Performance Of Surveillance System Research Articles

An evaluation of the severe acute respiratory infection surveillance system in Ireland

BackgroundExpansion of the current single hospital site Severe Acute Respiratory Infection (SARI) surveillance programme in Ireland, to create a SARI sentinel hospital network, commenced in 2023. This evaluation aimed to assess the performance of key surveillance system attributes to improve and guide expansion.MethodsA mixed quantitative and qualitative evaluation was conducted from September 2021 to November 2022 using European Centre for Disease Prevention and Control (ECDC) guidance as a framework. SARI surveillance data were analysed to assess completeness and timeliness. Semi-structured focus group discussions and key informant interviews and an online stakeholder questionnaire, evaluated usefulness, simplicity, flexibility and acceptability. Qualitative data were analysed using thematic analysis.ResultsCompleteness of key variables including age and outcome was 100% (n = 747). COVID-19 vaccination status was completed in 89%, completeness of ethnicity data was only 0.6%. Median time from hospitalisation to commencement of data entry was one day (IQR 1–3). Time to receipt of whole genome sequencing (WGS) results was 40 days (IQR 30–61). Themes identified in the qualitative evaluation were the complexity of SARI surveillance, dependence on key individuals and skilled staff. Suggested areas for improvement included process automation and additional resources to improve timeliness and acceptability, particularly during epidemic or pandemic surge periods.ConclusionsThe SARI surveillance system reports complete and timely data. However, improvement in the timeliness of WGS data and recording of vaccination status and equity stratifiers e.g. ethnicity, would strengthen data quality. Priorities for expansion include process automation and adequate resource allocation, to enable reliable SARI surveillance at all times.

The Second Round of a Population-Based Seroprevalence Study of Anti-SARS-CoV-2 Antibodies and COVID-19 Vaccination Assessment in the Republika Srpska, Bosnia and Herzegovina.

The aim of the study was to assess the seroprevalence of SARS-CoV-2 in the Republika Srpska, Bosnia and Herzegovina, after five waves of COVID-19 and 1 year after introduction of vaccination to better understand the true extent of the COVID-19 pandemic in the population of the Republika Srpska and role of vaccination in achieving herd immunity. The population-based study was conducted from December 2021 to February 2022 in a group of 4463 individuals in the Republika Srpska. Total anti-SARS-CoV-2 antibodies were determined in serum specimens using the Wantai total antibody ELISA assay. Quantitative analysis, using Kantaro IgG assays, was performed in subsamples (1273 specimens) to asses and compare levels of IgG among vaccinated, recovered and participants with hybrid immunity. To adjust for age and gender distribution in sample, poststratification method is applied. The overall cumulative seroprevalence was 94.6% (95% CI = 93.9-95.3). Significantly higher seroprevalence rates were observed among vaccinated 97.8% (95% CI = 97.3-98.4) comparing to unvaccinated participants (89.5%, 95% CI = 88.0-91.0). Seroprevalence increases with the number of received doses. Among various professions, the highest seroprevalence was found in the service industry (98.1%), education (98.0%) and healthcare (96.9%). We found that 2.2% of vaccinated and 3.6% of participants with SARS-CoV-2 positivity during 2021 had no detectable IgG antibodies. Both seroprevalence (98.6%) and antibody titres (1094.4 AU/mL) were significantly higher among people with hybrid immunity. Our findings reveal a 2.3-fold increase in seroprevalence of SARS-CoV-2 antibodies due to infection and vaccination, comparing to the first study performed 1 year earlier. This study provides better understanding of the SARS-CoV-2 transmission and highlights the important role of the vaccination in achieving the population immunity. Periodically conducted population-based seroprevalence studies are important to understand temporal trends and assess surveillance system performance and public compliance with vaccination policies.

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics

ObjectiveDigital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks.ResultsOBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Evaluation of cholera surveillance systems in Africa: a systematic review.

