Traditional Surveillance Methods Research Articles

Real-Time Violence Detection in Surveillance Videos Using Deep Learning Approach

Abstract: In order to tackle the ongoing problem of keeping fruits fresh, this research presents an automated freshness detection. The ever-growing presence of surveillance cameras has undoubtedly enhanced public safety. However, manually reviewing vast quantities of video footage for violence detection remains a tedious and error-prone task. This paper proposes a deep learningbased approach for anomaly detection in surveillance videos, with a specific focus on identifying violent activities. Traditional surveillance methods often lack the sophistication to distinguish between normal behaviour and potentially threatening actions. Deep learning offers a significant advantage by automating the identification of deviations from established behavioural patterns in video data. This automation enables real-time analysis of footage, potentially signifying the occurrence of violent incidents and allowing for a swifter response from security personnel. This research delves into the application of deep learning models for violence detection in surveillance footage. We begin by discussing the limitations inherent in conventional methods, such as motion detection and manual review, which struggle to capture the nuances of human behaviour. Subsequently, we explore the advantages that deep learning-based approaches bring to the table, including their ability to learn complex patterns from large datasets and identify subtle anomalies that might escape human observation.

A systematic review on the incidence of influenza viruses in wastewater matrices: Implications for public health.

Influenza viruses pose a significant public health threat, necessitating comprehensive surveillance strategies to enhance early detection and preventive measures. This systematic review investigates the incidence of influenza viruses in wastewater matrices, aiming to elucidate the potential implications for public health. The study synthesizes existing literature, employing rigorous inclusion criteria to identify relevant studies conducted globally. The essence of the problem lies in the gaps of traditional surveillance methods, which often rely on clinical data and may underestimate the true prevalence of influenza within communities. Wastewater-based epidemiology offers a novel approach to supplementing these conventional methods, providing a broader and more representative assessment of viral circulation. This review systematically examines the methodologies employed in the selected studies, including virus concentration techniques and molecular detection methods, to establish a standardized framework for future research. Our findings reveal a consistent presence of influenza viruses in diverse wastewater matrices across different geographic locations and seasons. Recommendations for future research include the standardization of sampling protocols, improvement of virus concentration methods, and the integration of wastewater surveillance into existing public health frameworks. In conclusion, this systematic review contributes to the understanding of influenza dynamics in wastewater matrices, offering valuable insights for public health practitioners and policymakers. Implementation of wastewater surveillance alongside traditional methods can enhance the resilience of public health systems and better prepare communities for the challenges posed by influenza outbreaks.

SPREAD: Spatiotemporal Pathogen Relationships and Epidemiological Analysis Dashboard.

In the scope of public health, the rapid identification and control of infectious disease outbreaks are a paramount concern. Traditional surveillance methods often face challenges in effectively combining genetic, geographical, and temporal data, which is crucial for a comprehensive understanding of disease transmission dynamics. Addressing this critical need, the Spatiotemporal Phylogenomic Research and Epidemiological Analysis Dashboard (SPREAD) emerges as an innovative standalone web-based application. SPREAD integrates several modules for detailed genomic relationships, pinpointing genetically close pathogens, and spatial mapping, providing in-depth views of how diseases spread across populations and territories, with significant advantage to manage both bacteria and viruses based on allele and variant calling, respectively. Designed for broad accessibility, SPREAD operates seamlessly within web browsers, eliminating the need for sophisticated IT infrastructure and facilitating its use across various public health contexts. Its intuitive interface ensures that users can effortlessly navigate complex datasets, facilitating widespread access to advanced surveillance capabilities. Through its initial deployments, SPREAD has proven instrumental in quickly identifying transmission clusters, significantly aiding in the formulation of prompt and targeted public health responses. Through the integration of state-of-the-art technology with a focus on user-centered design, SPREAD offers a promising solution that highlights the potential of digital health innovations.

The Association Between Internet Search Patterns and Scabies Incidence Across the United States

Background
 Scabies, a contagious skin infestation caused by Sarcoptes scabiei, is a significant public health concern [1]. Traditional surveillance methods for scabies suffer from time lag and incomplete data, hindering early detection and response [5]. The widespread use of the internet and search engines, such as GoogleTM, offer new opportunities for alternative surveillance approaches. 
 Objective
 This study aimed to explore the association between scabies search volumes on Google TrendsTM (GTs) and scabies incidence at the state level across the United States.
 Methods
 GTs data for each U.S. state and scabies incidence from 2011 to 2019 were analyzed for summary statistics and association.
 Results
 The mean Spearman correlation coefficient for the period of 2011-2019 indicated a strong positive correlation between GTs RSVs for “scabies” and the incidence of scabies in the United States. Using an unpaired t-test, this correlation was found to be statistically significant. 
 Conclusions
 In resource-scarce environments where access to care is a common barrier, healthcare providers and departments can leverage this information to effectively target populations and employ resources for scabies prevention and treatment. Analyzing search engine term patterns can enhance our understanding of people's behavior when they suspect a scabies infestation.

Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics.

The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore. This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats. This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities. Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore. As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events.

Web search volume as a near-real-time complementary surveillance tool of tick-borne encephalitis (TBE) in Italy

The Internet is an important gateway for accessing health-related information, and data generated through web queries have been increasingly used as a complementary source for monitoring and forecasting of infectious diseases and they may partially address the issue of underreporting. In this study, we assessed whether tick-borne encephalitis (TBE)-related Internet search volume may be useful as a complementary tool for TBE surveillance in Italy. Monthly Google Trends (GT) data for TBE-related information were extracted for the period between January 2017 and September 2022, corresponding to the available time series of TBE notifications in Italy. Time series modeling was performed by applying seasonal autoregressive integrated moving average (SARIMA) models with or without GT data. The search terms relative to tick bites reflected best the observed temporal distribution of TBE cases, showing a correlation coefficient of 0.81 (95 % CI: 0.71–0.88). Particularly, both the reported number of TBE cases and GT searches occurred mainly during the summer. The peak of disease notifications coincided with that of Google searches in 4 of 6 years. Once calibrated, SARIMA models with or without GT data were applied to a validation set. Retrospective forecast made by the model with GT data was associated with a lower prediction error and accurately predicted the peak timing. By contrast, the traditional SARIMA model underestimated the actual number of TBE notifications by 65 %. Timeliness, easy availability, low cost and transparency make monitoring of the TBE-related Internet search queries a promising addition to the traditional methods of TBE surveillance in Italy.

Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex

BackgroundMosquito-borne diseases are a major concern for public and veterinary health authorities, highlighting the importance of effective vector surveillance and control programs. Traditional surveillance methods are labor-intensive and do not provide high temporal resolution, which may hinder a full assessment of the risk of mosquito-borne pathogen transmission. Emerging technologies for automated remote mosquito monitoring have the potential to address these limitations; however, few studies have tested the performance of such systems in the field.MethodsIn the present work, an optical sensor coupled to the entrance of a standard mosquito suction trap was used to record 14,067 mosquito flights of Aedes and Culex genera at four temperature regimes in the laboratory, and the resulting dataset was used to train a machine learning (ML) model. The trap, sensor, and ML model, which form the core of an automated mosquito surveillance system, were tested in the field for two classification purposes: to discriminate Aedes and Culex mosquitoes from other insects that enter the trap and to classify the target mosquitoes by genus and sex. The field performance of the system was assessed using balanced accuracy and regression metrics by comparing the classifications made by the system with those made by the manual inspection of the trap.ResultsThe field system discriminated the target mosquitoes (Aedes and Culex genera) with a balanced accuracy of 95.5% and classified the genus and sex of those mosquitoes with a balanced accuracy of 88.8%. An analysis of the daily and seasonal temporal dynamics of Aedes and Culex mosquito populations was also performed using the time-stamped classifications from the system.ConclusionsThis study reports results for automated mosquito genus and sex classification using an optical sensor coupled to a mosquito trap in the field with highly balanced accuracy. The compatibility of the sensor with commercial mosquito traps enables the sensor to be integrated into conventional mosquito surveillance methods to provide accurate automatic monitoring with high temporal resolution of Aedes and Culex mosquitoes, two of the most concerning genera in terms of arbovirus transmission.Graphical

The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients.

