Outbreak detection algorithms for seasonal disease data: a case study using ross river virus disease

<p>Abstract</p> <p>Background</p> <p>Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens.</p> <p>Methods</p> <p>We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms.</p> <p>Results</p> <p>The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs.</p> <p>Conclusions</p> <p>This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.</p

Identification of optimal epitopes for Plasmodium falciparum rapid diagnostic tests that target histidine-rich proteins 2 and 3

Rapid diagnostic tests (RDTs) represent important tools to diagnose malaria infection. To improve understanding of the variable performance of RDTs that detect the major target in Plasmodium falciparum, namely, histidine-rich protein 2 (HRP2), and to inform the design of better tests, we undertook detailed mapping of the epitopes recognized by eight HRP-specific monoclonal antibodies (MAbs). To investigate the geographic skewing of this polymorphic protein, we analyzed the distribution of these epitopes in parasites from geographically diverse areas. To identify an ideal amino acid motif for a MAb to target in HRP2 and in the related protein HRP3, we used a purpose-designed script to perform bioinformatic analysis of 448 distinct gene sequences from pfhrp2 and from 99 sequences from the closely related gene pfhrp3. The frequency and distribution of these motifs were also compared to the MAb epitopes. Heat stability testing of MAbs immobilized on nitrocellulose membranes was also performed. Results of these experiments enabled the identification of MAbs with the most desirable characteristics for inclusion in RDTs, including copy number and coverage of target epitopes, geographic skewing, heat stability, and match with the most abundant amino acid motifs identified. This study therefore informs the selection of MAbs to include in malaria RDTs as well as in the generation of improved MAbs that should improve the performance of HRP-detecting malaria RDTs. Copyright © 2012, American Society for Microbiology

An investigation of supervector regression for forensic voice comparison on small data

International audienceThe present paper deals with an observer design for a nonlinear lateral vehicle model. The nonlinear model is represented by an exact Takagi-Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral observer (PMIO) based on the TS model is designed to estimate simultaneously the state vector and the unknown input (road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory which guaranties bounded error. Simulations are carried out and experimental results are provided to illustrate the proposed observer

Distributed sensing of a masonry vault due to nearby piling

Piles were constructed inside historic brick barrel vaults during the London Bridge Station Redevelopment. In order to ensure safe operation of the tracks above, movements of the vaults were monitored regularly by total stations. Concurrently, two distributed sensing technologies, fibre optic cables and laser scanners, were used to investigate the vault response to settlements. This paper discusses the monitoring data retrieved from these ‘point’ and ‘distributed’ sensing technologies and evaluates their use in structural assessment. The total station data are examined first. It is characterized by high precision and limited spatial coverage due to the use of optical targets. As a result, the total station data are useful for threshold detection but do not provide a detailed understanding of structural response or damage. In contrast, by utilizing distributed fibre optic sensors based on Brillouin optical domain reflectometry, the strain development in the structure during piling is quantified. The location and width of resulting crack openings are also determined, providing useful indicators for damage evaluation. The comparison of point clouds from laser scanners obtained at different stages of pile construction further expands the spatial coverage by detecting global movement of the structure on all visible surfaces. Using these data, the two hinge-response mechanism of the vault is revealed. The rich distributed data enable the calibration of the 2D mechanism and the finite element models, elucidating the contribution of arch stiffness, arch and backfill interaction, potential lateral movements and inter-ring sliding to the response