Surveillance Methods Research Articles

Hospital-acquired infections surveillance: The machine-learning algorithm mirrors National Healthcare Safety Network definitions.

Surveillance of hospital-acquired infections (HAIs) is the foundation of infection control. Machine learning (ML) has been demonstrated to be a valuable tool for HAI surveillance. We compared manual surveillance with a supervised, semiautomated, ML method, and we explored the types of infection and features of importance depicted by the model. From July 2021 to December 2021, a semiautomated surveillance method based on the ML random forest algorithm, was implemented in a Brazilian hospital. Inpatient records were independently manually searched by the local team, and a panel of independent experts reviewed the ML semiautomated results for confirmation of HAI. Among 6,296 patients, manual surveillance classified 183 HAI cases (2.9%), and a semiautomated method found 299 HAI cases (4.7%). The semiautomated method added 77 respiratory infections, which comprised 93.9% of the additional HAIs. The ML model considered 447 features for HAI classification. Among them, 148 features (33.1%) were related to infection signs and symptoms; 101 (22.6%) were related to patient severity status, 51 features (11.4%) were related to bacterial laboratory results; 40 features (8.9%) were related to invasive procedures; 34 (7.6%) were related to antibiotic use; and 31 features (6.9%) were related to patient comorbidities. Among these 447 features, 229 (51.2%) were similar to those proposed by NHSN as criteria for HAI classification. The ML algorithm, which included most NHSN criteria and >200 features, augmented the human capacity for HAI classification. Well-documented algorithm performances may facilitate the incorporation of AI tools in clinical or epidemiological practice and overcome the drawbacks of traditional HAI surveillance.

Estimating landscape spread of a low‐prevalence disease using multiple surveillance methods

Abstract Diseases occurring at low prevalence in free‐ranging wildlife can be difficult to study and predict at a landscape scale. This problem is compounded by imperfect pathogen detection. We used data from multiple surveillance methods to determine factors influencing spread of a low‐prevalence disease, bovine tuberculosis (bTB), among white‐tailed deer (Odocoileus virginianus) in a bTB endemic area over a 10‐year period. We fit dynamic occupancy models to predict spatial spread of bTB through a county in Michigan, USA, within the bTB endemic area, while accounting for imperfect detection and a suite of ecological processes potentially driving bTB spread. Detection by all surveillance methods was low overall (posterior mean detection probabilities 0.01–0.03). Probability of bTB occurrence was 1.46 times as likely in the historical centre of the outbreak compared to outside. Deer density had a weakly positive effect on bTB spread. The probability of a site being newly infected with bTB annually was low (posterior mean colonization probabilities 0.08–0.27), while the probability of persistence after initial infection was high (posterior mean persistence probability 0.67). Synthesis and applications: We used three surveillance methods to estimate probability of presence and spread of a low‐prevalence pathogen at a landscape scale under incomplete sampling coverage. Our methods are relevant for determining spatial risk of any wildlife disease that is monitored using multiple methods of surveillance, which is important for prioritization of management actions.

The safety and effectiveness of remdesivir in a postmarketing surveillance study in Japan

BackgroundTo evaluate the occurrence of adverse drug reactions (ADRs) and to assess mortality and health status in participants receiving remdesivir in real-world settings in Japan. MethodsThis postmarketing surveillance study used an all-case surveillance method for enrollment. Participants with SARS-CoV-2 infection administered remdesivir from July 2020 to November 2021 in Japan were eligible for inclusion. The observation period was from remdesivir treatment initiation to 4 weeks after the end of treatment or treatment discontinuation. Clinical status and outcomes were analyzed by Kaplan-Meier plots and compared across subgroups at baseline, Day 14, Day 28, and the final observation point. ResultsThe analysis included 2128 participants (mean age, 67 years; 71.4 % male; 84.1 % with current comorbidities). ADRs and serious adverse drug reactions (SADRs) were reported among 10.4 % and 1.2 % participants, respectively. Overall, 191/2127 participants died (mortality rate [95 % confidence interval], 11.10 [9.66–12.75] per 100 person-months), 1511/2127 showed clinical improvement (117.8 [112.0–123.9] per 100 person-months), 1392/2127 recovered (103.9 [98.6–110.0] per 100 person-months), and 216/324 were extubated (107.0 [93.6–122.3] per 100 person-months). ConclusionsThe incidence of ADRs and SADRs was low, and no new safety concerns were identified. Observed mortality and clinical improvement results were consistent with prior studies, confirming remdesivir's benefits in real-world settings in Japan.