Continuous surveillance for healthcare-associated infections such as central venous catheter-related bloodstream infections (CVC-BSI) is crucial for prevention. However, traditional surveillance methods are resource-intensive and prone to bias. This study aimed to develop and validate fully-automated surveillance algorithms for CVC-BSI. Two algorithms were developed using electronic health record data from 1000 admissions with a positive blood culture (BCx) at Karolinska University Hospital from 2017: (1) Combining microbiological findings in BCx and CVC cultures with BSI symptoms; (2) Only using microbiological findings. These algorithms were validated in 5170 potential CVC-BSI-episodes from all admissions in 2018-2019, and results extrapolated to all potential CVC-BSI-episodes within this period (n = 181,354). The reference standard was manual record review according to ECDC's definition of microbiologically confirmed CVC-BSI (CRI3-CVC). In the potential CVC-BSI-episodes, 51 fulfilled ECDC's definition and the algorithms identified 47 and 49 episodes as CVC-BSI, respectively. Both algorithms performed well in assessing CVC-BSI. Overall, algorithm 2 performed slightly better with in the total period a sensitivity of 0.880 (95%-CI 0.783-0.959), specificity of 1.000 (95%-CI 0.999-1.000), PPV of 0.918 (95%-CI 0.833-0.981) and NPV of 1.000 (95%-CI 0.999-1.000). Incidence according to the reference and algorithm 2 was 0.33 and 0.31 per 1000 in-patient hospital-days, respectively. Both fully-automated surveillance algorithms for CVC-BSI performed well and could effectively replace manual surveillance. The simpler algorithm, using only microbiology data, is suitable when BCx testing adheres to recommendations, otherwise the algorithm using symptom data might be required. Further validation in other settings is necessary to assess the algorithms' generalisability.

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.

Bazı Ekmeklik ve Makarnalık Buğday Çeşitlerinde Sarı Pas Hastalığının Fenolojik Dönemlere göre Hastalık-Verim İlişkisinin Çok Bantlı (Hiperspektral) Veriler Kullanılarak Araştırılması

The aim of this study was to evaluate the severity of yellow rust in different phenological periods by 
 subjecting bread (Bayraktar 2000, Demir 2000, Eser and Kenanbey) and durum (Çeşit-1252, Eminbey, Kızıltan 91 and Mirzabey 2000) wheat varieties to different spore doses (0%, 25%, 50% and 100%) under controlled epidemic conditions. The research was conducted in Yenimahalle, Ankara, Turkey during the 2018-2019 growing season. In the study, the morphological changes in yellow rust severity were determined at different phenological developmental stages of the test materials with the reflectance values obtained by using handheld spectroradiometer in different spore dose applications during the period from tillering to stalk emergence. These reflectance values were converted into vegetation index values expressed by mathematical formulae and used in determining yield estimates. Considering the results obtained, it was determined that the spectral indices calculated especially in the early flowering period (25 May 2019, Feekes 10.5.1) were effective in yield estimation for all bread varieties except Kenanbey variety (15 June 2019, Feekes 10.5.4). It was determined that the spectral band region of 25 May 2019 (Feekes 10.5.1), which includes all indices determined to predict yield in all bread and durum varieties and which is the beginning of flowering, was effective. In grain yield estimation, it was determined that there was a decrease in the correlation values of the spectral indices starting from the early flowering period (Feekes 10.5.1) towards the grain setting period (Feekes 10.5.3) and milk maturity period 
 (Feekes 10.5.4). When the correlations between these index values and yield values were examined, it was 
 determined that prominent phenological periods and high correlation indices could be calculated for these periods. Nowadays, with the use of optical sensor technology instead of traditional disease surveillance methods, the way has paved the way for the development of new approaches for early, fast and accurate yield estimation as a result of the verification of images taken by unmanned aerial vehicles on which multispectral and hyperspectral cameras are located with ground data using artificial intelligence and deep learning techniques.

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.

Global mosquito observations dashboard (GMOD): creating a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes

BackgroundMosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide.MethodsGMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection.ResultsSince its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs.ConclusionsGMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.

Association Between Aneurysm Wall Inflammation Detected by Imaging Perivascular Fat and Secondary Intervention Risk for Abdominal Aortic Aneurysm Patients After Endovascular Repair.

