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In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth. [ABSTRACT FROM AUTHOR]

Drone swarms consist of multiple drones that can achieve tasks that individual drones can not, such as search and recovery or surveillance over a large area. A swarm’s internal structure typically consists of multiple drones operating autonomously. Reliable detection and tracking of swarms and individual drones allow a greater understanding of the behaviour and movement of a swarm. Increased understanding of drone behaviour allows better coordination, collision avoidance, and performance monitoring of individual drones in the swarm. The research presented in this paper proposes a deep learning-based approach for reliable detection and tracking of individual drones within a swarm using stereo-vision cameras in real time. The motivation behind this research is in the need to gain a deeper understanding of swarm dynamics, enabling improved coordination, collision avoidance, and performance monitoring of individual drones within a swarm. The proposed solution provides a precise tracking system and considers the highly dense and dynamic behaviour of drones. The approach is evaluated in both sparse and dense networks in a variety of configurations. The accuracy and efficiency of the proposed solution have been analysed by implementing a series of comparative experiments that demonstrate reasonable accuracy in detecting and tracking drones within a swarm. [ABSTRACT FROM AUTHOR]

Abstract Background The large amphibious freshwater apple snail is an important invasive species in China, but there is currently no method available for their surveillance. The development and popularization of smartphones provide a new platform for research on surveillance technologies for the early detection and effective control of invasive species. Methods The ASI surveillance system was developed based on the infrastructure of the WeChat platform and Amap. The user can directly enter the game interface through the WeChat port on their mobile phone, and the system automatically obtains their location. The user can then report the location of apple snails. The administrator can audit the reported information, and all information can be exported to Microsoft Excel version 2016 for analysis. The map was generated by ArcGIS 10.2 and was used to characterize the spatial and temporal distribution of apple snails in Jiangsu Province. Results The architecture of ASI consists of three parts: a mobile terminal, a server terminal and a desktop terminal. We published more than 10 tweets on the official WeChat account of the system to announce it to the public, and a total of 207 users in 2020 and 2021 correctly reported sightings of apple snails. We identified 550 apple snails breeding sites in 2020 and 2021, featuring ponds (81%), parks (17%) and farmland (2%). In addition, most of the locations contained snail eggs, and the reporting times mainly occurred between May and September. Conclusions The ASI is an effective surveillance system that can be used to identify the breeding locations of apple snails and provides the basis of prevention and control for its dispersal. Its successful development and operation provide new potential avenues for surveillance of other public health issues. Graphical Abstract

Background: The large amphibious freshwater apple snail is an important invasive species in China, but there is currently no method available for their surveillance. The development and popularization of smartphones provide a new platform for research on surveillance technologies for the early detection and effective control of invasive species. Methods: The ASI surveillance system was developed based on the infrastructure of the WeChat platform and Amap. The user can directly enter the game interface through the WeChat port on their mobile phone, and the system automatically obtains their location. The user can then report the location of apple snails. The administrator can audit the reported information, and all information can be exported to Microsoft Excel version 2016 for analysis. The map was generated by ArcGIS 10.2 and was used to characterize the spatial and temporal distribution of apple snails in Jiangsu Province. Results: The architecture of ASI consists of three parts: a mobile terminal, a server terminal and a desktop terminal. We published more than 10 tweets on the official WeChat account of the system to announce it to the public, and a total of 207 users in 2020 and 2021 correctly reported sightings of apple snails. We identified 550 apple snails breeding sites in 2020 and 2021, featuring ponds (81%), parks (17%) and farmland (2%). In addition, most of the locations contained snail eggs, and the reporting times mainly occurred between May and September. Conclusions: The ASI is an effective surveillance system that can be used to identify the breeding locations of apple snails and provides the basis of prevention and control for its dispersal. Its successful development and operation provide new potential avenues for surveillance of other public health issues. [ABSTRACT FROM AUTHOR]

