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Abstract Background Healthcare-associated bloodstream infections (BSI) threaten patient safety and are the third most common healthcare-associated infection (HAI) in low- and middle-income countries. An intensive-care-unit (ICU) based HAI surveillance network recording BSIs was started in India in 2017. We evaluated this surveillance network’s ability to detect BSI to identify best practices, challenges, and opportunities in its implementation. Methods We conducted a mixed-methods descriptive study from January to May 2022 using the CDC guidelines for evaluation. We focused on hospitals reporting BSI surveillance data to the HAI network from May 2017 to December 2021, and collected data through interviews, surveys, record reviews, and site visits. We integrated quantitative and qualitative results and present mixed methods interpretation. Results The HAI surveillance network included 39 hospitals across 22 states of India. We conducted 13 interviews, four site visits, and one focus-group discussion and collected 50 survey responses. Respondents included network coordinators, surveillance staff, data entry operators, and ICU physicians. Among surveyed staff, 83% rated the case definitions simple to use. Case definitions were correctly applied in 280/284 (98%) case reports. Among 21 site records reviewed, 24% reported using paper-based forms for laboratory reporting. Interviewees reported challenges, including funding, limited human resources, lack of digitalization, variable blood culture practices, and inconsistent information sharing. Conclusion Implementing a standardized HAI surveillance network reporting BSIs in India has been successful, and the case definitions developed were simple. Allocating personnel, digitalizing medical records, improving culturing practices, establishing feedback mechanisms, and funding commitment are crucial for its sustainability.

Abstract Background The COVID-19 pandemic highlights the importance of strong surveillance systems in detecting and responding to public health threats. We sought to evaluate attributes of Keur Massar district's existing COVID-19 surveillance system. Method A descriptive, cross-sectional study was conducted in June 2022; desk review covered data collected from March 03, 2020 to May 31, 2022 in 18 health posts. Data were collected using a standardized questionnaire completed during a face-to-face interview and a desk review of surveillance data gathered from different notification platforms (Excel, ODK, DHIS2 aggregated, and tracker). Study was conducted in Keur Massar department, in the Dakar region. We conducted face-to-face interviews with 18 nurses in June 2022. We utilized a standardized, semi-structured questionnaire adapted from CDC guidelines for surveillance evaluation. Results All 18 head nurses targeted, responded to the questionnaire, with an average age of 41.5 years and 63% aged between 30 and 44. The sex ratio (M/F) was 0.6, and respondents had an average of 15.1 years of experience. All nurses were involved in COVID-19 surveillance and had notified at least one suspected case. While 39% conducted COVID-19 data analysis, 55.6% received feedback from the national level. The usefulness score for the surveillance system was 77.7, with the lowest score (72.9) related to describing the pandemic’s magnitude. Simplicity scored 63.3, with low scores for the availability of guidelines (0) but high scores for training and equipment (94.4). Acceptability scored 76.6, with strong support for COVID-19 surveillance but weak community involvement (48.6). While no cases were reported through the DHIS2 aggregated platform, 1327 PCR-positive SARS-CoV-2 cases were reported through the national Excel sheet and 278 PCR-positive cases were reported through the COVID-19 DHIS2 tracker during the same period. Timeliness varied, averaging 3 days using ODK and 7 days with the national Excel sheet, with a combined average of 5 days across both systems. Conclusion The study highlights challenges in COVID-19 surveillance due to limited human resources, multiple data systems, and delays in notification. While most nurses were trained and equipped, gaps in data quality, timeliness, and community support emphasize the need for streamlined processes and increased workforce capacity.

Background: Brucellosis, as a zoonotic disease, is widespread among humans and animal andcontinues to be a major public health problem. The present study aimed to analyze the trends and epidemiological characteristics of human brucellosis in Ain Témouchent (Western Algeria).Methods: In this registry-based longitudinal study, data on the reported new cases of brucellosis for five years (2014 to 2019) were obtained from the provincial notifiable diseases surveillance system of Ain Temouchent. Using official population data, we calculated the incidence rate per 100,000 inhabitants.Results: From 2014 to 2019, 599 cases of brucellosis cases were notified in Ain Temouchent province (mean annual incidence: 26.37 cases/100,000 inhabitants). The yearly notification peaked in 2014 (n=170 cases). The highest incidence was reported for males (56%) compared to females (44%) with a sex ratio of 1.29. Regarding age, the highest incidence of notified cases for brucellosis was in the group aged 25-44 years and over, accounting for 37,73% of all notified cases. The lowest incidence was found in the age group between 0-14 years with 11.52%. The disease peaked in May with 139 cases. The geographical distribution indicated that the highest risk area of human brucellosis was Hammam Bou Hadjar and Ain Larabaa region.Conclusion: Our findings showed a high prevalence of human brucellosis in Ain Témouchent, with gender, age, and seasonal changes as the main risk factors for human brucellosis.

Abstract Background Formal assessment of a surveillance system’s features and its ability to achieve objectives is crucial for disease control and prevention. Since the implementation of the mpox surveillance system in Cameroon, no evaluation has been conducted. Methods In a cross-sectional study, we assessed the performance of the mpox surveillance system in accordance with the Centers for Disease Control and Prevention (CDC) and World Health Organization (WHO) guidelines. We collected mpox surveillance data from 2018 to 2022 and conducted a survey with key stakeholders of the surveillance program. The survey results were summarized. The rates of complete reporting and mpox detection, as well as the time lag between the different stages of surveillance were analyzed using R version 4.1. Results The mpox detection rate was 21.6% (29/134) over the five years under review. Surveillance indicators revealed that a combination of sample types, including vesicles, crust, and blood, was associated with higher case confirmation. Overall, the mpox surveillance system was effective. Weaknesses in terms of simplicity were identified. Most components of the assessed system failed to meet the timeliness and data quality goals, except for the laboratory component, which was commendable. The lack of a computerized shared database and the system’s non-sustainability were a course of concern. Conclusions Despite all identified bottlenecks in the mpox surveillance system in Cameroon, it was found to meet it stipulated goals. Recommendations are made for training on surveillance system features, particularly at the facility/field level. Therefore, there is a crucial need to globally improve the mpox surveillance system in Cameroon for better disease control.

ABSTRACTRubella is a major cause of congenital defects, and the presence of rubella infection in a pregnant woman may lead to fetal death or congenital defects known as congenital rubella syndrome(CRS). Since China has not yet established a national CRS surveillance system, the true incidence cannot be determined. To understand the disease burden and epidemiological characteristics of CRS cases in China, the article reports the first case of CRS in Quzhou, China, and conducts a retrospective analysis of related cases that have been reported in China over the past decade. Because the availability of rubella-containing vaccines (RCV) was not widespread in China before 2008, women of childbearing age born before 2008 are generally unvaccinated against RCV. Due to the lack of routine CRS monitoring and screening, CRS is underreported in China. Vaccination of nonimmune women of childbearing age with RCV and establishing a sensitive and timely case-based CRS surveillance system can accelerate the elimination of rubella and CRS.

Abstract Background EU workers exposed to hand-arm vibration should be offered health surveillance to detect early symptoms, and findings, of Hand-Arm Vibration Syndrome (HAVS). To execute the mandatory vocational rehabilitation, the employer needs to be aware of injuries found in the medical check-up. We aimed to analyse: 1) How physicians graded the neurosensory component of HAVS on the Stockholm Workshop Scale (SWS), compared to semi-objective findings. 2) What vocational rehabilitative decisions (VRD) were taken by physicians after examinations. 3) Whether the VRDs differed in relation to the SWS-grading. Methods Data came from 660 medical records - all examinations performed during twelve consecutive months in one large Swedish occupational healthcare company. 572 individuals had data on the SWS from the physician. For the qualitative analysis, we used the inductive-iterative immersion-crystallization method. Results 60% of the examined workers had symptoms and 32% had semi-objective findings consistent with HAVS. The physicians’ SWS gradings were underestimated in 59% of the cases with semi-objective findings. The VRDs were classified, relative to communication with the employer, as: “Adequate” (57%), when no injury was present, communication had already taken place, was planned, or was no longer needed in the absence of further exposure, “Semi-adequate” (18%), if no plan for communication was yet established or only communicated through a document with a shorter time until next check-up, and “Inadequate”(25%), when patients refused (20%), or physicians failed to communicate with the employer, despite findings (80%). Underestimated SWS-gradings of HAVS were significantly associated with more “Inadequate” VRDs in the group with semi-objective findings. Conclusions Occupational physicians underestimate the number of individuals with SWS 2–3 compared with semi-objective findings and regularly fail to communicate to the employer despite findings of HAVS. The underestimation of SWS-grading, followed by inadequate VRDs, excludes many workers from the employer’s mandatory protective measures which may lead to aggravation of an untreatable injury in the affected individual and development of HAVS in their similarly exposed colleagues.