To investigate the association between the imaging biomarker (volumetric perivascular characterization index [VPCI]) which indicates the aortic wall inflammation by mapping the spatial changes of perivascular fat attenuation on computed tomography angiography (CTA) and the reintervention risk for abdominal aortic aneurysm (AAA) patients after endovascular aortic repair (EVAR). This case-control study included AAA patients undergoing EVAR from a single center (n=260). Cases were AAA patients undergoing reintervention after EVAR and a 1:1 frequency-matched control group of AAA patients post-EVAR with a shrunken or ≥3-year stable sac and free of reintervention signs during the follow-up. The predictive variable (VPCI trajectory) was converted to binary variables according to the changing trend of VPCI with follow-up time. As a quasi-complete separation data pattern, least absolute shrinkage and selection operator (lasso) regression was used to screen and prove the VPCI trajectory as the best predictor, and the performance was evaluated by calculating the accuracy, sensitivity, and specificity. Between 2010 and 2021, 15 AAA patients after EVAR with type I/III endoleak, aneurysm rupture, or impending rupture were included. Compared with the 1:1 frequency-matched controls with a shrunken or ≥3-year stable sac and free of reintervention signs during the follow-up, VPCI trajectories of the case group were all upward trends, whereas the controls showed 86.7% downward trends (p<0.001). The best predictive model of lasso regressions included 4 variables, and VPCI trajectory was the most outstanding, followed by the proximal landing zone, the distal landing zone, and the infrarenal β angle. The accuracy, sensitivity, and specificity of predicting the risk of reintervention were as follows, respectively: 93.3%, 100%, and 86.7%. The wall inflammation detected by imaging perivascular adipose tissue based on the CTAs was strongly associated with the reintervention risk for AAA patients after EVAR, which might hold major promise as a new imaging biomarker for the mechanism and treatment study of human AAAs before and after EVAR. The study introduces a novel imaging biomarker which indicates the aortic wall inflammation by mapping spatial changes of perivascular fat attenuation on CTA. This biomarker demonstrates a strong association with the reintervention risk in AAA patients after EVAR. Incorporation of VPCI into clinical practice has the potential to enhance the traditional surveillance methods (CT/CTAs) by providing clinicians with a non-invasive method to assess aortic wall inflammation and predict the risk of reintervention. Additionally, this study might offer a valuable tool for mechanism and treatment research in humans with AAAs both pre- and post-EVAR, ultimately improving patient outcomes and refining therapeutic strategies.

An effective internet-based system for surveillance and elimination of triatomine insects: AlertaChirimacha.

Vector-borne diseases remain a significant public health threat in many regions of the world. Traditional vector surveillance and control methods have relied on active and passive surveillance programs, which are often costly and time-consuming. New internet-based vector surveillance systems have shown promise in removing some of the cost and labor burden from health authorities. We developed and evaluated the effectiveness of a new internet-based surveillance system, "AlertaChirimacha", for detecting Triatoma infestans (known locally by its Quechua name, Chirimacha), the Chagas disease vector, in the city of Arequipa, Peru. In the first 26 months post-implementation, AlertaChirimacha received 206 reports of residents suspecting or fearing triatomines in their homes or neighborhoods, of which we confirmed, through pictures or inspections, 11 (5.3%) to be Triatoma infestans. After microscopic examination, none of the specimens collected were infected with Trypanosoma cruzi. AlertaChirimacha received 57% more confirmed reports than the traditional surveillance system and detected 10% more infested houses than active and passive surveillance approaches combined. Through in-depth interviews we evaluate the reach, bilateral engagement, and response promptness and efficiency of AlertaChirimacha. Our study highlights the potential of internet-based vector surveillance systems, such as AlertaChirimacha, to improve vector surveillance and control efforts in resource-limited settings. This approach could decrease the cost and time horizon for the elimination of vector-mediated Chagas disease in the region.

Text mining of veterinary forums for epidemiological surveillance supplementation

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.

Schools and Wastewater Surveillance: Practical Implications for an Emerging Technology to Impact Child Health.

Since the start of the COVID-19 pandemic, wastewater surveillance has emerged as a public health tool that supplements traditional surveillance methods used to detect the prevalence of the SARS-CoV-2 virus in communities. In May 2020, the Houston Health Department (HHD) partnered with a coalition of municipal and academic partners to develop a wastewater monitoring and reporting system for the city of Houston, Texas. The HHD subsequently launched a program to conduct targeted wastewater sampling at 52 school sites located in a large, urban school district in Houston. Data generated by this program are shared with school district officials and nurses from participating schools. Although initial feedback from these stakeholders indicated that they considered the wastewater data valuable, the emergency nature of the pandemic prevented a systematic evaluation of the program. To address this gap in knowledge, the HHD and Rice University conducted a study to determine how wastewater data are used to make decisions about COVID-19 prevention and mitigation practices in schools. Our findings indicate that maximizing the utility of wastewater data in the school context will require the development of communication strategies and education efforts tailored to the needs of specific audiences and improving collaboration between local health departments, school districts, and school nurses.

Digital tools for post-discharge surveillance of surgical site infection.