Food Traceability 4.0 (FT 4.0) is about tracing foods in the era of the fourth industrial revolution (Industry 4.0) with techniques and technologies reflecting this new revolution. Interest in food traceability has gained momentum in response to, among others events, the outbreak of the COVID-19 pandemic, reinforcing the need for digital food traceability that prevents food fraud and provides reliable information about food. This review will briefly summarize the most common conventional methods available to determine food authenticity before highlighting examples of emerging techniques that can be used to combat food fraud and improve food traceability. A particular focus will be on the concept of FT 4.0 and the significant role of digital solutions and other relevant Industry 4.0 innovations in enhancing food traceability. Based on this review, a possible new research topic, namely FT 4.0, is encouraged to take advantage of the rapid digitalization and technological advances occurring in the era of Industry 4.0. The main FT 4.0 enablers are blockchain, the Internet of things, artificial intelligence, and big data. Digital technologies in the age of Industry 4.0 have significant potential to improve the way food is traced, decrease food waste and reduce vulnerability to fraud opening new opportunities to achieve smarter food traceability. Although most of these emerging technologies are still under development, it is anticipated that future research will overcome current limitations making large-scale applications possible. [ABSTRACT FROM AUTHOR]

As the overdose epidemic overwhelmed medicolegal death investigation offices and toxicology laboratories, the King County Medical Examiner's Office responded with "real‐time" fatal overdose surveillance to expedite death certification and information dissemination through assembling a team including a dedicated medicolegal death investigator, an information coordinator, and student interns. In‐house testing of blood, urine, and drug evidence from scenes was performed using equipment and supplies purchased for surveillance. Collaboration with state laboratories allowed validation. Applied forensic epidemiology accelerated data dissemination. From 2010 to 2022, the epidemic claimed 5815 lives in King County; the last 4 years accounted for 47% of those deaths. After initiating the surveillance project, in‐house testing was performed on blood from 2836 decedents, urine from 2807, and 4238 drug evidence items from 1775 death scenes. Time to complete death certificates decreased from weeks to months to hours to days. Overdose‐specific information was distributed weekly to a network of law enforcement and public health agencies. As the surveillance project tracked the epidemic, fentanyl and methamphetamine became dominant and were associated with other indicators of social deterioration. In 2022, fentanyl was involved in 68% of 1021 overdose deaths. Homeless deaths increased sixfold; in 2022, 67% of 311 homeless deaths were due to overdose; fentanyl was involved in 49% and methamphetamine in 44%. Homicides increased 250%; in 2021, methamphetamine was positive in 35% of 149 homicides. The results are relevant to the value of rapid surveillance, its impact on standard operations, selection of cases requiring autopsy, and collaboration with other agencies in overdose prevention. [ABSTRACT FROM AUTHOR]

The evolution of hypervirulent and carbapenem-resistant Klebsiella pneumoniae can be categorized into three main patterns: the evolution of KL1/KL2-hvKp strains into CR-hvKp, the evolution of carbapenem-resistant K. pneumoniae (CRKp) strains into hv-CRKp, and the acquisition of hybrid plasmids carrying carbapenem resistance and virulence genes by classical K. pneumoniae (cKp). These strains are characterized by multi-drug resistance, high virulence, and high infectivity. Currently, there are no effective methods for treating and surveillance this pathogen. In addition, the continuous horizontal transfer and clonal spread of these bacteria under the pressure of hospital antibiotics have led to the emergence of more drug-resistant strains. This review discusses the evolution and distribution characteristics of hypervirulent and carbapenem-resistant K. pneumoniae , the mechanisms of carbapenem resistance and hypervirulence, risk factors for susceptibility, infection syndromes, treatment regimens, real-time surveillance and preventive control measures. It also outlines the resistance mechanisms of antimicrobial drugs used to treat this pathogen, providing insights for developing new drugs, combination therapies, and a "One Health" approach. Narrowing the scope of surveillance but intensifying implementation efforts is a viable solution. Monitoring of strains can be focused primarily on hospitals and urban wastewater treatment plants. • Outlines the traditional antimicrobial agents that have been used for treatment, along with their resistance mechanisms, immunotherapy, phage therapy, as well as emerging treatments that are currently in the experimental stage. • Three real-time surveillance methods available for clinical laboratories, which include polymerase chain reaction (PCR), whole-genome sequencing (WGS) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). These methods not only assist clinicians in rapidly adjusting treatment regimens and improving patient outcomes but also improve the surveillance system. • Focusing on a narrower scope of surveillance but intensifying its implementation is a viable control strategy, with surveillance primarily concentrated in hospitals and urban wastewater treatment plants. [ABSTRACT FROM AUTHOR]