Abstract Background Uganda has a sentinel surveillance system in seven high-risk sites to monitor yellow fever (YF) patterns and detect outbreaks. We evaluated the performance of this system from 2017 to 2022. Methods We evaluated selected attributes, including timeliness (lags between different critical time points), external completeness (proportion of expected sentinel sites reporting ≥ 1 suspect case in the system annually), and internal completeness (proportion of reports with the minimum required data elements filled), using secondary data in the YF surveillance database from January 2017–July 2022. We conducted key informant interviews with stakeholders at health facility and national level to assess usefulness, flexibility, simplicity, and acceptability of the surveillance system. Results In total, 3,073 suspected and 15 confirmed YF cases were reported. The median time lag from sample collection to laboratory shipment was 37 days (IQR:21–54). External completeness was 76%; internal completeness was 65%. Stakeholders felt that the surveillance system was simple and acceptable, but were uncertain about flexibility. Most (71%) YF cases in previous outbreaks were detected through the sentinel surveillance system; data were used to inform interventions such as intensified YF vaccination. Conclusion The YF sentinel surveillance system was useful in detecting outbreaks and informing public health action. Delays in case confirmation and incomplete data compromised its overall effectiveness and efficiency.

Introduction: In infectious diseases, there are essential indices used to describe the disease state. In this study, we estimated the basic reproduction number, R0, peak level, doubling time, and daily growth rate of COVID-19. Methods: This ecological study was conducted in 5 provinces of Iran. The daily numbers of new COVID-19 cases from January 17 to February 8, 2020 were used to determine the basic reproduction number (R0), peak date, doubling time, and daily growth rates in all five provinces. A sensitivity analysis was conducted to estimate epidemiological parameters. Result: The highest and lowest number of deaths were observed in Hamedan (657 deaths) and Chaharmahal and Bakhtiari (54 deaths) provinces, respectively. The doubling time of confirmed cases in Kermanshah and Hamedan ranged widely from 18.59 days (95% confidence interval (CI): 17.38, 20) to 76.66 days (95% CI: 56.36, 119.78). In addition, the highest daily growth rates of confirmed cases were observed in Kermanshah (0.037, 95% CI: 0.034, 0.039) and Sistan and Baluchestan (0.032, 95% CI: 0.030, 0.034) provinces. Conclusion: In light of our findings, it is imperative to tailor containment strategies to the unique epidemiological profiles of each region in order to effectively mitigate the spread and impact of COVID-19. The wide variation in doubling times underscores the importance of flexibility in public health responses. By adapting measures to local conditions, we can better address the evolving dynamics of the pandemic and safeguard the well-being of communities.

Introduction: The WHO targets Southeast Asia to eliminate measles and rubella by 2023. Measles-Contain Vaccination (MCV) coverage in Indonesia has dropped 6 %, and vaccine-preventable disease (VPD) reporting has fallen 30 % due to the pandemic. Bantul's MR positive rate rose 16 % in 2022. Measles outbreak with 71 cases in March 2023. We evaluated the system to identify program implementation gaps. Methods: Following system surveillance evaluation guidelines 2006, we conducted a descriptive cross-sectional study. We evaluate the structure and attribute components. We chose eighteen public health facilities for sub-districts, with 52 health workers as representatives of all areas in the Bantul district. We interviewed with a semi-structured questionnaire. We calculated the frequencies and proportions of each component, categorized as good, fair, and weak. Results: The average service time was almost ten years. No Public Health Center (PHC) used the system to make decisions. Since the private health facilities and community involvement was minimal, the surveillance system contains structural weaknesses in networks and partnerships. Timeliness completeness coupled with the weak evidence for variables of accessibility and flexibility is also vulnerable. Conclusions: The Bantul measles-rubella disease surveillance system lacked networking, partnerships, and community involvement. The health office is expected to boost private health facility participation, monitoring and assessment, and vaccine coverage by at least 95 %, especially in vulnerable areas.

SEL, a State-based Language for Video Surveillance Modeling, is a formal language designed to represent and identify activities in surveillance systems through scenario semantics and the creation of motion primitives structured in programs. Motion primitives represent the temporal evolution of motion evidence. They are the most basic motion structures detected as motion evidence, including operators such as sequence, parallel, and concurrency, which indicate trajectory evolution, simultaneity, and synchronization. SEL is a very expressive language that characterizes interactions by describing the relationships between motion primitives. These interactions determine the scenario’s activity and meaning. An experimental model is constructed to demonstrate the value of SEL, incorporating challenging activities in surveillance systems. This approach assesses the language’s suitability for describing complicated tasks.

Nowadays, for controlling crime, surveillance cameras are typically installed in all public places to ensure urban safety and security. However, automating Human Activity Recognition (HAR) using computer vision techniques faces several challenges such as lowlighting, complex spatiotemporal features, clutter backgrounds, and inefficient utilization of surveillance system resources. Existing attempts in HAR designed straightforward networks by analyzing either spatial or motion patterns resulting in limited performance while the dual streams methods are entirely based on Convolutional Neural Networks (CNN) that are inadequate to learning the long-range temporal information for HAR. To overcome the above-mentioned challenges, this paper proposes an optimized dual stream framework for HAR which mainly consists of three steps. First, a shots segmentation module is introduced in the proposed framework to efficiently utilize the surveillance system resources by enhancing the lowlight video stream and then it detects salient video frames that consist of human. This module is trained on our own challenging Lowlight Human Surveillance Dataset (LHSD) which consists of both normal and different levels of lowlighting data to recognize humans in complex uncertain environments. Next, to learn HAR from both contextual and motion information, a dual stream approach is used in the feature extraction. In the first stream, it freezes the learned weights of the backbone Vision Transformer (ViT) B-16 model to select the discriminative contextual information. In the second stream, ViT features are then fused with the intermediate encoder layers of FlowNet2 model for optical flow to extract a robust motion feature vector. Finally, a two stream Parallel Bidirectional Long Short-Term Memory (PBiLSTM) is proposed for sequence learning to capture the global semantics of activities, followed by Dual Stream Multi-Head Attention (DSMHA) with a late fusion strategy to optimize the huge features vector for accurate HAR. To assess the strength of the proposed framework, extensive empirical results are conducted on real-world surveillance scenarios and various benchmark HAR datasets that achieve 78.6285%, 96.0151%, and 98.875% accuracies on HMDB51, UCF101, and YouTube Action, respectively. Our results show that the proposed strategy outperforms State-of-the-Art (SOTA) methods. The proposed framework gives superior performance in HAR, providing accurate and reliable recognition of human activities in surveillance systems.

Globally, fire incidents cause significant social, economic, and environmental destruction, making early detection and rapid response essential for minimizing such devastation. While various traditional machine learning and deep learning techniques have been proposed, their detection performances remain poor, particularly due to low-resolution data and ineffective feature selection methods. Therefore, this study develops a novel framework for accurate fire detection, especially in challenging environments, focusing on two distinct phases: preprocessing and model initializing. In the preprocessing phase, super-resolution is applied to input data using LapSRN to effectively enhance the data quality, aiming to achieve optimal performance. In the subsequent phase, the proposed network utilizes an attention-based deep neural network (DNN) named Xception for detailed feature selection while reducing the computational cost, followed by adaptive spatial attention (ASA) to further enhance the model’s focus on a relevant spatial feature in the training data. Additionally, we contribute a medium-scale custom fire dataset, comprising high-resolution, imbalanced, and visually similar fire/non-fire images. Moreover, this study conducts an extensive experiment by exploring various pretrained DNN networks with attention modules and compares the proposed network with several state-of-the-art techniques using both a custom dataset and a standard benchmark. The experimental results demonstrate that our network achieved optimal performance in terms of precision, recall, F1-score, and accuracy among different competitive techniques, proving its suitability for real-time deployment compared to edge devices.