Conduct a scoping review on the development and use of digital tools for post-discharge surgical site infection surveillance. Scoping review. Science Direct, PubMed, Embase, Literatura Latino-Americana e do Caribe em Ciências da Saúde and Cumulative Index to Nursing and Allied Health Literature were searched from 2013 to May 2022. Six intellectual property registries were reviewed from 2013 to 2022. The review followed the Joanna Briggs Institute model, and included intellectual property records (applications, prototypes and software) and scientific articles published in any language on the development and/or testing of digital tools for post-discharge surveillance of surgical site infection among surgical patients aged 18 and over. One intellectual property record and 13 scientific articles were identified, covering 10 digital tools. The intellectual property record was developed and registered by a China educational institution in 2018. The majority of manuscripts were prospective cohort studies and randomized clinical trials, published between 2016 and 2022, and more than half were conducted in the United States. The population included adult patients undergoing cardiac, thoracic, vascular, abdominal, arthroplasty and caesarean surgery. The main functionalities of the digital tools were the previously prepared questionnaire, the attachment of a wound image, the integrated Web system and the evaluation of data by the health team, with post-discharge surgical site infection surveillance time between 14 and 30 days after surgery. Digital tools show promise for the surveillance of surgical site infection, collaborating with the early detection of wound infection. Mobile technology was favourable for detecting surgical site infections, reducing unnecessary visits to the health service, and increasing patient satisfaction. Technological advances in the health area open new perspectives for post-discharge surveillance of surgical site infection. WHAT IS ALREADY KNOWN?: There is underreporting of surgical site infections due to difficulties related to traditional methods of post-discharge surveillance. The use of digital tools within surgical site infection surveillance is increasing. Benefits of using digital tools within surgical site infection surveillance have been reported. WHAT HAS THIS STUDY ADDED TO OUR KNOWLEDGE?: This scoping review is one of the first to analyse the development and use of digital tools for post-discharge surveillance of surgical site infection in different countries. The main functionalities of digital tools are: structured questionnaires; attachment of wound images; integrated web systems; and evaluation of data by professionals. The use of mobile technology is favourable for detecting surgical site infections with a reduction in costs from face-to-face consultations and increased patient satisfaction. WHERE AND ON WHOM WILL THE RESEARCH HAVE AN IMPACT?: Healthcare providers can successfully use digital tools for surgical site infection post-discharge surveillance. Remote monitoring can reduce unnecessary patient visits to healthcare facilities. Policy makers can study how to implement digital platforms for remote patient monitoring. PRISMA statement for Scoping Reviews (PRISMA-ScR). No patient or public contribution. The study protocol was registered in the OSF (https://doi.org/10.17605/OSF.IO/BA8D6).

Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada.

The use of social media data provides an opportunity to complement traditional influenza and COVID-19 surveillance methods for the detection and control of outbreaks and informing public health interventions. The first aim of this study is to investigate the degree to which Twitter users disclose health experiences related to influenza and COVID-19 that could be indicative of recent plausible influenza cases or symptomatic COVID-19 infections. Second, we seek to use the Twitter datasets to train and evaluate the classification performance of Bidirectional Encoder Representations from Transformers (BERT) and variant language models in the context of influenza and COVID-19 infection detection. We constructed two Twitter datasets using a keyword-based filtering approach on English-language tweets collected from December 2016 to December 2022 in Saskatchewan, Canada. The influenza-related dataset comprised tweets filtered with influenza-related keywords from December 13, 2016, to March 17, 2018, while the COVID-19 dataset comprised tweets filtered with COVID-19 symptom-related keywords from January 1, 2020, to June 22, 2021. The Twitter datasets were cleaned, and each tweet was annotated by at least two annotators as to whether it suggested recent plausible influenza cases or symptomatic COVID-19 cases. We then assessed the classification performance of pre-trained transformer-based language models, including BERT-base, BERT-large, RoBERTa-base, RoBERT-large, BERTweet-base, BERTweet-covid-base, BERTweet-large, and COVID-Twitter-BERT (CT-BERT) models, on each dataset. To address the notable class imbalance, we experimented with both oversampling and undersampling methods. The influenza dataset had 1129 out of 6444 (17.5%) tweets annotated as suggesting recent plausible influenza cases. The COVID-19 dataset had 924 out of 11939 (7.7%) tweets annotated as inferring recent plausible COVID-19 cases. When compared against other language models on the COVID-19 dataset, CT-BERT performed the best, supporting the highest scores for recall (94.8%), F1(94.4%), and accuracy (94.6%). For the influenza dataset, BERTweet models exhibited better performance. Our results also showed that applying data balancing techniques such as oversampling or undersampling method did not lead to improved model performance. Utilizing domain-specific language models for monitoring users' health experiences related to influenza and COVID-19 on social media shows improved classification performance and has the potential to supplement real-time disease surveillance.