Cell-free DNA testing for early hepatocellular carcinoma surveillance

Liver cirrhosis (LC) is the highest risk factor for hepatocellular carcinoma (HCC) development worldwide. The efficacy of the guideline-recommended surveillance methods for patients with LC remains unpromising. A total of 4367LCs not previously known to have HCC and 510 HCCs from 16 hospitals across 11 provinces of China were recruited in this multi-center, large-scale, cross-sectional study. Participants were divided into Stage Ⅰ cohort (510 HCCs and 2074 LCs) and Stage Ⅱ cohort (2293LCs) according to their enrollment time and underwent Tri-phasic CT/enhanced MRI, US, AFP, and cell-free DNA (cfDNA). A screening model called PreCar Score was established based on five features of cfDNA using Stage Ⅰ cohort. Surveillance performance of PreCar Score alone or in combination with US/AFP was evaluated in Stage Ⅱ cohort. PreCar Score showed a significantly higher sensitivity for the detection of early/very early HCC (Barcelona stage A/0) in contrast to US (sensitivity of 51.32% [95% CI: 39.66%-62.84%] at 95.53% [95% CI: 94.62%-96.38%] specificity for PreCar Score; sensitivity of 23.68% [95% CI: 14.99%-35.07%] at 99.37% [95% CI: 98.91%-99.64%] specificity for US) (P<0.01, Fisher's exact test). PreCar Score plus US further achieved a higher sensitivity of 60.53% at 95.08% specificity for early/very early HCC screening. Our study developed and validated a cfDNA-based screening tool (PreCar Score) for HCC in cohorts at high risk. The combination of PreCar Score and US can serve as a promising and practical strategy for routine HCC care. A full list of funding bodies that contributed to this study can be found in Acknowledgments section.

Secure IoT based smart system for monitoring health care for ambulatory and fetal

&lt;p&gt;The Internet of Things (IoT) along with Artificial Intelligence (AI) has now developed the most prevalent instruments in applications in the health care industry for widespread and intelligent automatic diagnostic systems. The maternal clinical information for ubiquitous and intelligent autonomous diagnostic systems, IoT with AI has now taken the lead as the predominant instrument in the healthcare industry. With respect to the incorporation of IoT sensors along with deep learning techniques, this article suggests the establishment of smart networks to monitor maternal and fetal signs in dangerous pregnancies. IoT sensors are utilized to gather clinical information about the mother, including her temperature, blood pressure levels, and saturation in oxygen level, heart rate, and heartbeat of the unborn child. This information is then stored in the cloud for tracking and forecasting. Additionally, a brand-new optimal Gated Recurrent Unit (GRU) is suggested for improved categorization and a forecast of the several emergencies affecting both pregnant women and unborn children. Additionally, to assist the data’s security and the design of IoT systems is based on a number of sensors that are interfaced with MICOT boards (Node MCU+MCP3008) and a cloud system. For the evaluation, about 500 pieces of data were gathered and utilized. With cloud-centric learning techniques like K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Convolution Neural Networks (CNN), and Extreme Learning Machines (ELM) thorough experimentation is conducted, and various parameters including accuracy, precision, recall, and sensitivity, along with F1-score are estimated. The evaluation found that the recommended classifier outperformed the competing learning strategies. The recommended framework is a practical and useful method for maternal and foetal surveillance that is powered by IoT and AI.&lt;/p&gt;

Concepts, objectives and analysis of public health surveillance systems

Public health surveillance (PHS) systems are an important tool to map the distribution and burden of disease as well as enable efficient distribution of resources to fight a disease. The surveillance systems are used to detect, report, track a disease as well as assess the response to the disease and people's attitudes. PHS systems are changing with the rapid change in technology and are becoming more real-time responsive with availability of new type of data such as online content and social media data. This review presents the basics of surveillance systems and develop from it to show the evolution of surveillance systems. The different forms of data available, surveillance methods and surveillance types are also reviewed such as social media based, web-based and clinical data based PHS maps. This review provide comprehensive details of the surveillance systems in terms of data types used, source of data and purpose of the surveillance system.