ABSTRACT Pathogen typing is pivotal to detecting the emergence of high-risk clones in hospital settings and to limit their spread. Unfortunately, the most commonly used typing methods (i.e., pulsed-field gel electrophoresis [PFGE], multilocus sequence typing [MLST], and whole-genome sequencing [WGS]) are expensive or time-consuming, limiting their application to real-time surveillance. High-resolution melting (HRM) can be applied to perform cost-effective and fast pathogen typing, but developing highly discriminatory protocols is challenging. Here, we present hypervariable-locus melting typing (HLMT), a novel approach to HRM-based typing that enables the development of more effective and portable typing protocols. HLMT types the strains by assigning them to melting types (MTs) on the basis of a reference data set (HLMT-assignment) and/or by clustering them using melting temperatures (HLMT-clustering). We applied the HLMT protocol developed on the capsular gene wzi for Klebsiella pneumoniae on 134 strains collected during surveillance programs in four hospitals. Then, we compared the HLMT results to those obtained using wzi, MLST, WGS, and PFGE typing. HLMT distinguished most of the K. pneumoniae high-risk clones with a sensitivity comparable to that of PFGE and MLST+wzi. It also drew surveillance epidemiological curves comparable to those obtained using MLST+wzi, PFGE, and WGS typing. Furthermore, the results obtained using HLMT-assignment were consistent with those of wzi typing for 95% of the typed strains, with a Jaccard index value of 0.9. HLMT is a fast and scalable approach for pathogen typing, suitable for real-time hospital microbiological surveillance. HLMT is also inexpensive, and thus, it is applicable for infection control programs in low- and middle-income countries. IMPORTANCE In this work, we describe hypervariable-locus melting typing (HLMT), a novel fast approach to pathogen typing using the high-resolution melting (HRM) assay. The method includes a novel approach for gene target selection, primer design, and HRM data analysis. We successfully applied this method to distinguish the high-risk clones of Klebsiella pneumoniae, one of the most important nosocomial pathogens worldwide. We also compared HLMT to typing using WGS, the capsular gene wzi, MLST, and PFGE. Our results show that HLMT is a typing method suitable for real-time epidemiological investigation. The application of HLMT to hospital microbiology surveillance can help to rapidly detect outbreak emergence, improving the effectiveness of infection control strategies.

In response to escalating concerns about the indoor transmission of respiratory diseases, this study introduces a sophisticated software tool engineered to accurately determine contact rates among individuals in enclosed spaces-essential for public health surveillance and disease transmission mitigation. The tool applies YOLOv8, a cutting-edge deep learning model that enables precise individual detection and real-time tracking from video streams. An innovative feature of this system is its dynamic circular buffer zones, coupled with an advanced 2D projective transformation to accurately overlay video data coordinates onto a digital layout of the physical environment. By analyzing the overlap of these buffer zones and incorporating detailed heatmap visualizations, the software provides an in-depth quantification of contact instances and spatial contact patterns, marking an advancement over traditional contact tracing and contact counting methods. These enhancements not only improve the accuracy and speed of data analysis but also furnish public health officials with a comprehensive framework to develop more effective non-pharmaceutical infection control strategies. This research signifies a crucial evolution in epidemiological tools, transitioning from manual, simulation, and survey-based tracking methods to automated, real time, and precision-driven technologies that integrate advanced visual analytics to better understand and manage disease transmission in indoor settings.

Background: During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation., Objective: To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model's implementation and the assessment of the tool's effectiveness., Methods: In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks., Results: A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely-having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system., Conclusions: Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods., (©Rachel Martonik, Caitlin Oleson, Ellyn Marder. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 16.10.2024.)

This study describes the development of a pilot sentinel school absence syndromic surveillance system. Using data from a sample of schools in England the capability of this system to monitor the impact of disease on school absences in school-aged children is shown, using the coronavirus disease 2019 (COVID-19) pandemic period as an example. Data were obtained from an online app service used by schools and parents to report their children absent, including reasons/symptoms relating to absence. For 2019 and 2020, data were aggregated into daily counts of ‘total’ and ‘cough’ absence reports. There was a large increase in the number of absence reports in March 2020 compared to March 2019, corresponding to the first wave of the COVID-19 pandemic in England. Absence numbers then fell rapidly and remained low from late March 2020 until August 2020, while lockdown was in place in England. Compared to 2019, there was a large increase in the number of absence reports in September 2020 when schools re-opened in England, although the peak number of absences was smaller than in March 2020. This information can help provide context around the absence levels in schools associated with COVID-19. Also, the system has the potential for further development to monitor the impact of other conditions on school absence, e.g. gastrointestinal infections. [ABSTRACT FROM AUTHOR]