Despite several interventions on the control of cholera, it still remains a significant public health problem in Africa. According to the World Health Organization, 251,549 cases and 4,180 deaths (CFR: 2.9%) were reported from 19 African countries in 2023. Tools exist to enhance the surveillance of cholera but there is limited evidence on their deployment and application. There is limited evidence on the harmonization of the deployment of tools for the evaluation of cholera surveillance. We systematically reviewed available literature on the deployment of these tools in the evaluation of surveillance systems in Africa. Three electronic databases (PubMed, Medline and Embase) were used to search articles published in English between January 2012 to May 2023. Grey literature was also searched using Google and Google Scholar. Only articles that addressed a framework used in cholera surveillance in Africa were included. The quality of articles was assessed using the appropriate tools. Data on the use of surveillance tools and frameworks were extracted from articles for a coherent synthesis on their deployment. A total of 13 records (5 frameworks and 8 studies) were fit for use for this study. As per the time of the study, there were no surveillance frameworks specific for the evaluation of surveillance systems of cholera in Africa, however, five frameworks for communicable diseases and public health events could be adapted for cholera surveillance evaluation. None (0%) of the studies evaluated capacities on cross border surveillance, multisectoral one health approach and linkage of laboratory networks to surveillance systems. All (100%) studies assessed surveillance attributes even though there was no synergy in the attributes considered even among studies with similar objectives. There is therefore the need for stakeholders to harmoniously identify a spectrum of critical parameters and attributes to guide the assessment of cholera surveillance system performance.

AI Enhancement Automated Movement Detection

This paper introduces an innovative artificial intelligence (AI) method aimed at tackling the challenges associated with detecting and tracking moving objects in video surveillance systems. By utilizing self-organization through artificial neural networks, our approach effectively manages scenes with dynamic backgrounds and gradual changes in lighting, ensuring robust detection across different types of videos recorded by stationary cameras. In the realm of moving object detection, our method leverages the adaptability of neural networks, enabling precise detection in complex visual environments. For object tracking, we propose a combination of Kalman filtering techniques and a sophisticated matching model based on Multiple Hypothesis Testing, ensuring accurate and consistent tracking across frames. Through experimental validation using various color video sequences, we demonstrate the effectiveness and reliability of our approach, highlighting its potential to enhance the performance of surveillance systems in real-world scenarios.

Evaluation of the national AIDS program and HIV/AIDS surveillance system in Jordan.

The National AIDS Program in Jordan has faced numerous challenges, including insufficient funding, limited resources, and complexities brought on by refugee influxes. The absence of a reliable HIV/AIDS surveillance system further complicates tracking and responding to the epidemic. This study aimed to evaluate the performance and functionality of the National AIDS Program and the HIV/AIDS surveillance system. A qualitative study was conducted to evaluate the National AIDS Program and the HIV/AIDS Surveillance System in Jordan. The study involved 14 key informants specialized in HIV/AIDS Surveillance Systems. Interviews were guided by an interview guide based on the Updated Centers for Disease Control and Prevention Guidelines for Evaluating Public Health Surveillance Systems. Data analysis was performed through directed content analysis. The Voluntary Counseling and Testing Center in Jordan confronts multiple challenges, including poor infrastructure, limited accessibility, and inadequate facilities. Additionally, there is a lack of effective coordination between the Voluntary Counseling and Testing center and the Chest Diseases and Migrant Health Directorate, particularly regarding testing for Tuberculosis. HIV screening in Jordan is hindered by various factors, including disease sensitivity. Voluntary Counseling and Testing lacks HIV/AIDS specialists AND staff adequate training and fails to adhere to updated treatment guidelines. Persistent deficiencies in human resources, equipment, and training continue to plague the HIV/AIDS Surveillance System. Key informants expressed dissatisfaction with the data's usefulness, citing concerns over poor data quality. The data were seldom used for prioritizing resources, identifying at-risk individuals, assessing HIV/AIDS risk factors, or evaluating prevention and control measures. The National AIDS Program and HIV/AIDS Surveillance System activities in Jordan are unstructured, poorly coordinated, and inefficient. Many gaps related to National AIDS Program and HIV/AIDS Surveillance System performance and data were identified. Recommendations include developing an electronic surveillance system for data collection, notification, and reporting, and building the capacities of HIV/AIDS healthcare providers in screening, diagnosis, and management.