Literature review: Substance and infectious disease surveillance from wastewater-based epidemiology

As the scale of illicit drug abuse and the expansion of pandemic diseases increase, traditional epidemiological surveillance methods are unable to meet the demand of massive real-time observations. The promising approach of wastewater-based epidemiology (WBE) as an effective supplement to traditional surveillance methods relates domestic wastewater to the information of the population within a determined sewer collection area, as its advantages of non-privacy, low-cost and high-efficiency. Resorting to detection and analysis of wastewater samples, the consumption and dispersion of an epidemic substance in a community can be inverse-calculated in an easy way. This review introduces the origin and development of WBE to emerge the great potential of WBE in applications, and recommends basic procedures and calculation methods. Then we focus on its role in monitoring the consumption of some substances (e.g., tobacco, alcohol, sedative narcotic drugs, illicit psychotropic drugs, and synthetic chemicals) and its forewarning signal in epidemic, and finally provides insight into challenges and prospectives.

Vehicle recognition pipeline via DeepSort on aerial image datasets.

Unmanned aerial vehicles (UAVs) are widely used in various computer vision applications, especially in intelligent traffic monitoring, as they are agile and simplify operations while boosting efficiency. However, automating these procedures is still a significant challenge due to the difficulty of extracting foreground (vehicle) information from complex traffic scenes. This paper presents a unique method for autonomous vehicle surveillance that uses FCM to segment aerial images. YOLOv8, which is known for its ability to detect tiny objects, is then used to detect vehicles. Additionally, a system that utilizes ORB features is employed to support vehicle recognition, assignment, and recovery across picture frames. Vehicle tracking is accomplished using DeepSORT, which elegantly combines Kalman filtering with deep learning to achieve precise results. Our proposed model demonstrates remarkable performance in vehicle identification and tracking with precision of 0.86 and 0.84 on the VEDAI and SRTID datasets, respectively, for vehicle detection. For vehicle tracking, the model achieves accuracies of 0.89 and 0.85 on the VEDAI and SRTID datasets, respectively.

SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast

SoK of Machine Learning and Deep Learning Based Anomaly Detection Methods for Automatic Dependent Surveillance- Broadcast

MILITARY SURVEILLANCE BOT

This research describes a cutting-edge method for border and distant surveillance using multipurpose robots that are based on the most recent 3G technology used in defense and military applications. This autonomous vehicle has the capacity to conduct surveillance in border regions in place of soldiers. Using the internet as a communication tool, the robotic vehicle may be operated both manually and autonomously. This multimodal robot is employed in distant and combat zones to detect fire, explosives, hazardous gasses, and people. Traditional wireless security robots are rendered obsolete by their constrained human control and frequency range. The use of 3G technology, which has an infinite range, allows one to overcome these restrictions. By using solar panels, this system further improves the usage of renewable energy sources. Infrared and ultrasonic sensors are used to operate autonomously. The DTMF decoder controls the manual operation, and mobile phones are employed as video cameras by initiating 3G video calls and modifying the robot's course based on surrounding real-time information. The experimental findings of the solar panel's tilt angle selection and power consumption in both automated and manual modes are also shown in this study. Under certain conditions, this robotic vehicle may be used for both surveillance and reconnaissance.

Fusobacterium Nucleatum Aggravates Intestinal Barrier Impairment and Colitis Through IL-8 Induced Neutrophil Chemotaxis by Activating Epithelial Cells.