Background: Syndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally.Methods: We systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify "emergency department" and "syndromic surveillance" were applied to NICE healthcare, Global Health and Scopus databases.Results: In total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan).Conclusions: EDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to 'real-time', with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future.Prospero Number: CRD42017069150 . [ABSTRACT FROM AUTHOR]

A near-infrared (NIR) spectroscopy-based real-time monitoring system is proposed to sample and analyse agro-industrial raw materials transported in bulk in a single stage, easing and optimising the evaluation process of incoming lots at reception of agri-food plants. NIR analysis allows rapid and cost-effective analytical results to be obtained, and hence to rethink current sampling protocols. For this purpose, multistage and adaptive sampling designs were tested in this paper, which have been reported (in soil science and ecology) to be more flexible and efficient than conventional strategies to study patterns of clustering or patchiness, which can be the result of natural phenomena. The additional spatial information provided by NIR has also been exploited, using geostatistical analysis to model the spatial pattern of key analytical constituents in Processed Animal Proteins (PAPs). This study addresses the assessment of two kinds of quality/safety issues in PAP lots – moisture accumulation and cross-contamination. After a simulation study, qualitative and quantitative analyses were carried out to make a performance comparison between sampling designs. Results show that sampling densities below 10–15% demonstrated higher estimation errors, failing to represent the actual spatial patterns, while a stratified adaptive cluster sampling design achieved the best performance. Image 1 • Real-time monitoring of raw materials transported in bulk. • In-situ control based on Adaptive sampling and Near Infrared Spectroscopy. • Spatial analysis of analytical constituents in agri-food products. [ABSTRACT FROM AUTHOR]

Highlights • Extracted parameters were very informative in ARDS prediction. • Selection of pertinent parameters raised the sensitivity of the model. • Discounting enhanced the overall performance. • ARDS is predicted earlier to its onset with high performances. Abstract Real-time health surveillance becomes important and necessary with the increase of the elderly population to preserve their quality of life. Real-time models aim to provide alerts before the severe illness occurs. Acute respiratory distress syndrome is a crucial disease of the respiratory system that threats the health of the elderly. This paper proposes a real-time model for the surveillance of ARDS based on belief functions theory. Non-invasive physiological signals are considered such as heart rate, respiratory rate, oxygen saturation and mean airway blood pressure. Different linear and nonlinear parameters are extracted from these signals; then a parameters selection procedure is performed to reduce their dimensionality. Afterwards, classifiers are constructed using parameters distributions defined in the evidence framework. Real-time prediction is then performed by combining all classifiers decisions. As results, high performances are obtained over the testing sets with performances of 77% and 71% for sensitivity and specificity, respectively. [ABSTRACT FROM AUTHOR]

• Model-based gait recognition techniques apt for handling covariates. • An exploration of various parameters of deep learning pipelines. • List of covariates-based gait datasets for training robust gait recognition system. • Overview of gait sensing technologies with their benefits and limitations. • Research gaps and challenges in the implementation of deep learning pipelines. Deep learning (DL) pipelines have evolved for over a decade now and are efficient at solving many challenging problems of image and signal processing applications. Designing deep learning pipelines for a particular application requires a good understanding of deep learning and various intermediate layers available. To develop a DL pipeline, one uses available dataset(s) suitable for an application, and the pipeline is refined by iterating over intermediate layers. A large amount of time and extensive thinking goes into these selections and validating the performance of each configuration. Thus, it is hard to choose the correct and robust DL pipeline that performs well on all relevant datasets. This review aims to aid researchers in understanding different gait sensing technologies and provide foundational knowledge of the deep learning concepts for faster solutions for a given problem. Gait recognition is more recent since it hasn't yet been used in a real-world situation. This article provides a comprehensive overview of gait biometrics suited to real-time surveillance applications. All the important parameters of deep learning pipelines are explained, along with their selection and implication for a given problem. Authors have reviewed important research articles recently on deep learning models and how these perform across different application datasets. The benefits and drawbacks of the approaches are elucidated to help arrive at the optimized pipeline derived from a fusion of available pipelines to achieve faster but accurate results for a given problem. [ABSTRACT FROM AUTHOR]