China has been continuously improving its monitoring methods and strategies to address key infectious diseases (KIDs). After the severe acute respiratory syndrome epidemic in 2003, China established a comprehensive reporting system for infectious diseases (IDs) and public health emergencies. The relatively lagging warning thresholds, limited warning information, and outdated warning technology are insufficient to meet the needs of comprehensive monitoring for modern KIDs. Strengthening early monitoring and warning capabilities to enhance the public health system has become a top priority, with increasing demand for early warning thresholds, information, and techniques, thanks to constant innovation and development in molecular biology, bioinformatics, artificial intelligence, and other identification and analysis technologies. A panel of 31 experts has recommended a fourth-generation comprehensive surveillance system targeting KIDs (41 notifiable diseases and emerging IDs). The aim of this surveillance system is to systematically monitor the epidemiology and causal pathogens of KIDs in hosts such as humans, animals, and vectors, along with associated environmental pathogens. By integrating factors influencing epidemic spread and risk assessment, the surveillance system can serve to detect, predict, and provide early warnings for the occurrence, development, variation, and spread of known or novel KIDs. Moreover, we recommend comprehensive ID monitoring based on the fourth-generation surveillance system, along with a data-integrated monitoring and early warning platform and a consortium pathogen detection technology system. This series of considerations is based on systematic and comprehensive monitoring across multiple sectors, dimensions, factors, and pathogens that is supported by data integration and connectivity. This expert consensus will provides an opportunity for collaboration in various fields and relies on interdisciplinary application to enhance comprehensive monitoring, prediction, and early warning capabilities for the next generation of ID surveillance. This expert consensus will serve as a reference for ID prevention and control as well as other related activities.

Abstract Background South Africa set a target to eliminate malaria by 2023, with KwaZulu-Natal (KZN) Province the malaria-endemic province closest to achieving this goal. Objective two of the National Malaria Elimination Strategic Plan (NMESP) focused on strengthening surveillance systems to support the country’s elimination efforts. Regular evaluations of the malaria surveillance systems against the targets of the NMESP objective are crucial in improving their performance and impact. This study aimed to assess whether the malaria surveillance system in KwaZulu-Natal Province meets the NMESP surveillance objective and goals. Methods A mixed-methods cross-sectional study design was used to evaluate the malaria surveillance system, focusing on the District Health Information System 2 (DHIS2). The study assessed the data quality, timeliness, simplicity, and acceptability of the system. Key personnel from KZN’s Provincial malaria control programme were interviewed using self-administered questionnaires to evaluate their perception of the system's simplicity and acceptability. Malaria case data from January 2016 to December 2020 were extracted from the DHIS2 and evaluated for data quality and timeliness. Results The survey respondents generally found the DHIS2-based surveillance system acceptable (79%, 11/14) and easy to use (71%, 10/14), stating that they could readily find, extract, and share data (64%, 9/14). Overall data quality was good (88.9%), although some variables needed for case classification had low completeness and data availability. However, case notifications were not timely, with only 61% (2 622/4 329) of cases notified within 24 h of diagnosis. During the 5-year study period, the DHIS2 captured 4 333 malaria cases. The majority of cases (81%, 3 489/4 330) were categorized as imported, and predominately in males (67%, 2 914/4 333). Conclusion While the malaria surveillance system in KZN Province largely met the NMESP surveillance strategic goals, it failed to achieve the overarching surveillance objective of 100% notification of cases within 24 h of diagnosis. The majority of reported cases in KZN Province were classified as imported, emphasizing the importance of complete data for accurate case classification. Engaging with healthcare professionals responsible for case notification and disseminating aggregated data back to them is needed to encourage and improve notification timeliness.

Abstract Background More than half of the global population lives in areas at risk of dengue (DENV) transmission. Developing an efficient risk prediction system can help curb dengue outbreaks, but multiple variables, including mosquito-based surveillance indicators, still constrain our understanding. Mosquito or oviposition positive index (MOI) has been utilized in field surveillance to monitor the wild population density of Aedes albopictus in Guangzhou since 2005. Methods Based on the mosquito surveillance data using Mosq-ovitrap collection and human landing collection (HLC) launched at 12 sites in Guangzhou from 2015 to 2017, we established a MOI-based model of the basic dengue reproduction number (R 0) using the classical Ross-Macdonald framework combined with a linear mixed-effects model. Results During the survey period, the mean MOI and adult mosquito density index (ADI) using HLC for Ae. albopictus were 12.96 ± 17.78 and 16.79 ± 55.92, respectively. The R 0 estimated from the daily ADI (ADID) showed a significant seasonal variation. A 10-unit increase in MOI was associated with 1.08-fold (95% CI 1.05, 1.11) ADID and an increase of 0.14 (95% CI 0.05, 0.23) in the logarithmic transformation of R 0. MOI-based R 0 of dengue varied by month and average monthly temperature. During the active period of Ae. albopictus from April to November in Guangzhou region, a high risk of dengue outbreak was predicted by the MOI-based R 0 model, especially from August to October, with the predicted R 0 > 1. Meanwhile, from December to March, the estimates of MOI-based R 0 were < 1. Conclusions The present study enriched our knowledge about mosquito-based surveillance indicators and indicated that the MOI of Ae. albopictus could be valuable for application in estimating the R 0 of dengue using a statistical model. The MOI-based R 0 model prediction of the risk of dengue transmission varied by month and temperature in Guangzhou. Our findings lay a foundation for further development of a complex efficient dengue risk prediction system. Graphical Abstract

Background: Blitar district was in the last second position in East Java Province, achieving low Tuberculosis recovery and treatment of the national target by 2022. Therefore, it is necessary to support surveillance systems as a prerequisite for providing information as decision-making material. Purpose: to describe the quality of the tuberculosis surveillance system based on its approach and attributes. Methods: This research was an evaluation study design on the tuberculosis surveillance system in 2022. Respondents totaled 19 people. Data was collected by interview using a questionnaire and document study using a checklist. Data was analyzed by comparing the system approach and surveillance attributes to existing guidelines. The data was presented in tables and narratives. Results: Evaluation of tuberculosis surveillance systems based on input, process, and output was available by surveillance guidelines. Evaluation of the surveillance attributes showed that the flexible system has high data quality, high sensitivity, timeliness, and high stability. However, the system is not simple in operation, has low acceptability of the network, and has low positive predictive value because the suspect detection is too loose and not representative. Conclusion: The implementation of the tuberculosis surveillance system has largely been carried out well, supported by several complete surveillance attributes. It is necessary to provide standard operational procedures for recording and reporting, improve coordination with the network to manually and electronically report suspected tuberculosis, and optimize the Mantoux test in children.

There are few studies utilizing only IR cameras for long-distance gender recognition, and they have shown low recognition performance due to their lack of color and texture information in IR images with a complex background. Therefore, a rough body segmentation-based gender recognition network (RBSG-Net) is proposed, with enhanced gender recognition performance achieved by emphasizing the silhouette of a person through a body segmentation network. Anthropometric loss for the segmentation network and an adaptive body attention module are also proposed, which effectively integrate the segmentation and classification networks. To enhance the analytic capabilities of the proposed framework, fractal dimension estimation was introduced into the system to gain insights into the complexity and irregularity of the body region, thereby predicting the accuracy of body segmentation. For experiments, near-infrared images from the Sun Yat-sen University multiple modality re-identification version 1 (SYSU-MM01) dataset and thermal images from the Dongguk body-based gender version 2 (DBGender-DB2) database were used. The equal error rates of gender recognition by the proposed model were 4.320% and 8.303% for these two databases, respectively, surpassing state-of-the-art methods.