Musculoskeletal Complaint Epidemiology inAustralian Special Operation Forces Trainees.

Elite military trainees are burdened by high numbers of musculoskeletal (MSK) injuries and are a priority military population for injury prevention. This research aims to describe the MSK complaint epidemiology of trainees undertaking special forces (SF) training in the Australian Defence Force (ADF). One barrier to accurate injury surveillance in military populations is that traditional surveillance methods rely on personnel engaging with the military healthcare system to collect injury data. This approach is likely to underestimate the injury burden as it is known that many military personnel, particularly trainees, avoid reporting their injuries because of various motives. Subsequently, the insights from surveillance systems may underestimate the injury burden and limit the ability to inform prevention requirements. This research aims to actively seek MSK complaint information directly from trainees in a sensitive manner to mediate injury-reporting behaviors. This descriptive epidemiology study included two consecutive cohorts of ADF SF trainees from 2019 to 2021. Musculoskeletal data items and their respective recording methods were based on international sports injury surveillance guidelines and adapted to a military context. Our case definition encompassed all injuries or physical discomforts as recordable cases. A unit-embedded physiotherapist retrospectively collected MSK complaint data from selection courses and collected prospective data over the training continuum. Data collection processes were external to the military health care system to mediate reporting avoidance and encourage injury reporting. Injury proportions, complaint incidence rates, and incidence rate ratios were calculated and compared between training courses and cohorts. In total, 334 MSK complaints were reported by 103 trainees (90.4%), with a complaint incidence rate of 58.9 per 1,000 training weeks (95% CI, 53.0-65.5). Of these MSK complaints, 6.4% (n = 22) resulted in time loss from work. The lumbar spine (20.6%, n = 71) and the knee (18.9%, n = 65) were the most frequently affected body parts. Most of the MSK complaints were reported during selection courses (41.9%), followed by field survival and team tactics (23.0%) and urban operations courses (21.9%). Physical training accounted for 16.5% of complaints. Fast-roping training was associated with more severe MSK complaints. Musculoskeletal complaints are highly prevalent in ADF SF trainees. Complaints are more frequently reported in selection and qualification training courses than in physical training. These activities are priorities for focused research to understand injury circumstances in ADF elite training programs to inform injury prevention strategies. A strength of our study is the data collection methods which have provided greater MSK complaint information than past research; however, much work remains in conducting consistent and accurate surveillance. Another strength is the use of an embedded physiotherapist to overcome injury-reporting avoidance. Embedded health professionals are recommended as continued practice for ongoing surveillance and early intervention.

Epidemic season 2021–2022: Frequency of co-infection by respiratory viral pathogens

BACKGROUND: This study was conducted to determine the characteristics of various viral respiratory pathogens spreading during the epidemic season 20212022 and the frequency of co-infection with SARS-CoV-2 and influenza.&#x0D; AIM: To assess the development of the influenza epidemic and frequency of cases of co-infection with respiratory pathogens in patients with acute respiratory viral infections between 2021 and 2022.&#x0D; MATERIALS AND METHODS: Traditional and hospital epidemiological surveillance methods for acute respiratory viral infections were used.&#x0D; RESULTS: The epidemic season of 20212022 was characterized by the early activity of the influenza A(H3N2) virus and the emergence and rapid spread of the omicron variant of SARS-CoV-2. The distribution of different respiratory pathogens during the epidemic season 20212022 was clearly traced: SARS-CoV-2 (18.8%) was predominant, followed by influenza viruses (10.6%) and pathogens of other acute respiratory viral infections (0.43.7%). With respect to influenza A (H3N2) and B viruses, the heterogeneity of their populations and drift variability in relation to vaccine strains were noted.&#x0D; DISCUSSION: The frequency of co-infection with various respiratory pathogens was low, i.e., it was no more than 0.1%according to traditional surveillance, and no more than 9.2% in the hospital surveillance. The rationale for updating the composition of influenza vaccines for the countries in the Northern Hemisphere for 20222023 season was identified.&#x0D; CONCLUSION: At present, early diagnosis of influenza is important given the availability of effective drugs with a direct mechanism of action for the prevention and treatment of this pathogen. Timely use of anti-influenza drugs will reduce the risks of a severe course, complications, and death, including co-infection with SARS-CoV-2.