modelling and network architecture algorithms have been introduced to solve a ll'ide range of problems across many fields. This paper looks into the aspects of using SOMs to a biological example to permit understanding of a complex environmental process. The preliminmy research results to study the effects of urbanisation on marine life at the Long Bay-Okura Marine Reserve, situated in northern Nell' Zealand is discussed in detail. This was the count1y 's first urban, marine resen1e to be established (1995), and resulted from groll'ing concern of environmental groups and general public of the area. Since then many institutions have conducted research to find the cause for the observed environmental change. All these data sets are fused and analysed collectively to study the patterns in them. The use of SOMs to industrial process monitoring has been ve1y successful in many areas and is applied here to study the process dynamics in environmental process modelling with SOM trajectories. The analyses show the relationships found in the data sets from different sources in easily perceivable formats without having to model the complex physical process

Self-organising Maps to Study the Effects of Urbanisation at Long Bay in New Zealand

Biologically inspired Artificial Neural Network (ANN) modelling methods provide a means of problem solving that incorporates heuristics with conventional algorithmic processing Over the last few decades, nell' techniques for neuron relationship modelling and network architecture algorithms have been introduced to solve a ll'ide range of problems across many fields. This paper looks into the aspects of using SOMs to a biological example to permit understanding of a complex environmental process. The preliminmy research results to study the effects of urbanisation on marine life at the Long Bay-Okura Marine Reserve, situated in northern Nell' Zealand is discussed in detail. This was the count1y 's first urban, marine resen1e to be established (1995), and resulted from groll'ing concern of environmental groups and general public of the area. Since then many institutions have conducted research to find the cause for the observed environmental change. All these data sets are fused and analysed collectively to study the patterns in them. The use of SOMs to industrial process monitoring has been ve1y successful in many areas and is applied here to study the process dynamics in environmental process modelling with SOM trajectories. The analyses show the relationships found in the data sets from different sources in easily perceivable formats without having to model the complex physical process