A practical object detection-based multiscale attention strategy for person reidentification

<p>In person reidentification (PReID) tasks, challenges such as occlusion and small object sizes frequently arise. High-precision object detection methods can accurately locate small objects, while attention mechanisms help focus on the strong feature regions of objects. These approaches mitigate the mismatches caused by occlusion and small objects to some extent. This paper proposes a PReID method based on object detection and attention mechanisms (ODAMs) to achieve enhanced object matching accuracy. In the proposed ODAM-based PReID system, You Only Look Once version 7 (YOLOv7) was utilized as the detection algorithm, and a size attention mechanism was integrated into the backbone network to further improve the detection accuracy of the model. To conduct feature extraction, ResNet-50 was employed as the base network and augmented with residual attention mechanisms (RAMs) for PReID. This network emphasizes the key local information of the target object, enabling the extraction of more effective features. Extensive experimental results demonstrate that the proposed method achieves a mean average precision (mAP) value of 90.1% and a Rank-1 accuracy of 97.2% on the Market-1501 dataset, as well as an mAP of 82.3% and a Rank-1 accuracy of 91.4% on the DukeMTMC-reID dataset. The proposed PReID method offers significant practical value for intelligent surveillance systems. By integrating multiscale attention and RAMs, this method enhances both its object detection accuracy and its feature extraction robustness, enabling a more efficient individual identification process in complex scenes. These improvements are crucial for enhancing the real-time performance and accuracy of video surveillance systems, thus providing effective technical support for intelligent monitoring and security applications.</p>

Explainable artificial intelligence models for enhancing classification reliability of ground weapon systems

This study focused on the development of a reliable artificial intelligence (AI) model to enhance the classification reliability of ground weapon systems for surveillance and reconnaissance applications. The proposed AI model overcomes the limited data availability of military objects such as tanks, canons, and multiple-launch rockets by leveraging transfer learning and fine-tuning techniques. A comprehensive evaluation of 35 deep learning models using the publicly available Military-Vehicles dataset on Kaggle identified MobileNet as the most suitable model for ground weapon system classification. The selected MobileNet model achieved an average F1 score of 92% when tested on a dataset comprising five types of ground-weapon systems. In addition, the application of the explainable AI technique Grad-CAM provided insights into the decision-making process of the proposed model and verified its reliability. Real-world evaluations using frames extracted from training videos demonstrated promising accuracy for tanks, canons, and multiple-launch rockets. However, challenges related to object occlusion and the absence of target objects in the images were observed, which resulted in misclassifications. Overall, this study contributes to the development of explainable and reliable AI models for enhancing the performance of ground surveillance and reconnaissance systems.

An evaluation of the severe acute respiratory infection (SARI) surveillance system in Ireland

Abstract Background Expansion of the current single hospital site Severe Acute Respiratory Infection (SARI) surveillance programme in Ireland, to create a sentinel network, will commence in 2023. This evaluation aimed to assess the performance of key surveillance system attributes to improve and guide expansion. Methods A mixed quantitative and qualitative evaluation was conducted from September 2021 to November 2022. SARI surveillance data were analysed to assess completeness, timeliness and positive predictive value (PPV). An online stakeholder questionnaire, semi-structured focus group discussions and key informant interviews evaluated usefulness, simplicity, flexibility and acceptability. Qualitative data were analysed using thematic analysis. Results Among SARI cases (n = 747), completeness of key variables including age, sex and outcome was 100%. Symptom data were completed in > 90%, COVID-19 vaccination status was completed in 89% and completeness of ethnicity data was 0.6%. Median times from hospitalisation to data entry commencement and completion were one day (IQR 1-3) and 48 days (IQR 21-81) respectively. Time to receipt of whole genome sequencing (WGS) results was 40 days (IQR 30-61). PPV was 95% (95% CI 93%-97%). The questionnaire response rate was 21% (75/362) and 96% (72/75) agreed that SARI surveillance is important. Key themes identified in the qualitative study (n = 11) were the complexity and importance of SARI surveillance. Suggested areas for improvement included process automation and additional designated resources to improve timeliness, flexibility and acceptability; particularly during epidemic or pandemic surge periods. Conclusions The SARI surveillance system reports high-quality data. Timeliness of WGS data and recording of vaccination status and equity stratifiers e.g., ethnicity should be improved. Priorities for expansion should be automation and adequate resource allocation to enable reliable SARI surveillance at all times. Key messages • The SARI surveillance system is a resource intensive, complex system that is an important component of national surveillance in Ireland and reports high-quality and timely surveillance data. • The priorities for expansion are process automation where possible and ensuring adequate resources, specifically skilled staff. These evaluation findings should inform SARI expansion in Ireland.