Inflammatory bowel disease (IBD) is affected by interactions between intestinal microbial factors, abnormal inflammation, and an impaired intestinal mucosal barrier. Neutrophils (NE) are key players in IBD. Fusobacterium nucleatum (F. nucleatum) is reported to contribute to IBD progression. However, the relationship between F. nucleatum, abnormal inflammation, and intestinal barrier impairment should be interpreted to understand the role of F. nucleatum in IBD. Dextran sulfate sodium (DSS)-induced colitis model was established and mice were orally administered with F. nucleatum. F. nucleatum colonization was confirmed by fluorescence in situ hybridization (FISH) and PCR. Intestinal barrier impairment was investigated by tight junction protein expression. Immuno-histochemistry (IHC) for Ly6G and flow cytometry detection to measure NE chemotaxis in mouse colon tissues. Caco-2 monolayers were used to evaluate epithelial integrity and permeability in vitro. A transwell model involving caco-2 cells and NE co-culture was used to assess NE chemotaxis. NE chemokines were measured by ELISA. A mouse model of NE exhaustion using an anti-Ly6G antibody was used to identify the role of NEs in F. nucleatum-induced colitis. Transcriptome sequencing and bioinformatics analysis were applied to screen cytokines and signaling pathways. Administration of F. nucleatum aggravated colitis in the DSS model. F. nucleatum infection downregulates ZO-1 and Occludin expression and increases intestinal permeability. Additionally, F. nucleatum-induced NE chemotaxis decreases the integrity and permeability of the caco-2 monolayer. F. nucleatum-induced NE chemotaxis is dependent on IEC-derived interleukin 8 (IL-8) secretion, mediated by the TLR2/ERK signaling pathway. In addition, NE exhaustion in mice inhibited F. nucleatum-induced intestinal barrier impairment and colitis. F. nucleatum improves NE chemotaxis by infecting intestinal epithelial cells (IECs) to secrete IL-8 and aggravate intestinal barrier impairment, contributing to the progression of intestinal inflammation. Examining and eliminating F. nucleatum could be a valuable microbiome-based method for IBD surveillance and prevention.

Measurement of enzyme activity of insoluble substrates based on ordered porous layer interferometry and the application in evaluation of thrombolytic drugs.

The development of innovative methods for real-time surveillance of enzymatic activity determination processes is essential, particularly for insoluble substrate enzymatic assessments. In this work, a novel method for enzymatic activity determination was devised by assembling a 190 nm silica colloidal crystal (SCC) film onto a glass slide, coupled with Ordered Porous Layer Interferometry (OPLI) technology. By fixing the substrate of the enzyme on the surface of the silica sphere, a solid-liquid interface can be formed for monitoring enzymatic activity. The enzymatic activity is gauged by the change in the SCC film's thickness caused by the digestion of the loaded substrate. The procedure of chymotrypsin-mediated casein digestion was documented in real time, facilitating the examination of chymotrypsin's activity and kinetics. The newly-developed enzymatic activity determination method demonstrated exceptional sensitivity towards chymotrypsin activity, with a linear range spanning 0.0505-2.02 units per mg. Additionally, the method was extended to the assessment of fibrinolysis enzyme activity and kinetic analysis, yielding promising results. Therefore, this technique can serve as a real-time, user-friendly, cost-effective novel approach for enzymatic activity determination, providing fresh perspectives for enzymatic activity determination studies.

Preparation of amphiphilic poly(divinylbenzene-co-N-vinylpyrrolidone)-functionalized polydopamine magnetic nanoadsorbents for enrichment of synthetic cannabinoids in wastewater.