Abstract Background In the modern era of antibiotics, healthcare-associated infections (HAIs) have emerged as a prominent and concerning health threat worldwide. Implementing an electronic surveillance system for healthcare-associated infections offers the potential to not only alleviate the manual workload of clinical physicians in surveillance and reporting but also enhance patient safety and the overall quality of medical care. Despite the widespread adoption of healthcare-associated infections surveillance systems in numerous hospitals across China, several challenges persist. These encompass incomplete coverage of all infection types in the surveillance, lack of clarity in the alerting results provided by the system, and discrepancies in sensitivity and specificity that fall short of practical expectations. Methods We design and develop a knowledge-based healthcare-associated infections surveillance system (KBHAIS) with the primary goal of supporting clinicians in their surveillance of HAIs. The system operates by automatically extracting infection factors from both structured and unstructured electronic health data. Each patient visit is represented as a tuple list, which is then processed by the rule engine within KBHAIS. As a result, the system generates comprehensive warning results, encompassing infection site, infection diagnoses, infection time, and infection probability. These knowledge rules utilized by the rule engine are derived from infection-related clinical guidelines and the collective expertise of domain experts. Results We develop and evaluate our KBHAIS on a dataset of 106,769 samples collected from 84,839 patients at Gansu Provincial Hospital in China. The experimental results reveal that the system achieves a sensitivity rate surpassing 0.83, offering compelling evidence of its effectiveness and reliability. Conclusions Our healthcare-associated infections surveillance system demonstrates its effectiveness in promptly alerting patients to healthcare-associated infections. Consequently, our system holds the potential to considerably diminish the occurrence of delayed and missed reporting of such infections, thereby bolstering patient safety and elevating the overall quality of healthcare delivery.

The UK's work-related diseases and occupational injury surveillance system consists of Reporting of Injuries, Diseases and Dangerous Occurrences Regulations 2013 (RIDDOR), Labour Force Survey (LFS), The Health and Occupation Research network in General Practice (THOR-GP), and Reporting to The Health and Occupation Research network by specialist physicians. This article briefly described the scope, content, and methods of each surveillance programme in the UK work-related diseases and occupational injury surveillance system, and summarized their advantages and disadvantages. Among them, employers are required to report to relevant law enforcement authorities by RIDDOR, data are highly accessible but with a concern of serious underreport, and it is the only data source of fatal occupational injuries; LFS, a representative national household sample survey, covering occupational injuries and work-related diseases, is the primary data source of non-fatal occupational injuries and work-related diseases such as stress, anxiety, and depression, but collects non-clinically proven data based on self-perception; general practitioners report clinically confirmed work-related diseases, which is more scientific in attribution and is a good secondary source of work-related diseases; specialist physicians report clinically confirmed cases of higher severity, which is the primary source of data on conditions such as asthma and dermatitis, but may underestimate morbidity. Each surveillance programme of the system has its own characteristics, intersects, and complements each other, which can provide reference for the construction of occupational injury surveillance system in China.

In order to promote the development of China's occupational injury surveillance system, this paper presented the legal basis, project overview, reporting procedures, definitions and stati statistical scope, data sources and collection standards, statistical data management and analysis points of the European Statistics on Accidents at Work (ESAW), and combined with existing research and related surveillance management system in China, five key points were proposed for constructing China's occupational injury surveillance system: 1) Establish and improve laws and regulations related to occupational injury surveillance; 2) Promote utilization of nation-level data systems; 3) Establish and optimize a sound national occupational injury surveillance system; 4) Provide standardized protocols for data collection and data application of occupational injury statistics; 5) Strengthen supervision and law enforcement targeting industries and enterprises.

Abstract Background and Aims No recovery related surveillance system exists but given the evidence of effectiveness and growing supply, a house‐ and resident‐ level recovery house (RH) surveillance system could be beneficial for data collection on recovery support service (RSS) engagement, and retention; for improved standardization of RH programs and services; and for identification of outcomes associated with long‐term recovery. Methods This study aimed to explore current data collection practices at the resident‐ and house‐ level through qualitative focus interviews of RH representatives. The 13 RH interviews were scheduled with 16 RH representative respondents. Results The most frequently collected resident data was at entry (92%) and departure (85%) and included demographics (n = 5), substance use history (n = 6), treatment and recovery history (n = 5), legal and correctional history (n = 6) and mental health information (n = 7). Recovery support data was collected by 85% of houses. Post‐stay data was only collected by four RHs (31%). Conclusion These results indicate that there is a lack of standardized systematic collection, analysis, and reporting of recovery related data in the RH field. A recovery related surveillance system has the potential to fill this gap and inform and improve standard of resident care to support long‐term recovery from substance use disorder.

Background: It is estimated that in 2025, the cost of diabetes will account for 40% of total health spending. In Iran, 4.5 to 5 million people are afflicted with diabetes, and its prevalence has doubled during the past three decades. The aim of this study was to determine the function and preparedness of diabetes surveillance system in Iran.Methods: In this cross-sectional study, all managers, health policymakers and experts of diabetes(N:125) entered the study and completed a questionnaire on the existence and implementation of the surveillance system domains (from data collection to evaluation) and the relationship between domains of this system and their function. The descriptive statistical method, path analysis, was employed using Amos.Results: A ccording t o m anager’s v iewpoint, t he r ange o f existence and implementation of all domains of surveillance system were 12% to 68.7% in average. They believed that 10-69% of domains of surveillance system were working properly. Most of respondents (59-87%) declared that investing in diabetes at different levels of prevention was less than expected. Satisfaction with both diabetes surveillance system function and diabetic patients was low and they don’t have a good and bright future for diabetes and its various aspects. By modeling analytical structural equations, some components of surveillance system were moderately worked together.Conclusion: we conclude that we must first formulate and prepare a standard surveillance system as well as train the relevant personnel and make clear changes in the existing care system. With the current surveillance system, we have a long way to go to prevent and control diabetes to an acceptable the average level of performance of the care system is lower than expected

This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Objectives: We evaluated the malaria surveillance system in Adaklu District of the Volta Region of Ghana to determine if the system was meeting its objectives and assessed its usefulness and attributes. Study design: Descriptive cross-sectional design was used in evaluating the surveillance system. Methods: We interviewed stakeholders using a semi-structured questionnaire on case detection and reporting. We assessed the system attributes using the Centers for Disease Control and Prevention updated guidelines for evaluating public health surveillance systems. We extracted and reviewed malaria surveillance data from the District Health Management Information System 2. Summary statistics and direct content analysis were performed on quantitative and qualitative data respectively. Results: Of the 80,441 suspected malaria cases recorded in Adaklu District from 2014 to 2018, 47,917 (59.6%) cases were confirmed. The system was meeting its objective of detecting malaria cases and monitoring trends in the population however, the system missed an epidemic in August 2016. Data generated from the surveillance system is used by the NMCP to aid in the distribution of logistics such as LLINs, RDT test kits, and track malaria control progress in the district. Staff at all levels were able to detect, confirm, treat and report malaria. All sub-districts/health facilities reported to the district and reports were all accurate and timely. The predictive value positive was 62.9%. Conclusions: The malaria surveillance system in Adaklu District was useful and meeting its set objective of monitoring trends of malaria in the population. It was simple, flexible, acceptable and representative; however, the system was not detecting epidemics. The District Health Management Team should set alert and epidemic thresholds to help detect promptly epidemics of malaria in the district.

Abstract Background Suicide is currently the second leading cause of death among adolescents ages 10–14, and third leading cause of death among adolescents ages 15–19 in the United States (U.S). Although we have numerous U.S. based surveillance systems and survey data sources, the coverage offered by these data with regard to the complexity of youth suicide had yet to be examined. The recent release of a comprehensive systems map for adolescent suicide provides an opportunity to contrast the content of surveillance systems and surveys with the mechanisms listed in the map. Objective To inform existing data collection efforts and advance future research on the risk and protective factors relevant to adolescent suicide. Methods We examined data from U.S. based surveillance systems and nationally-representative surveys that included (1) observations for an adolescent population and (2) questions or indicators in the data that identified suicidal ideation or suicide attempt. Using thematic analysis, we evaluated the codebooks and data dictionaries for each source to match questions or indicators to suicide-related risk and protective factors identified through a recently published suicide systems map. We used descriptive analysis to summarize where data were available or missing and categorized data gaps by social-ecological level. Results Approximately 1-of-5 of the suicide-related risk and protective factors identified in the systems map had no supporting data, in any of the considered data sources. All sources cover less than half the factors, except the Adolescent Brain Cognitive Development Study (ABCD), which covers nearly 70% of factors. Conclusions Examining gaps in suicide research can help focus future data collection efforts in suicide prevention. Our analysis precisely identified where data is missing and also revealed that missing data affects some aspects of suicide research (e.g., distal factors at the community and societal level) more than others (e.g., proximal factors about individual characteristics). In sum, our analysis highlights limitations in current suicide-related data availability and provides new opportunities to identify and expand current data collection efforts.