Evaluation of the Suicide Attempt Surveillance System in Santiago de Cali, Colombia, 2016–2019

IntroductionSuicidal behaviors are one of the top ten causes of death worldwide. It is recommended to prioritize the surveillance of suicide attempts, as this is a useful risk factor in the short-term prediction of suicide. In Colombia, its surveillance was implemented in 2016. The study evaluated the performance of the suicide attempt surveillance system in Cali, Colombia, in the period 2016–2019. Material and methodsThe attributes of timeliness, data quality and representativeness were evaluated according to the CDC Guidelines for Violence and Injury Surveillance, in addition to describing patterns of occurrence through time series. ResultsThe median timeliness of notification was 0 days (RIQ=3). The variables exposure to violence (73.5%), depressive disorder (49.6%) and personality disorder (44.1%) had the highest percentages of missing data. The system identifies priority populations similar to other sources of information, such as young people between 15 and 24 years of age (28.1%), women (64.8%), students (35.5%) and the single population (69.6%); triggering factors such as relationship problems (35.6%), economic problems (13.7%) and serious illness (7%); and mechanisms used such as intoxication (61.6%), short stabbing weapon (27.6%) and hanging (5.3%). The system predicts 22 cases (95% CI=21.6–22.3) per week of attempted suicide in the short term. ConclusionsThe system meets the attributes of quality, timeliness and representativeness. It also allows the identification of an expected threshold of cases that needs to be validated to detect outbreak situations. This study contributes to the knowledge and prevention of the suicidal phenomenon.

Wastewater Surveillance for SARS-CoV-2 at Long-Term Care Facilities: Mixed Methods Evaluation.

Wastewater surveillance provided early indication of COVID-19 in US municipalities. Residents of long-term care facilities (LTCFs) experienced disproportionate morbidity and mortality early in the COVID-19 pandemic. We implemented LTCF building-level wastewater surveillance for SARS-CoV-2 at 6 facilities in Kentucky to provide early warning of SARS-CoV-2 in populations considered vulnerable. This study aims to evaluate the performance of wastewater surveillance for SARS-CoV-2 at LTCFs in Kentucky. We conducted a mixed methods evaluation of wastewater surveillance following Centers for Disease Control and Prevention (CDC) guidelines for evaluating public health surveillance systems. Evaluation steps in the CDC guidelines were engaging stakeholders, describing the surveillance system, focusing the evaluation design, gathering credible evidence, and generating conclusions and recommendations. We purposively recruited stakeholders for semistructured interviews and undertook thematic content analysis of interview data. We integrated wastewater, clinical testing, and process data to characterize or calculate 7 surveillance system performance attributes (simplicity, flexibility, data quality, sensitivity and positive predictive value [PPV], timeliness, representativeness, and stability). We conducted 8 stakeholder interviews. The surveillance system collected wastewater samples (N=811) 2 to 4 times weekly at 6 LTCFs in Kentucky from March 2021 to February 2022. Synthesis of credible evidence indicated variable surveillance performance. Regarding simplicity, surveillance implementation required moderate human resource and technical capacity. Regarding flexibility, the system efficiently adjusted surveillance frequency and demonstrated the ability to detect additional pathogens of interest. Regarding data quality, software identified errors in wastewater sample metadata entry (110/3120, 3.53% of fields), technicians identified polymerase chain reaction data issues (140/7734, 1.81% of reactions), and staff entered all data corrections into a log. Regarding sensitivity and PPV, using routine LTCF SARS-CoV-2 clinical testing results as the gold standard, a wastewater SARS-CoV-2 signal of >0 RNA copies/mL was 30.6% (95% CI 24.4%-36.8%) sensitive and 79.7% (95% CI 76.4%-82.9%) specific for a positive clinical test at the LTCF. The PPV of the wastewater signal was 34.8% (95% CI 27.9%-41.7%) at >0 RNA copies/mL and increased to 75% (95% CI 60%-90%) at >250 copies/mL. Regarding timeliness, stakeholders received surveillance data 24 to 72 hours after sample collection, with delayed reporting because of the lack of weekend laboratory staff. Regarding representativeness, stakeholders identified challenges delineating the population contributing to LTCF wastewater because of visitors, unknown staff toileting habits, and the use of adult briefs by some residents preventing their waste from entering the sewer system. Regarding stability, the reoccurring cost to conduct 1 day of wastewater surveillance at 1 facility was approximately US $144.50, which included transportation, labor, and materials expenses. The LTCF wastewater surveillance system demonstrated mixed performance per CDC criteria. Stakeholders found surveillance feasible and expressed optimism regarding its potential while also recognizing challenges in interpreting and acting on surveillance data.

Changes in epidemic intensity of influenza during 2014-2020 in Shanghai

Objective: To evaluate the performance of the influenza surveillance network and compare the epidemic intensity of influenza during 2014-2020 in Shanghai. Methods: Based on the weekly reports of influenza-like illness (ILI) and laboratory-confirmed influenza cases from January 1, 2014 to December 31, 2020. This study first evaluated the data reporting and specimen collection of ILI cases for each sentinel hospital, and then calculated the percentage of ILI (ILI%), the proportion of specimens tested positive for influenza, and the incidence of influenza among all ILI outpatient and emergency visits to measure the epidemic intensity of influenza. Finally, seasonal autoregressive integrated moving average (ARIMA) model was applied to quantify the changes in epidemic intensity of influenza in 2020. Results: The proportion of influenza surveillance sentinel hospitals with a score of less than 5 in the evaluation of ILI data reporting and samples collection were 9.68% and 21.05% in 2020 in Shanghai, respectively. ILI% was estimated to be 1.51% (95%CI: 1.50%-1.51%) and 2.31% (95%CI: 2.30%-2.32%), respectively for 2014-2019 and 2020; the proportion of specimens tested positive was 24.27% (95%CI: 24.02%- 24.51%) and 7.15% (95%CI: 6.78%-7.54%), respectively; and the incidence of influenza was 3.66‰ (95%CI: 3.62‰-3.70‰) and 1.65‰ (95%CI: 1.57‰-1.74‰), respectively. ARIMA model showed that ILI% was increased by 45.25% in 2020 in Shanghai, and the proportion of specimens tested positive and the incidence of influenza were reduced by 78.45% and 51.80%, respectively. Conclusions: In 2020, the performance of influenza surveillance system has changed, ILI% has increased, the proportion of specimens tested positive and the incidence of influenza has decreased in Shanghai. The change in the quality of influenza surveillance is also a potential factor affecting the epidemic intensity of influenza. In the future, the quality control of influenza surveillance network still needs to be further strengthened.

Surveillance detection of anomalous activities with optimized deep learning technique in crowded scenes

The performance of conventional surveillance systems is challenged by high error detection rates in busy scenes, which has significantly affected the accurate detection of the current surveillance system. Feature representation and object pattern extraction from different scenes have made deep learning (DL) promising methods in surveillance systems, compared to the approaches where features are created manually. To improve the detection accuracy, this paper presents an intelligent DL technique that combines convolutional neural network (CNN) and long short-term memory (LSTM). CNN extracts and learns the object features from a set of raw images, while the LSTM is then used by gated mechanisms to store important information from the extracted features. The proposed method was validated using datasets from the University of California San Diego (UCSD). The result shows that the model achieves 95% accuracy, which is superior compared to other conventional detection models.

Evaluation of the influenza-like illness sentinel surveillance system: A national perspective in Tanzania from January to December 2019.