Concerns have been raised about synthetic cannabinoids (SCs), which are among the most often trafficked and used illegal substances. An analytical method that holds promise for determining illicit drug use in the general population is wastewater-based epidemiology (WBE). Unfortunately, the concentration of SCs in wastewater is often extremely low on account of their hydrophobic nature, thus presenting a significant obstacle to the accurate detection and quantification of SCs using WBE. In this study, we present novel magnetic nanomaterials as amphiphilic adsorbents for pretreatment of wastewater using magnetic solid phase extraction (MSPE). Polydopamine-modified Fe3O4 nanoparticles were used as the magnetic core and further functionalized with poly(divinylbenzene-N-vinylpyrrolidone). Coupled with UHPLC-MS/MS analysis, an analytical method to simultaneously detect nine SCs at trace-levels in wastewater was developed and validated, enriching 50 mL wastewater to 100 μL with limits of detection (LOD) being 0.005-0.5 ng L-1, limits of quantification (LOQ) being 0.01-1.0 ng L-1, recoveries ranging from 73.99 to 110.72%, and the intra- and inter-day precision's relative standard deviations less than 15%. In comparison to the time-consuming conventional column-based solid phase extraction, the entire MSPE procedure from sample pre-treatment to data acquisition could be finished in one hour, thus largely facilitating the WBE method for drug surveillance and control.

Mental Health Admissions to Paediatric Wards Study (MAPS): protocol of a prospective study of mental health admissions to paediatric wards in England using surveillance and qualitative methods

IntroductionChildren and young people (CYP) presenting with a mental health (MH) crisis are frequently admitted to general acute paediatric wards as a place of safety. Prior to the pandemic, a...

Postpartum infections and antimicrobial resistance of responsible pathogens in Ukraine: results a multicenter study (2020-2022).

Aim: To determine the current prevalence of postpartum infections and antimicrobial resistance and antimicrobial resistance of responsible pathogens in Ukraine. Materials and Methods: Multicenter prospective cohort study was conducted from January 2020 to December 2022 in fifteen hospitals from twelve regions of Ukraine. Definitions of healthcare- associated postpartum infection were adapted from the Centers for Disease Control and Prevention's National Healthcare Safety Network. Antibiotic susceptibility was done by the disc diffusion test as recommended by EUCAST. Results: Among 21,968 women, 6,175 (28.1%) postpartum infections were observed. Of all postpartum infection cases, 83.1% were detected after hospital discharge. The postpartum infection rates were 17.3% after cesarean section and 10.8% after vaginal delivery. The most common postpartum infection types were endometritis (17.3%), followed by urinary tract Infection (3.5%), mastitis (3.4%), surgical site infection (excluding endometritis) (2.4%), and episiotomy site infection (1.5%). The predominant postpartum infection pathogens in Ukraine were: Escherichia coli (10.4%), Enterococcus spp. (9.6%), Staphylococcus aureus (6.7%), Pseudomonas aeruginosa (5.8%), Enterobacter spp. (5.8%). In our study pathogens of postpartum infection had differently levels of resistance to antibiotics. Conclusions: Our results indicate that postpartum infections requiring medical attention are common in Ukraine and that most postpartum infections occur after hospital discharge, so that use of routine inpatient surveillance methods alone will lead to underestimation of postpartum infection rates. Optimizing the antibiotic prophylaxis may reduce the burden of postpartum infection, but prevention is the key element.

Low prevalence of HIV in the northern Cameroon: contribution of some AIDS restriction genes and potential implications for gene therapy.

HIV infection and its progression to AIDS depend on several factors including host genetic factors. The immunological mechanisms of host resistance to HIV infection greatly influence the prevalence of HIV in a given region. Worldwide, Cameroon not exempted, the frequency of AIDS-associated genes varies and may influence this prevalence. The North and Far North Regions of Cameroon have had the lowest HIV prevalence in the country for many years despite risky behaviors associated with their customs and habits. In this work, we seek to explore the contribution of host genes to the HIV low prevalence in these regions. Five genes variants previously described as HIV AIDS related were studied. These genes are: CCR5Δ32, CCR5promoter59029G, CCR2-64I, SDF1-3'A and Trim5α(R136Q). A total of 384 consented participants were included in this study. The HIV serological status was confirmed using national algorithm. Genomic DNA was extracted from the buffy coats and used for genotyping. The results obtained were compiled in Excel 2016, Epi Info 7.1 and snpStats software and Chi two tests allowed us to compare the frequencies of the AIDS related alleles in the North with those in other Regions of Cameroon and to measure the impact of these ARGs on protection against HIV. The frequency of protective alleles CCR5Δ32, CCR5promoter59029G, CCR2-64I, SDF1-3'A and Trim5α(R136Q) was the allelic frequencies should be expressed as percentages i.e. 0.52%; 37.56%; 36.46%; 25.19% and 69.33%. These allelic frequencies exhibited a significant difference when compared to those obtained in other regions of Cameroon (p < 0.01). Protective alleles were predominant in the Northern region compared to others and were associated with resistance to HIV [(p < 0.0001); OR = 2.02 CI, 95%]. The higher frequency of HIV-protective alleles in the northern regions may be a contributing factor to the lower prevalence of HIV. Nevertheless, this should be reinforced by other preventive and surveillance methods to guarantee the sustained low prevalence. HIV can develop resistance through the process of mutation, but the host targets themselves are genetically stable. The study of these host genetic restriction factors is of great value in the design of a practical cure for HIV infection or an effective vaccine.