Abstract Background Healthcare-associated infections (HAIs) are a threat to patients. Accurate surveillance is required to identify and prevent HAIs. To estimate the incidence rate, report the accuracy and identify the barriers of reporting HAIs using a mixed-method study. Methods In this quantitative study, we externally evaluated the incidence rate and accuracy of the routine surveillance system in one of the main hospitals by an active follow-up of patients from September to December 2021. We used in-depth interviews with 18 experts to identify the barriers of the routine surveillance system. Results Among 404 hospitalized patients, 88 HAIs were detected. The estimated rate of HAIs was 17.1 (95% Confidence Intervals 95: 14.1, 21.1) per 1000 patient-days follow-up. However, in the same period, 116 HAIs were reported by the routine surveillance system, but the agreement between the two approaches was low (sensitivity = 61.4%, specificity = 82.6%, negative predictive value = 89.7%, and positive predictive validity = 46.5%). The minimum and maximum positive predictive values were observed in urinary tract infection (32.3%) and surgical site infection (60.9%). The main barrier of reporting HAIs was lack of cooperation in reporting HAIs by infection control link nurses and laboratory supervisors. Conclusions The discrepancy between the longitudinal study findings and the routine surveillance might be related to the inaccessibility of the surveillance system to clinical information of patients. In this regard, decreasing the barriers, increasing the knowledge of infection control nurses and other nurses, as well as the development of hospital information systems are necessary.

Introduction: In Italy, approximately 80.5% of the population has completed the primary anti-COVID vaccination cycle with approximately 141 million doses administered. With the introduction of new measures to counter the spread of COVID-19, including compulsory vaccination for certain categories of people, the population expressed fears about the safety and adverse effects of SARS-CoV-2 vaccines. Several factors, such as gender and age, could have influenced the outcomes associated with the vaccine. Our single-centre work seeks to provide such evidence with respect to Pfizer/BioNTech’s BNT162b2 (Comirnaty) and AstraZeneca’s AZD1222 (Vaxzevria) vaccines. Materials and Methods: Single-centre descriptive study carried out on a sample of subjects who underwent anti-COVID vaccination at the ‘San Giovanni di Dio e Ruggi d’Aragona’ AOU vaccination centre in Salerno. Patients who reported a suspected adverse reaction after receiving a dose of vaccine were included in the study. The regional vaccine platform SORESA and the VigiFarmaco portal were used to collect the data. Results: During the period covered by the study, 126,928 doses of SARS-CoV-2 vaccine were administered. The Pfizer-BioNTech vaccine group comprised 124,138 administrations. The AstraZeneca vaccine group consisted of 2,790 administrations. 287 post-vaccination adverse reaction reports entered in the National Pharmacovigilance Network were considered. In most of the reactions reported, for both vaccines considered, the symptomatology was attributable to local reactions at the injection site. At the systemic level, however, we noted the prevalence of non-specific events such as fever, headache and diffuse arthromyalgia. Conclusions: Based on our results and comparison with the literature, the data collected on the vaccines considered in the study suggest a favourable safety profile for their large-scale use. The rate of minor adverse events turned out to be low, with similarly reassuring data compared to serious adverse events, such as not to justify hesitation towards vaccination for COVID-19 disease control.

Abstract Background In many countries, including Italy, there are few national data on pre-pregnancy Body Mass Index (BMI) and gestational weight gain (GWG), despite these being important predictors of maternal and neonatal health outcomes. This dearth of information makes it difficult to develop and monitor intervention policies to reduce the burden of disease linked to inadequate BMI status and/or GWG in pregnant women. This study describes the setting up and initial implementation of a regional surveillance system on pre-pregnancy BMI and GWG. Methods Between 1 January 2017 and 31 December 2018, anthropometric data were collected from all pregnant women accessing public health services in the Friuli Venezia Giulia region (Italy) for first ultrasound check (T1) and at delivery (T2). Anthropometric data collected at T1 (self-reported pre-pregnancy weight and measured weight and height) and T2 (measured weight and self-reported pre-pregnancy weight and height) were compared. Results The system was able to reach 43.8% of all the women who gave birth in the region, and provided complete data for 6400 women of the 7188 who accessed the services at T1. At the beginning of pregnancy 447 (7.0%) women were underweight, 4297 (67.1%) had normal weight, 1131 (17.7%) were overweight and 525 (8.2%) had obesity. At delivery, 2306 (36.0%) women were within the appropriate weight gain range, while for 2021 (31.6%) weight gain was insufficient and for 2073 (32.4%) excessive. Only minor differences were observed between measured and self-reported anthropometric data. Conclusions The surveillance system offers an overview of the weight status of women during pregnancy. About 1/3 of women entered pregnancy with unsatisfactory BMI and 2/3 did not achieve the recommended weight gain. This surveillance system can be an effective tool to guide public health interventions.

Abstract Background One of the four main elements of the worldwide polio eradication strategy is acute flaccid paralysis surveillance (AFP). This system is based on (acute flaccid paralysis (AFP) cases reported and tested at World Health Organization (WHO) accredited laboratories. To measure and monitor performance, indicators were created. The current study aims to evaluate the system components, performance, and efficiency in River Nile State, Northern Sudan, and their compliance with World Health Organization (WHO) requirements for it to be adopted as a good system; its results can be used to certify whether a country is polio-free or not. Material and methods A facility-based retrospective descriptive study was conducted in the River Nile State, Northern Sudan, from Jan 2017 to Dec 2020. This study included all reporting sites/units, workers who reported acute flaccid paralysis (AFP) cases, and officers at the locality level. A total of 50 health institutions were visited for surveillance, and interviews with 59 health workers who were part of the AFP surveillance system were undertaken. The data were collected from participants using a pre-tested questionnaire designed and constructed by the World Health Organization (WHO) framework, and the data were analyzed using the SPSS version (22). Results The River Nile State’s AFP surveillance system was of high quality in terms of the infrastructure that had been put in place and the effectiveness of the system’s operations, as evidenced by the following statistics: from 2017 to 2020, the reported non-polio acute flaccid paralysis (AFP) cases were at a mean rate of 4.02 per 100,000 children under the age of 15; the majority of AFP reported cases were under 10 years; and males made up 73.3% of reported cases; The completeness of reports and surveillance documents exceeded 80%, and active surveillance was applied in 80% of reporting sites. Conclusion Despite the fact that the surveillance system is capable of detecting cases, Sudan continues to report cases of imported polio from other countries, highlighting the need to strengthen surveillance systems and eradication efforts in these countries.

Background: Case finding and malaria foci management is significant strategy for the prosperous performance of amalaria elimination program. Therefore, detection, treatment. and follow-up of all cases (symptomatic & Asymptomatic) in malaria elimination program is necessary. This study aimed to find an active cases of malaria in Bashagard (With a previous history of local malaria transmission) with emphasis on the use of molecular methods in order to successfully implement the malaria elimination program.Methods: A cross-sectional study was conducted to examine more precise the positive cases reported and to identify possible cases of additional malaria. In the first stage, all eligible residents of the community were sampled. In the next step, the villagers who had moved to work in the neighboring city were identified and sampled. In this study, 230 people participated to be tested for malaria by microscopic, Rapid Diagnostic Tests and Nested- PCR techniques.Results: In the first phase of the study, three positive cases of Plasmodium vivax were reported using microscopic and RDT methods. In the second step of the survey, an asymptomatic malaria reservoir was identified using molecular technique.Conclusion: The results of this study emphasize the important role of active cases finding using molecular techniques along with routine diagnostic methods in malaria control and elimination programs.

Due to the lack of perfect regulations and standards in civilian drones field, drones sometimes fly illegally. When illegally flying drones invade airports, which causes flights to be diverted and even airports to be paralyzed. In order to protect the flight safety, drone countermeasure equipment is need to use for driving away illegally flying drones. But drone countermeasure equipment is a new active interference source. When it is used at or around airports, which may cause electromagnetic interference to the surveillance system, and it will not work correctly. Therefore, it is necessary to study protective distances of the surveillance system. In this paper, a method for analyzing the electromagnetic interference influence of drone countermeasure equipment on the surveillance system is proposed. Firstly the radiated field strength of drone countermeasure equipment is measured in an anechoic chamber, and then stray radiation field strengths at frequency bands of a secondary surveillance radar (SSR) and an automatic dependent surveillance-broadcast (ADS-B) also are acquired. Meanwhile, test results are compared with protection requirements of electromagnetic environment of SSR and ADS-B. Moreover, electromagnetic interference effects of drone countermeasure equipment on SSR and ADS-B are analyzed according to the electromagnetic compatibility theory and protection ratios of SSR and ADS-B. At last, protective distances of SSR and ADS-B are proposed separately. The results can provide some technical supports for the safe use of drone countermeasure equipment at airports.

IntroductionDog-mediated rabies is enzootic in Vietnam, resulting in at least 70 reported human deaths and 500,000 human rabies exposures annually. In 2016, an integrated bite cases management (IBCM) based surveillance program was developed to improve knowledge of the dog-mediated rabies burden in Phu Tho Province of Vietnam.MethodsThe Vietnam Animal Rabies Surveillance Program (VARSP) was established in four stages: (1) Laboratory development, (2) Training of community One Health workers, (3) Paper-based-reporting (VARSP 1.0), and (4) Electronic case reporting (VARSP 2.0). Investigation and diagnostic data collected from March 2016 to December 2019 were compared with historical records of animal rabies cases dating back to January 2012. A risk analysis was conducted to evaluate the probability of a rabies exposure resulting in death after a dog bite, based on data collected over the course of an IBCM investigation.ResultsPrior to the implementation of VARSP, between 2012 and 2015, there was an average of one rabies investigation per year, resulting in two confirmed and two probable animal rabies cases. During the 46 months that VARSP was operational (2016 – 2019), 1048 animal investigations were conducted, which identified 79 (8%) laboratory-confirmed rabies cases and 233 (22%) clinically-confirmed(probable) cases. VARSP produced a 78-fold increase in annual animal rabies case detection (one cases detected per year pre-VARSP vs 78 cases per year under VARSP). The risk of succumbing to rabies for bite victims of apparently healthy dogs available for home quarantine, was three deaths for every 10,000 untreated exposures.DiscussionA pilot IBCM model used in Phu Tho Province showed promising results for improving rabies surveillance, with a 26-fold increase in annual case detection after implementation of a One Health model. The risk for a person bitten by an apparently healthy dog to develop rabies in the absence of rabies PEP was very low, which supports the WHO recommendations to delay PEP for this category of bite victims, when trained animal assessors are available and routinely communicate with the medical sector. Recent adoption of an electronic IBCM system is likely to expedite adoption of VARSP 2.0 to other Provinces and improve accuracy of field decisions and data collection.

Background: Asymptomatic malaria is a major challenge to be addressed in the implementation of the malaria elimina­tion program. The main goal of the malaria surveillance system in the elimination phase is to identify reliably all the positive cases of malaria reliably (symptomatic and asymptomatic) in the shortest possible time. This study focused on the monitoring of asymptomatic malaria reservoirs in areas where local transmission had been previously established. Methods: It was a case-study approach that was conducted in the Anarestan area. A total of 246 residents and immi­grants living in the area at the age range of 4–60 years old were randomly selected to be tested for malaria by micro­scope, RDT, and nested-PCR techniques. The inclusion criterion for participants to be entered into the study was the absence of specific symptoms of malaria. Moreover, participants who have been taking antimalarials for the last month were excluded from the study. Results: The results indicated no positive cases of asymptomatic malaria among the participants tested by all methods. Conclusion: The results of this study have shown that, without concerns for asymptomatic parasitic patients, a malaria elimination program has been successfully implemented within the studies area. In addition, the findings emphasized the existence of a strong malaria surveillance system in this area.

Abstract Background Since 2009, Morocco has been implementing the Maternal Death Surveillance System (MDSS). The results obtained indicate significant regional variations in terms of implementation stage, completeness of maternal death reporting, and information use for action. The objective of this research is to better understand the contextual factors involved in the implementation process and use of MDSS, with a focus on the facilitators and barriers, as experienced by stakeholders in health regions. Methods Evaluation research was conducted in 2017 based on a descriptive qualitative study using semi-structured in-depth interviews, in four out of the twelve health regions of Morocco. A total of thirty-one in-depth interviews were held with members of regional committees of maternal death reviews (RC-MDR) and other key informant staff. Interviews focused on participants’ views and their experiences with the MDSS since the introduction in 2009. We conducted thematic analysis relied on inductive and deductive approaches. Applying the Consolidated Framework for Implementation Research guided data analysis and reporting findings. Findings Engaging leadership at all health system levels, regular training of district and regional MDSS coordinators and supportive supervision at a national level were the most important MDSS implementation facilitators. Reported barriers were essentially related to the review system: Irregular review meetings, blame culture, high turn-over of RC-MDR members, lack of analytical capacity to inform the review process and formulate recommendations, finally limited accountability for recommendation follow-up. While financial incentives boosted MDSS adoption, they were nonetheless a substantial barrier to its sustainability. Conclusions The MDSS is a complex process that requires taking numerous steps, including the commitment of multiple stakeholders with varying roles as well as information sharing across health system levels. Contextual factors that influence MDSS implementation at the sub-national level are to be considered. Horizontal and vertical communication about MDSS goals and feedback is crucial to strengthen stakeholders’ commitment, hence improving quality and use of MDSS. Furthermore, health regions should place emphasis on making high-quality recommendations in partnerships between the regional management teams, RC-MDR members and external stakeholders.

Abstract Background Malaria is a priority global health disease with high morbidity and mortality especially among children under-five and pregnant women. Malaria elimination requires an effective surveillance system. The malaria surveillance system in Benue State was evaluated to assess its attributes and performance in line with set objectives. Methods The updated United States Centers for Disease Control and Prevention guideline for evaluating surveillance systems was used. The surveillance system’s key attributes was quantitatively and qualitatively assessed. Semi-structured questionnaires were administered to all Local Government Area (LGA) Roll Back Malaria (RBM) focal persons and five key informants were interviewed at the State level. The Benue State District Health Information System-2 (DHIS-2) malaria data and monthly summary forms were reviewed from January 2015 to December 2019. Results A total of 46 RBM focal persons and 5 key-informants participated. About 56.9% were males, the mean-age 43.8 (SD ± 9.3) years and 32 (62.8%) had ≥ 20-year experience on malaria surveillance with mean-year-experience 20.8 (SD ± 7.8) years. All 46 (100%) RBMs understood the case definition; 43 (93.5%) found it easy-to-fill the standardized data tools and understood the data flow channels. The malaria surveillance system in Benue is simple, acceptable and useful to all stakeholders, 36 (70.6%) found switching from the paper-based to the electronic-data tools with ease and 45 (88.2%) stated that analysed data were used for decision-making. Data flow from LGA to State is clearly defined, however majority of the data is collected from public health facilities through the DHIS-2 Platform. The overall timeliness and completeness of reporting was 76.5% and 95.7%, respectively, which were below the ≥ 80% and 100% targets, respectively. Conclusions The malaria surveillance system in Benue State is simple, useful, acceptable, and flexible, but it is not representative and timely. Public–private and public-public-partnerships should be strengthened to encourage reporting from both private and tertiary health facilities and improve representativeness, and frequent feedback to improve reporting timeliness.