Pyrenopeziza brassicae (anamorph Cylindrosporium concentricum) is an ascomycete fungus that causes light leaf spot ( LLS) disease of brassicas. It has recently become the most important pathogen of winter oilseed rape ( Brassica napus) crops in the UK. The pathogen is spread by both asexual splash-dispersed conidia and sexual wind-dispersed ascospores. Such inoculum can be detected with existing qualitative and quantitative PCR diagnostics, but these require time-consuming laboratory-based processing. This study describes two loop-mediated isothermal amplification ( LAMP) assays, targeting internal transcribed spacer ( ITS) or β-tubulin DNA sequences, for fast and specific detection of P. brassicae isolates from a broad geographical range (throughout Europe and Oceania) and multiple brassica host species ( B. napus, B. oleracea and B. rapa). Neither assay detected closely related Oculimacula or Rhynchosporium isolates, or other commonly occurring oilseed rape fungal pathogens. Both LAMP assays could consistently detect DNA amounts equivalent to 100 P. brassicae conidia per sample within 30 minutes, although the β-tubulin assay was more rapid. Reproducible standard curves were obtained using a P. brassicae DNA dilution series (100 ng-10 pg), enabling quantitative estimation of amounts of pathogen DNA in environmental samples. In planta application of the β-tubulin sequence-based LAMP assay to individual oilseed rape leaves collected from the field found no statistically significant difference in the amount of pathogen DNA present in parts of leaves either with or without visible LLS symptoms. The P. brassicae LAMP assays described here could have multiple applications, including detection of symptomless host infection and automated real-time monitoring of pathogen inoculum. [ABSTRACT FROM AUTHOR]

• A customized image dataset is built for research on face mask detection. • The dataset is manually labeled to provide high annotation accuracy. • For Face mask detection customized CNN with multi-step image processing is used. • The performance of the proposed CNN is compared with YOLO v3 and Faster R-CNN. • Two publicly available datasets including MAFA and MOXA used for validation. Face mask detection has several applications including real-time surveillance, biometrics, etc. Face mask detection is also useful for surveillance of the public to ensure face mask wearing in public places. Ensuring that people are wearing a face mask is not possible with monitoring staff; instead, automatic systems are a much better choice for face mask detection and monitoring to help manage public behaviour and contribute to restricting the outbreak of COVID-19. Despite the availability of several such systems, the lack of a real image dataset is a big hurdle to validating state-of-the-art face mask detection systems. In addition, using the simulated datasets lack the analysis needed for real-world scenarios. This study builds a new dataset namely RILFD by taking real pictures using a camera and annotating them with two labels (with mask, without mask) which are publicly available for future research. In addition, this study investigates various machine learning models and off-the-shelf deep learning models YOLOv3 and Faster R-CNN for the detection of face masks. The customized CNN models in combination with the 4 steps of image processing are proposed for face mask detection. The proposed approach outperforms other models and proved its robustness with a 97.5% of accuracy score in face mask detection on the RILFD dataset and two publicly available datasets (MAFA and MOXA). [ABSTRACT FROM AUTHOR]

This paper introduces a method of surveillance using deviations from probabilistic forecasts. Realised observations are compared with probabilistic forecasts, and the "deviation" metric is based on low probability events. If an alert is declared, the algorithm continues to monitor until an all-clear is announced. Specifically, this article addresses the problem of syndromic surveillance for influenza (flu) with the intention of detecting outbreaks, due to new strains of viruses, over and above the normal seasonal pattern. The syndrome is hospital admissions for flu-like illness, and hence, the data are low counts. In accordance with the count properties of the observations, an integer-valued autoregressive process is used to model flu occurrences. Monte Carlo evidence suggests the method works well in stylised but somewhat realistic situations. An application to real flu data indicates that the ideas may have promise. The model estimated on a short run of training data did not declare false alarms when used with new observations deemed in control, ex post. The model easily detected the 2009 H1N1 outbreak. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends. [ABSTRACT FROM AUTHOR]

Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion. [ABSTRACT FROM AUTHOR]

Illegally parked vehicle detection systems are considered crucial elements in the development of any video-surveillance based traffic-management system. The major challenges in this task lie in making the end solution real time, illumination invariant and occlusion tolerant. A two-stage application framework is presented which efficiently identifies vehicles parked illegally in restricted parking-zones. A real-time approach has been followed and an improved foreground segmentation method based on Segmentation History Images (SHI) is developed to identify stationary objects. A three step pixel based classification method is applied on the background segmentation output to segment adjacent moving pixels that become stationary for certain periods of time. The process then locks on to all identified stationary pixel patches, parts of which overlap with the regions of interest marked interactively a priori. The second stage of the process is applied subsequently to track all the stationary pixel patches detected during the first stage using an adaptive edge orientation based tracking method. Experimental results show that the tracking technique gives more than a 90% detection success rate, even if objects are partially occluded. The technique has been tested on the UK Home Office i-LIDS Parked Vehicle video sequences along with the University of Sussex Traffic Dataset and results are compared with other available state of the art methods. [ABSTRACT FROM AUTHOR]

Abstract: This study presents an inception-to-maturity data acquisition philosophy and deriving value from such surveillance. Specifically, translating wellhead pressures (WHP) to bottomhole pressures (BHP) allowed many reservoir-engineering calculations during the flow period. While conversion of WHP to BHP proved feasible during a flow test, measurements showed that shut-in tests do not lend themselves for such treatment because of rapid heat dissipation of a low-heat-capacity fluid, such as gas. Therefore, we relied upon flow-after-flow (FAF) tests that were embedded in monthly variable-rate production measurements to obtain average reservoir pressure and absolute open-flow potential. These average pressures enriched those that were obtained with downhole gauges from shut-in tests for the material-balance analysis. The resultant time-dependent inflow–performance relationship (IPR) and absolute-open-flow potential helped understand well performance. Indeed, evolution of declining IPR slope led to the identification of gradual wellbore blockage in one of the wells completed openhole. Downhole video recording confirmed mechanical issues in two openhole completions. Production logging showed preferential flow from the upper section of the thick carbonate interval in two wells. However, residual doubts remained about possible flow up the annulus in the openhole/slotted liner completions. Analytic modeling confirmed that the notion of preferential flow up the annulus is untenable. [ABSTRACT FROM AUTHOR]

Onboard-diagnostic (OBD) system is developed to detect engine operation conditions for air-pollution monitoring. The diagnostic trouble code from vehicle microprocessor is generated resulting from a system error or malfunction. Based on a practical demand, this paper presents a modified system construction from vehicle surveillance technology for OBD data report in real time. A circuit system is designed and fabricated to convert OBD protocol into RS-232 protocol for data transmission via general-packet-radio-service mobile communication. The pro- posed system integrates the developing technology for both OBD and intelligent-transportation-system applications. In this paper, hardware and software in both design and implementation are discussed and verified by road tests. [ABSTRACT FROM AUTHOR]

Background: For the last 10 years, mobile phones have provided the global health community with innovative and cost-effective strategies to address the challenges in the prevention and management of dengue fever., Objective: The aim is to introduce and describe the design and development process of Mozzify, an integrated mobile health (mHealth) app that features real-time dengue fever case reporting and mapping system, health communication (real-time worldwide news and chat forum/timeline, within-app educational videos, links to local and international health agency websites, interactive signs and symptoms checker, and a hospital directions system), and behavior modification (reminders alert program on the preventive practices against dengue fever). We also aim to assess Mozzify in terms of engagement and information-sharing abilities, functionality, aesthetics, subjective quality, and perceived impact., Methods: The main goals of the Mozzify app were to increase awareness, improve knowledge, and change attitudes about dengue fever, health care-seeking behavior, and intention-to-change behavior on preventive practices for dengue fever among users. It was assessed using the Mobile Application Rating Scale (MARS) among 50 purposively sampled individuals: public health experts (n=5), environment and health-related researchers (n=23), and nonclinical (end users) participants (n=22)., Results: High acceptability and excellent satisfaction ratings (mean scores ≥4.0 out of 5) based on the MARS subscales indicate that the app has excellent user design, functionality, usability, engagement, and information among public health experts, environment and health-related researchers, and end users. The app's subjective quality (recommending the app to other people and the app's overall star rating), and specific quality (increase awareness, improve knowledge, and change attitudes about dengue fever; health care-seeking behavior; and intention-to-change behavior on preventive practices for dengue fever) also obtained excellent satisfaction ratings from the participants. Some issues and suggestions were raised during the focus group and individual discussions regarding the availability of the app for Android devices, language options limitations, provision of predictive surveillance, and inclusion of other mosquito-borne diseases., Conclusions: Mozzify may be a promising integrated strategic health intervention system for dengue fever case reporting and mapping; increase awareness, improve knowledge, and change attitude about dengue fever; and disseminating and sharing information on dengue fever among the general population and health experts. It also can be an effective aid in the successful translation of knowledge on preventive measures against dengue fever to practice., (©Von Ralph Dane Marquez Herbuela, Tomonori Karita, Micanaldo Ernesto Francisco, Kozo Watanabe. Originally published in JMIR Formative Research (http://formative.jmir.org), 08.01.2020.)