The World Health Organization (WHO) recommends periodic evaluations of influenza surveillance systems to identify areas for improvement and provide evidence of data reliability for policymaking. However, data on the performance of established influenza surveillance systems are limited in Africa, including Tanzania. We aimed to assess the usefulness of the Influenza surveillance system in Tanzania and to ascertain if the system meets its objectives, including; estimating the burden of disease caused by the Influenza virus in Tanzania and identifying any circulating viral strains with pandemic potential. From March to April 2021, we collected retrospective data through a review of the Tanzania National Influenza Surveillance System electronic forms for 2019. Furthermore, we interviewed the surveillance personnel about the system's description and operating procedures. Case definition (ILI-Influenza Like Illness and SARI-Severe Acute Respiratory Illness), results, and demographic characteristics of each patient were obtained from the Laboratory Information System (Disa*Lab) at Tanzania National Influenza Center. The United States Centers for disease control and prevention updated guidelines for evaluating public health surveillance systems were used to evaluate the system's attributes. Additionally, the system's performance indicators (including turnaround time) were obtained by evaluating Surveillance system attributes, each being scored on a scale of 1 to 5 (very poor to excellent performance). A total of 1731 nasopharyngeal and oropharyngeal samples were collected from each suspected influenza case in 2019 from fourteen (14/14) sentinel sites of the influenza surveillance system in Tanzania. Laboratory-confirmed cases were 21.5% (373/1731) with a predictive value positive of 21.7%. The majority of patients (76.1%) tested positive for Influenza A. Thirty-seven percent of patients' results met the required turnaround time, and 40% of case-based forms were incompletely filled. Although the accuracy of the data was good (100%), the consistency of the data was below (77%) the established target of ≥ 95%. The overall system performance was satisfactory in conforming with its objectives and generating accurate data, with an average performance of 100%. The system's complexity contributed to the reduced consistency of data from sentinel sites to the National Public Health Laboratory of Tanzania. Improvement in the use of the available data could be made to inform and promote preventive measures, especially among the most vulnerable population. Increasing sentinel sites would increase population coverage and the level of system representativeness.

Intelligent Reflecting Surface Aided Wireless Information Surveillance

This paper investigates a new concept of employing intelligent reflecting surface (IRS) to enhance the monitoring performance of wireless information surveillance system, where a full-duplex legitimate monitor is employed to eavesdrop the suspicious transmission from a transmitter to a receiver with the help of an IRS. Under this setup, we consider three IRS deployment strategies, where the IRS is placed near the suspicious transmitter, the suspicious receiver and the legitimate monitor, respectively. First, the monitoring rate achievable by the IRS-aided surveillance system under each deployment strategy is analyzed, which reveals that deploying the IRS near the suspicions transmitter achieves the maximum rate with an asymptotically large number of IRS reflecting elements. Next, efficient algorithms are proposed to maximize the monitoring rate by jointly optimizing the receive and jamming beamforming vectors at the legitimate monitor and the reflection coefficients at the IRS. In particular, a performance upper bound is obtained via properly characterizing the upper and lower bounds of the jamming signal power and using semidefinite relaxation (SDR), while low-complexity algorithms based on the penalty dual decomposition (PDD) framework are also presented to achieve near-optimal performance. Finally, numerical results are presented to validate our analysis as well as the effectiveness of the proposed algorithms, and useful insights are drawn.

System performance and optimization in NOMA mobile edge computing surveillance network using GA and PSO

Recent years have witnessed the computing process gradually move to the edge network, close to the physical data source, to serve applications that require large computations with very little latency. However, the terminal wireless devices’ limited computing and energy resources pose obstacles to the practical implementation of these applications. Mobile Edge Computing (MEC) based non-orthogonal multiple access (NOMA) technology is solving this problem well thanks to its ability to serve many users with high data rates and spectrum utilization efficiency. This study investigates the performance and optimization of MEC surveillance systems using NOMA. Specifically, two camera units (CUs) perform the monitoring task to be accomplished by the MEC access point (AP) through Rayleigh fading wireless links. We then proposed the four-phase protocol for this system. Accordingly, we derive the closed-form exact expressions of the successful computation probability (SCP) and study the impact of the network parameters on the system performance. Furthermore, we propose and compare three meta-heuristic-based algorithms, namely MSCP-GA, MSCP-PSO, and MSCP-HGAPSO, to find the optimal parameters set to help the proposed system achieve the maximum SCP. The results show that the proposed algorithms can significantly improve the system’s performance by 40% higher than when the optimal algorithm is not used. Insights into the pros and cons of different algorithms are also discussed in this study. Finally, we use the Monte-Carlo simulation to verify the correctness of this study.

Fabrication and Underwater Testing of a Vector Hydrophone Comprising a Triaxial Piezoelectric Accelerometer and Spherical Hydrophone.

A vector hydrophone is an underwater acoustic sensor that can detect the direction of a sound source. Wide-band characteristics and high sensitivity enhance the performance of underwater surveillance systems in complex environments. A vector hydrophone comprising a triaxial piezoelectric accelerometer and spherical hydrophone was fabricated and tested in the air and underwater. The vector hydrophone was designed to exceed the quantitative figures of merit (i.e., receiving voltage sensitivity and bandwidth) of commercially available hydrophones. Accelerometer performance was enhanced by placing a pair of piezoelectric single crystals on each axis and modifying the seismic mass material. The receiving voltage sensitivity of the omnidirectional hydrophone was approximately -160 dB relative to 1 V/μPa with the amplifier in water; the sensitivity of the accelerometer exceeded 300 mV/g in air and -215 dB relative to 1 V/μPa underwater over the frequency range of interest. The receiving directivity of the vector hydrophone was validated underwater, which confirmed that it could detect the direction of a sound source.

Trends of key surveillance performance indicators of acute flaccid paralysis: a descriptive analysis, Uganda, 2015–2020

BackgroundPolio is disease caused by poliovirus which can in turn cause irreversible paralytic disease, presenting as Acute Flaccid Paralysis (AFP). A sensitive AFP surveillance system, in which all reported AFP cases are evaluated, first to determine if they are true AFP cases or not, is key for tracking polio eradication. True AFP cases are then later categorized as polio AFP or non-polio AFP (NPAFP) cases. Sensitivity is defined by meeting an annual NPAFP rate/100,000 population < 15 years of ≥ 4/100,000, and an annual stool adequacy (SA) rate of ≥ 80%. We describe Uganda’s AFP surveillance performance between 2015–2020, based on the WHO-recommended indicators, including; NPAFP and stool adequacy rate.MethodsWe performed a descriptive analysis of national AFP surveillance data, 2015–2020 obtained from ministry of health. We evaluated proportion of reported AFP cases that were true AFP, and changes in NPAFP and stool adequacy (SA) rate over the study period. We evaluated the trends in achieving the targeted NPAFP and SA rates from 2015–2020. We used QGIS to illustrate patterns in NPAFP and SA rates across districts and subregions.ResultsAmong 3,605 AFP cases reported and investigated countrywide from 2015–2020, 3,475 (96%) were true AFP cases. All the true AFP cases were non-polio related. District reporting was near-complete (97–100% each year). Overall, the mean NPAFP rate declined from 3.1/100,000 in 2015 to 2.1/100,000 in 2020. Less than 40% of districts met the NPAFP target rate in all years. The proportion of districts achieving the NPAFP target rate of ≥ 4/100,000 significantly declined from 35% in 2015 to 20% in 2020. The mean annual SA rate nationally was 88% from 2015–2020. Only 66% of districts achieved the SA target rate of ≥ 80% in the study period. The proportion of districts with SA rate ≥ 80% significantly increased from 68 to 80% between 2015 and 2020.ConclusionMost districts reported AFP cases. However, there was a decline in the NPAFP rate from 2015–2020 and few districts achieved the target rate. The suboptimal AFP surveillance system performance leaves the country at risk of missing ongoing poliovirus transmission. We recommend health worker training on active AFP searches, intensified supportive supervision, increase the number of environmental surveillance sentinel sites to boost AFP surveillance in the country, and periodic review meetings with districts to assess AFP surveillance performance.

Monocular Visual Traffic Surveillance: A Review

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined.

A Unified Airspace Risk Management Framework for UAS Operations

Collision risk modelling has a long history in the aviation industry, with mature models currently utilised for the strategic planning of airspace sectors and air routes. However, the progressive introduction of Unmanned Aircraft Systems (UAS) and other forms of air mobility poses new challenges, compounded by a growing need to address both offline and online operational requirements. To address the associated gaps in the existing airspace risk assessment models, this article proposes a comprehensive risk management framework, which relies on a novel methodology to model UAS collision risk in all classes of airspace. This methodology inherently accounts for the performance of Communication, Navigation and Surveillance (CNS) systems, and, as such, it can be applied to both strategic and tactical operational timeframes. Additionally, the proposed approach can be applied inversely to determine CNS performance requirements given a target value of collision probability. This new risk assessment methodology is based on a rigorous analysis of the CNS error characteristics and transformation of the associated models into the spatial domain to generate a protection volume around each predicted air traffic conflict. Additionally, a methodology to quickly and conservatively evaluate the multi-integral formulation of collision probability is introduced. The validity of the proposed framework is tested using representative CNS performance parameters in two simulation case studies targeting, respectively, a terminal manoeuvring area and an enroute scenario.