IntroductionAntimicrobial resistance (AMR) is a One Health (OH) challenge. To achieve or maintain an effective and efficient AMR surveillance system, it is crucial to evaluate its performance in meeting the proposed objectives, while complying with resource restrictions. The OH-EpiCap tool was created to evaluate the degree of compliance of hazard surveillance activities with essential OH concepts across the following dimensions: organization, operational activities, and impact of the surveillance system. We present feedback on the application of the OH-EpiCap tool from a user’s perspective, based on the use of the tool to evaluate nine national AMR surveillance systems, each with different contexts and objectives.MethodsThe OH-EpiCap was assessed using the updated CoEvalAMR methodology. This methodology allows the evaluation of the content themes and functional aspects of the tool and captures the user’s subjective experiences via a strengths, weaknesses, opportunities, and threats (SWOT) approach.Results and DiscussionThe results of the evaluation of the OH-EpiCap are presented and discussed. The OH-EpiCap is an easy-to-use tool, which can facilitate a fast macro-overview of the application of the OH concept to AMR surveillance. When used by specialists in the matter, an evaluation using OH-EpiCap can serve as a basis for the discussion of possible adaptations of AMR surveillance activities or targeting areas that may be further investigated using other evaluation tools.

Abstract Background Enterovirus A71 (EV A71) is one of the most important enteroviruses related to morbidity and mortality in children worldwide. This study aimed to analyse the secular trend of EV A71 in Taiwan from 1998 to 2020 and to evaluate the effectiveness of infection control measures. Methods We collected the epidemiological data of EV A71 from disease surveillance systems in Taiwan. We analysed the association between the secular trend of EV A71 and preventive measures such as hand washing, case isolation, and suspension of classes. Results The incidence of enterovirus infections with severe complications (EVSC) decreased from 16.25 per 100,000 children under six in 1998 to less than 9.73 per 100,000 children under six after 2012 (P = 0.0022). The mortality rate also decreased significantly, from 3.52 per 100,000 children under six in 1998 to 0 per 100,000 children under six in 2020 (P

Abstract Background Suicide is a serious cause of morbidity and mortality in Iran and worldwide. Although several organizations gather information on suicide and suicide attempts, there is substantial misperception regarding the description of the phenomenon. This study proposes the minimum data set (MDS) for suicidal behaviors surveillance. Methods A literature review was first conducted to achieve a thorough overview of suicide-related items and map the existing evidence supporting the development of the MDS. The data items included in the literature review were then analyzed using a two-round Delphi technique with content validation by an expert panel. The suicidal behaviors surveillance system was then established based on the confirmed MDS, and ultimately, its performance was assessed by involving the end-users. Results The panel of experts consisted of 50 experts who participated in the Delphi phase and validity content review. Of these, 46% were men, and their mean age and average work experience were (36.4, SD ± 6.4) and (12.32, SD ± 5.2) years, respectively. The final MDS platform of our study contained 108 items classified into eight main categories. A web-based system with a modular and layered architecture was developed based on the derived MDS. Conclusion The developed system provides a framework for recording suicidal behaviors' data. The integration of multiple suicide-related information systems at the regional and national levels makes it possible to assess the long-term outcomes and evolutions of suicide prevention interventions.

This article presents the design and validation of a novel visual servoing scheme for a surveillance system. In this system, a two-axis gimbal mechanism operates the rotation of a camera which is able to provide visual information on the tracked target for the control system. The control objective is to bring the target’s projection to the center of the image plane with the smallest steady-state error and a smooth transient response, even with the unpredictable motion of the target and the influence of external disturbances. To fulfill these tasks, the proposed control scheme is designed consisting of two parts: (1) an observer estimates simultaneously the matched and unmatched disturbances; and (2) a motion control law guarantees the finite-time stability and visual servoing performance. Finally, experiments are conducted for validation and evaluation. The proposed control system shows its consistency and ought to perform better than previous approaches.

This paper presents a novel approach to risk assessment by incorporating image captioning as a fundamental component to enhance the effectiveness of surveillance systems. The proposed surveillance system utilizes image captioning to generate descriptive captions that portray the relationship between objects, actions, and space elements within the observed scene. Subsequently, it evaluates the risk level based on the content of these captions. After defining the risk levels to be detected in the surveillance system, we constructed a dataset consisting of [Image-Caption-Danger Score]. Our dataset offers caption data presented in a unique sentence format, departing from conventional caption styles. This unique format enables a comprehensive interpretation of surveillance scenes by considering various elements, such as objects, actions, and spatial context. We fine-tuned the BLIP-2 model using our dataset to generate captions, and captions were then interpreted with BERT to evaluate the risk level of each scene, categorizing them into stages ranging from 1 to 7. Multiple experiments provided empirical support for the effectiveness of the proposed system, demonstrating significant accuracy rates of 92.3%, 89.8%, and 94.3% for three distinct risk levels: safety, hazard, and danger, respectively.

Background and Aim: Rabies remains a public health concern in Indonesia, and the coronavirus disease (COVID-19) pandemic has stymied rabies prevention and control efforts. There is a need to transform the rabies program to be adaptable to pandemic situations to improve program coverage on dog vaccination and rabies surveillance. This study aimed to create a rabies control (RaCon) mobile application for a community-based rabies surveillance system during COVID-19 in Bali, Indonesia. Materials and Methods: We employ the Design Science Research methodology. Surveillance officers, veterinarians, community leaders, outreach workers, and dog owners participated in a series of offline in-depth interviews and focus group discussions. The RaCon prototype was evaluated using the Post-Study System Usability Questionnaire (PSSUQ) framework, which included the system's usefulness, information quality, and interface quality. In this study, we used both a qualitative (n=50) and quantitative (n=342) approach. Results: According to the findings of this study, integrating public health and animal health into the rabies surveillance system are critical to supporting the One Health approach and encouraging community engagement in rabies programs. The RaCon prototype is expected to include features such as pet ownership, case report, news and announcements, nearest vet, health information, outbreak radar, emergency call, and app feedback. The RaCon prototype passed both qualitative and quantitative evaluations, indicating that it could be used to support the rabies surveillance system, particularly in the COVID-19 situation. Conclusion: The RaCon prototype was accepted by the users and got positive feedback in terms of the system's usefulness, information quality, and interface quality dimension. As a result, this prototype has the potential to be integrated into the rabies surveillance system in Bali, particularly to strengthen the community-based rabies surveillance system. Even though this prototype received positive feedback, this study focuses solely on the design development and evaluation of its user interface. As a result, further development is required before incorporating RaCon into the rabies prevention and control program.

Smart cities are expected to provide residents with convenience via various agents such as CCTV, delivery robots, security robots, and unmanned shuttles. Environmental data collected by various agents can be used for various purposes, including advertising and security monitoring. This study suggests a surveillance map data framework for efficient and integrated multimodal data representation from multi-agents. The suggested surveillance map is a multilayered global information grid, which is integrated from the multimodal data of each agent. To confirm this, we collected surveillance map data for 4 months, and the behavior patterns of humans and vehicles, distribution changes of elevation, and temperature were analyzed. Moreover, we represent an anomaly detection algorithm based on a surveillance map for security service. A two-stage anomaly detection algorithm for unusual situations was developed. With this, abnormal situations such as unusual crowds and pedestrians, vehicle movement, unusual objects, and temperature change were detected. Because the surveillance map enables efficient and integrated processing of large multimodal data from a multi-agent, the suggested data framework can be used for various applications in the smart city.

BackgroundCOVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation.ObjectivesThis study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh.MethodsThe system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes.ResultsThis study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform.ConclusionThis screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.

Background: Pneumonia is one of the leading causes of death in children under 5 years of age. Although overall deaths from pneumonia have decreased substantially by 56%, since 1990 pneumonia contributes to about 740,000 deaths, per year. In Indonesia, in 2021, 444 children under five (CFR 0.16%) died due to pneumonia, in 2020, 498 children under five (CFR 0.16%), and in 2019, 551 children under five (CFR 0.12%). Increasing the scope of finding pneumonia in children under five is one of the efforts done in Indonesia to control pneumonia. The Influenza Like Illness and Severity Acute Respiratory System (ILI-SARI) surveillance is sentinel surveillance that is used to catch cases of pneumonia under five in outpatient and inpatient health facilities. Objective: This literature review aims to describe the implementation of ILI-SARI surveillance increasing the detection of pneumonia in children under five. Methods: The method used is to search the database through Google Scholar, Pubmed, and Research Gate. The key words used in this database search were ILI-SARI surveillance, Pneumonia under five. Results: There are 8 articles obtained and 5 articles analyzed through the suitability of the topic, objectives, methods used, sample size, and results from each article. Conclusion: Care-seeking activities are suggested to be an integral part of this method of preventing and controlling under-five pneumonia.

Abstract Background The collaborative integrated surveillance system known as Vigilancia Integrada Comunitaria (ViCo) was implemented in 2007 to better understand and characterize the burden of diarrheal, respiratory and febrile illnesses in Guatemala. Methods To evaluate the usefulness of ViCo and inform a redesign of the system and new surveillance activities in the Central American region, personnel from the United States Centers for Disease Control and Prevention (CDC) conducted thirty-nine in-depth interviews from June—December 2018 with key stakeholders responsible for the design and implementation of ViCo in Guatemala. A semi-structured questionnaire adapted from the Updated CDC Guidelines for Evaluating Public Health Surveillance Systems was used for data collection. We used a grounded theory approach to explore stakeholder perceptions of ViCo and generate recommendations for improvement. Primary qualitative findings were organized based on thematic areas using ATLAS.ti version 8 software. Results Emergent themes relevant to the usefulness of ViCo were organized across strengths, weaknesses, and recommendations pertaining to the: (1) Size and Complexity of ViCo, (2) Stakeholder Expectations About the Objectives of ViCo, (3) Data Management and Structure of the Information System, (4) Local Control of Data, (5) Integration of ViCo within the Ministry of Health, and, (6) Improvement of the Operational and Design Aspects of ViCo across System, Process, and Output levels. Conclusions Stakeholders perceived ViCo to be useful. They recommended measures to improve system performance and quality, including simplifying the surveillance system, routine data analysis and feedback, and channeling efforts towards integrating surveillance data into the national health information system. To create a well-performing surveillance system and achieve the intended objective of surveillance for public health action, ongoing evaluation and assessment of surveillance activities are necessary.

Abstract Background Limited data from low- and middle-income countries (LMICs) on the severity of road traffic injuries (RTIs) and their relation to different variables of interest are routinely obtained. Knowledge on this subject relies on evidence from high-income countries, which might not be the same as in LMICs. This information is greatly needed to advance and inform local and regional efforts towards the United Nations’ Decade of Action and the Sustainable Development Goals. Methods From May 2012 to November 2014, a RTI surveillance system was implemented in two referral hospitals in two Mexican cities, León and Guadalajara, with the objective of exploring the relationship between Injury Severity Score (ISS) and different sociodemographic characteristics of the injured as well as different variables related to the event and the environment. All individuals suffering RTIs who visited the Emergency Rooms (ER) were included after granting informed consent. A Zero-Truncated Negative Binomial Model was employed to explore the statistical association between ISS and variables of interest. Results 3024 individuals participated in the study: 2185 (72.3%) patients from León and 839 patients (27.7%) from Guadalajara. Being male, in the 20–59 age-group, having less schooling, events occurring in Guadalajara, on Sundays, at night, and arriving at ER via public/private ambulance were all associated with an increased log count of ISS. Found a significant interaction effect (p-value

ObjectiveSalmonella infection is a common cause of bacterial foodborne diseases (FBDs) globally. In this study, we aimed to explore the epidemiological and etiological characteristics of Salmonella infection from 2012–2021 in Zhejiang Province, China.MethodsDescriptive statistical methods were used to analyze the data reported by the Centers for Disease Control and Prevention at all levels in Zhejiang Province through the China National Foodborne Diseases Surveillance Network from 2012–2021.ResultsA total of 11,269 Salmonella cases were reported, with an average positive rate of 3.65%, including 1,614 hospitalizations. A significant seasonal trend was observed for Salmonella cases, with the highest rate over the summer period, peaking from May to October, accounting for 77.96%. The results indicated a higher positive rate among respondents aged 0–4 years, especially for the scattered children (P < 0.05). The highest number of Salmonella infections were caused due to contaminated fruit and fruit products. Households (54.69%) had the most common exposure settings. Serotypes analysis revealed that Salmonella typhimurium (36.07%), Salmonella enteritidis (15.17%), and Salmonella london (6.05%) were the dominant strains among the 173 serotypes. Diarrhea, abdominal pain, fever, nausea, and vomiting were the main symptoms of these serotypes.ConclusionsFBDs caused by Salmonella are important issues for public health in Zhejiang Province, and there is a need to focus on the epidemiological and etiological characteristics to control Salmonella infections.

IntroductionThe knowledge of animal movements is key to formulating strategic animal disease control policies and carrying out targeted surveillance. This study describes the characteristics of district-level cattle, small ruminant, and pig trade networks in the Cattle Corridor of Uganda between 2019 and 2021.MethodologyThe data for the study was extracted from 7,043 animal movement permits (AMPs) obtained from the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF) of Uganda. Most of the data was on cattle (87.2%), followed by small ruminants (11.2%) and pigs (1.6%). Two types of networks representing animal shipments between districts were created for each species based on monthly (n = 30) and seasonal (n = 10) temporal windows. Measures of centrality and cohesiveness were computed for all the temporal windows and our analysis identified the most central districts in the networks.ResultsThe median in-degree for monthly networks ranged from 0–3 for cattle, 0–1 for small ruminants and 0–1 for pigs. The highest median out-degrees for cattle, small ruminant and pig monthly networks were observed in Lira, Oyam and Butambala districts, respectively. Unlike the pig networks, the cattle and small ruminant networks were found to be of small-world and free-scale topologies.DiscussionThe cattle and small ruminant trade movement networks were also found to be highly connected, which could facilitate quick spread of infectious animal diseases across these networks. The findings from this study highlighted the significance of characterizing animal movement networks to inform surveillance, early detection, and subsequent control of infectious animal disease outbreaks.

Background: Antimicrobial resistance (AMR) is a topic of concern, especially in high-level care departments like neonatal intensive care units (NICUs). The systematic use of an “active” epidemiological surveillance system allows us to observe and analyze any changes in microbial distribution, limiting the risk of healthcare-associated infection (HAI) development. Methods: We have conducted a longitudinal observational study in the five NICUs of Palermo, comparing the “pre-pandemic period” (March 2014–February 2020) with the “pandemic” one (March 2020–February 2022). The primary aim of the study was to evaluate the cumulative prevalence of carriage from multi-drug resistant (MDR) bacteria in the cumulative NICUs (NICU C). Results: During the “pre-pandemic period”, 9407 swabs were collected (4707 rectal, 4700 nasal); on the contrary, during the “pandemic period”, a total of 2687 swabs were collected (1345 rectal, 1342 nasal). A statistically significant decrease in MDR-Gram-negative bacteria (GNB) carriage prevalence was detected during the pandemic. At the same time, there was a general worsening of the carriage of carbapenemase-forming MDR-GNB (CARBA-R+) and methicillin-resistant Staphylococcus aureus (MRSA) during the pandemic period. A significant reduction in methicillin-susceptible Staphylococcus aureus (MSSA) carriage was detected too. Conclusions: The surveillance of MDRO carriage in NICUs is fundamental for limiting the social and economic burden of HAIs.

A surveillance camera is the typical device that makes up a surveillance camera system in a modern city. It will still be a representative surveillance unit in future scenarios such as in smart cities. Furthermore, as the demand for public safety increases, a massive number of surveillance cameras will be in use in the future, and an automated system that controls surveillance cameras intelligently will also be required. Meanwhile, installing a surveillance system without any verification system might not be cost-effective, so a simulation that evaluates the system’s performance is required in advance. For this reason, we introduce how to simulate a surveillance area and evaluate surveillance performance in this paper to assess a surveillance system consisting of large amounts of surveillance cameras. Our simulator defined the surveillance area as a pair of two-dimensional planes, which depend on various camera-related configurations. Both surveillance areas are used to determine if the moving object belongs to the coverage of a surveillance camera. In addition, our simulator adopts several performance indices to evaluate a surveillance camera system in terms of target detection and quality. The simulation study provides comprehensive results on how various components of the surveillance system affect the performance of the surveillance system, leading to the conclusion that building a sophisticated scheme to control a large number of surveillance cameras can provide a cost-effective and reliable surveillance system for smart cities.