The evolution of Kenya's animal health surveillance system and its potential for efficient detection of zoonoses.

Animal health surveillance systems in Kenya have undergone significant changes and faced various challenges throughout the years. In this article, we present a comprehensive overview of the Kenya animal health surveillance system (1944 to 2024), based on a review of archived documents, a scoping literature review, and an examination of past surveillance assessments and evaluation reports. The review of archived documents revealed key historical events that have shaped the surveillance system. These include the establishment of the Directorate of Veterinary Services in 1895, advancements in livestock farming, the implementation of mandatory disease control interventions in 1944, the growth of veterinary services from a section to a ministry in 1954, the disruption caused by the Mau Mau insurrection from 1952 to 1954, which led to the temporary halt of agriculture in certain regions until 1955, the transition of veterinary clinical services from public to private, and the progressive privatization plan for veterinary services starting in 1976. Additionally, we highlight the development of electronic surveillance from 2003 to 2024. The scoping literature review, assessments and evaluation reports uncovered several strengths and weaknesses of the surveillance system. Among the strengths are a robust legislative framework, the adoption of technology in surveillance practices, the existence of a formal intersectoral coordination platform, the implementation of syndromic, sentinel, and community-based surveillance methods, and the presence of a feedback mechanism. On the other hand, the system's weaknesses include the inadequate implementation of strategies and enforcement of laws, the lack of standard case definitions for priority diseases, underutilization of laboratory services, the absence of formal mechanisms for data sharing across sectors, insufficient resources for surveillance and response, limited integration of surveillance and laboratory systems, inadequate involvement of private actors and communities in disease surveillance, and the absence of a direct supervisory role between the national and county veterinary services. To establish an effective early warning system, we propose the integration of surveillance systems and the establishment of formal data sharing mechanisms. Furthermore, we recommend enhancing technological advancements and adopting artificial intelligence in surveillance practices, as well as implementing risk-based surveillance to optimize the allocation of surveillance resources.

Urinary mRNA-based biomarkers for non-muscle-invasive bladder cancer: a mini-review.

Bladder cancer (BC) is the second most common type of cancer of the urinary system. Approximately 75% of the cases are non-muscle invasive bladder cancer (NMIBC), which has a high recurrence and progression rate. Current diagnosis and surveillance methods present challenges, including risks to the patients. For this reason, urinary biomarkers have been proposed as alternatives to the methods. The goal of this mini-review is to describe urinary mRNA-based biomarkers available in current literature for NMIBC tumors, using the PubMed database. The search included the following keywords: "biomarkers" AND "bladder cancer" AND "urine" and "RNA" and "non-muscle". The search yielded 11 original researchers utilizing mRNA-based urinary biomarkers. Although there is a wide variety of biomarkers described, the cohorts of the studies were not exclusively NMIBC, which is the subtype of BC that would mostly benefit from the introduction of a good follow-up biomarker, highlighting the need for randomized interventional trials for NMIBC.

Respiratory human adenovirus outbreak captured in wastewater surveillance

Respiratory adenovirus wastewater surveillance was compared to clinical data from a 2022 outbreak, showing a correlation with cases. Respiratory adenoviruses were less prevalent, with types 40/41 dominating. The schematic illustrates findings.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

IntroductionTraditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks...