Value of syndromic surveillance within the Armed Forces for early warning during a dengue fever outbreak in French Guiana in 2006

<p>Abstract</p> <p>Background</p> <p>A dengue fever outbreak occured in French Guiana in 2006. The objectives were to study the value of a syndromic surveillance system set up within the armed forces, compared to the traditional clinical surveillance system during this outbreak, to highlight issues involved in comparing military and civilian surveillance systems and to discuss the interest of syndromic surveillance for public health response.</p> <p>Methods</p> <p>Military syndromic surveillance allows the surveillance of suspected dengue fever cases among the 3,000 armed forces personnel. Within the same population, clinical surveillance uses several definition criteria for dengue fever cases, depending on the epidemiological situation. Civilian laboratory surveillance allows the surveillance of biologically confirmed cases, within the 200,000 inhabitants.</p> <p>Results</p> <p>It was shown that syndromic surveillance detected the dengue fever outbreak several weeks before clinical surveillance, allowing quick and effective enhancement of vector control within the armed forces. Syndromic surveillance was also found to have detected the outbreak before civilian laboratory surveillance.</p> <p>Conclusion</p> <p>Military syndromic surveillance allowed an early warning for this outbreak to be issued, enabling a quicker public health response by the armed forces. Civilian surveillance system has since introduced syndromic surveillance as part of its surveillance strategy. This should enable quicker public health responses in the future.</p

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Exploring Web-Based University Policy Statements on Plagiarism by Research-Intensive Higher Education Institutions

Plagiarism may distress universities in the US, but there is little agreement as to exactly what constitutes plagiarism. While there is ample research on plagiarism, there is scant literature on the content of university policies regarding it. Using a systematic sample, we qualitatively analyzed 20 Carnegie-classified universities that are “Very High in Research.” This included 15 public state universities and five high-profile private universities. We uncovered highly varied and even contradictory policies at these institutions. Notable policy variations existed for verbatim plagiarism, intentional plagiarism and unauthorized student collaboration at the studied institutions. We conclude by advising that the American Association of University Professors (AAUP), the American Association of Colleges and Universities (AACU) and others confer and come to accord on the disposition of these issues

Using combined diagnostic test results to hindcast trends of infection from cross-sectional data

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to ‘hindcast’ (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time

Multilocus ISSR Markers Reveal Two Major Genetic Groups in Spanish and South African Populations of the Grapevine Fungal Pathogen Cadophora luteo-olivacea

Cadophora luteo-olivacea is a lesser-known fungal trunk pathogen of grapevine which has been recently isolated from vines showing decline symptoms in grape growing regions worldwide. In this study, 80 C. luteo-olivacea isolates (65 from Spain and 15 from South Africa) were studied. Inter-simple-sequence repeat-polymerase chain reaction (ISSR-PCR) generated 55 polymorphic loci from four ISSR primers selected from an initial screen of 13 ISSR primers. The ISSR markers revealed 40 multilocus genotypes (MLGs) in the global population. Minimum spanning network analysis showed that the MLGs from South Africa clustered around the most frequent genotype, while the genotypes from Spain were distributed all across the network. Principal component analysis and dendrograms based on genetic distance and bootstrapping identified two highly differentiated genetic clusters in the Spanish and South African C. luteo-olivacea populations, with no intermediate genotypes between these clusters. Movement within the Spanish provinces may have occurred repeatedly given the frequent retrieval of the same genotype in distant locations. The results obtained in this study provide new insights into the population genetic structure of C. luteo-olivacea in Spain and highlights the need to produce healthy and quality planting material in grapevine nurseries to avoid the spread of this fungus throughout different grape growing regions