Background: Pre-exposure prophylaxis, or PrEP (brand name Truvada), is a once daily medication that reduces the chance of contracting HIV by more than 90% for those facing an elevated risk of HIV exposure. When starting PrEP, it takes at least seven days to reach high levels of protection and seven consecutive days to maintain protection. Unfortunately, many do not follow the guidelines for PrEP, leaving them vulnerable. Metro Wellness and Community Centers (Metro Wellness) provides comprehensive HIV services throughout Tampa Bay. Metro Wellness, with Mail-Meds Clinical Pharmacy (Mail-Meds), deployed a new technology, Nomi, to support patients in their journey of HIV prevention. Nomi accurately captures data through a connected prescription bottle to reveal how patients take their medications in real-time. Nomi translates data into actionable information for automatic patient interventions and engagement. Objective: To understand patients' ability to adopt innovative technology to maintain adherence to the PrEP regimen. In addition, the study will determine if technology converts non adherent days to adherent through automated, personalized interventions. Methods: Patients enrolled in Nomi must be HIV negative, at high-risk for HIV infection, receiving PrEP for the first time through Metro Wellness, and have a cell phone with SMS capabilities. Patients receive their Truvada prescription in a connected bottle that measures the amount of medication taken by weight. The prescriptions are filled by Mail-Meds in clinic pharmacy. As patients take Truvada, the bottle sends data to Nomi. Nomi reviews the data and sends automatic text interventions and escalations based on the patient's behavior. The texts sent are designed to be discrete to ensure patient privacy. Nomi also communicates with Metro Wellness staff. Staff receive escalations from Nomi to reach out the patents needing additional assistance. Results: The program is ongoing. All reported results are as of July 31, 2018. Forty-nine patients have been enrolled. Days on therapy range from 29 to 378. The average length of therapy is 107 days. Reasons for ending therapy include, patients changing prescribers, declining therapy, and side effects. A total of 1914 SMS text interventions have been sent. Patients need an intervention 24% of the time and convert 48% (n=917) of the time. Additionally, 45% of conversions (n=412) occurred within 1 hour of the intervention. Ninety percent (n=44) of the patients responded directly to Nomi at least once. A total of 564 total responses have been received. Each patient has sent an average of 13 responses. Conclusions: Patients had no perceived barriers to adopting Nomi. In fact, patients enjoy participating and interact frequently through responses. Personalized interventions, based on real-time data, quickly change patient behavior from what would have been a missed day, to a correct day. Metro Wellness integrated Nomi and medication adherence into the daily lives of their patients, through a direct communication channel. By using Nomi, Metro Wellness staff is able to connect with their patients more frequently, building stronger relationships, which has improved adherence in order to prevent HIV infection. [ABSTRACT FROM AUTHOR]

Global health research can transform clinical and surgical practice worldwide. Partnerships between US academic centers and hospitals in low- and middle-income counties can improve clinical care at the host institution hospital and give the visiting institution access to a large volume of valuable research data. Recognizing the value of these partnerships, the University of North Carolina (UNC) formed a partnership with Kamuzu Central Hospital (KCH) in Lilongwe, Malawi. The Department of Neurosurgery joined the partnership with KCH and designed a Head Trauma Surveillance Registry. The success of this registry depended on the development of methods to accurately collect head injury data at KCH. Since medical record documentation is often unreliable in this setting, data collection teams were implemented to capture data from head trauma patients on a 24-hours-a-day, 7-days-a-week basis. As data collection improved, pilot groups tested methods to collect new variables and the registry expanded. UNC provided onsite and remote oversight to strengthen the accuracy of the data. Data accuracy still remains a hurdle in global research. Data collection teams, oversight from UNC, pilot group testing, and meaningful collaboration with local physicians improved the accuracy of the head trauma registry. Overall, these methods helped create a more accurate epidemiological and outcomes-centered analysis of brain injury patients at KCH to date.