Traditional Surveillance Methods Research Articles

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.

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.

MACHINE LEARNING-BASED FACIAL RECOGNITION FOR VIDEO SURVEILLANCE SYSTEMS

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model''''s performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.

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.

Bats are sentinels for invasive pest surveillance based on DNA metabarcoding

Loss of crop yield due to non-native invasive pests is a global phenomenon with profound population, biodiversity, ecosystem, and economic impacts. The polyphagous and migratory fall armyworm (FAW) Spodoptera frugiperda (Lepidoptera, Noctuidae) has become a major invasive pest around the globe in the past decade, posing a great threat to global food security. Early warning and timely detection of this pest and its associated agricultural risks are essential for preventing, intervening, controlling, and mitigating invasion threats, which could reduce crop losses and economic damage. However, one key challenge is how to effectively and accurately monitor pest invasion as soon as possible prior to their establishment. Here, we aimed to determine the effectiveness of four different methods for identifying the presence of FAW, namely DNA metabarcoding, field survey, light trap, and sex pheromone trap. We collected fecal samples of six bat species in the FAW colonization area, including Rhinolophus pearsonii, Rhinolophus rex, Ia io, Myotis altarium, Myotis pilosus, and Myotis chinensis. We then utilized bat fecal samples to monitor FAW through DNA metabarcoding during its early establishment stages and compared this approach to three traditional surveillance methods. The results revealed interspecific differences in the occurrence frequency of FAW in different bat species. Furthermore, bat fecal DNA metabarcoding successfully monitored the occurrence of this pest nearly one month in advance compared with traditional approaches, which coincides with the adult migration time of larval counterparts found in the field based on effective accumulated temperature calculations. Our results showed that DNA metabarcoding dietary analysis of generalist insectivorous bats can be an effective, noninvasive, and economically viable tool for invasion pests monitoring to inform management decisions.

Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review.

This study reviews the current utilization of electronic health records (EHRs) for chronic disease surveillance, discusses approaches that are used in obtaining EHR-derived disease prevalence estimates, and identifies health indicators that have been studied using EHR-based surveillance methods. PubMed was searched for relevant keywords: (electronic health records [Title/Abstract] AND surveillance [Title/Abstract]) OR (electronic medical records [Title/Abstract] AND surveillance [Title/Abstract]). Articles were assessed based on detailed inclusion and exclusion criteria and organized by common themes, as per the PRISMA review protocol. The study period was limited to 2015-2021 due to the wider adoption of EHR in the U.S. only since 2015. The review included only US studies and only those that focused on chronic disease surveillance. 17 studies were included in the review. The most common approaches the review identified focused on validating EHR-derived estimates against those from traditional national surveys. The most studied conditions were diabetes, obesity, and hypertension. The majority of reviewed studies demonstrated comparable prevalence estimates with traditional population health surveillance surveys. The most common approach for the estimation of chronic disease conditions was to use small-area estimation by geographic patterns, neighborhoods, or census tracts. The use of EHR-based surveillance systems for public health purposes is feasible, and the population health estimates appear comparable to those obtained through traditional surveillance surveys. The application of EHRs for public health surveillance appears promising and could offer a real-time alternative to traditional surveillance methods. A timely assessment of population health at local and regional levels would ensure a more targeted allocation of public health and healthcare resources as well as more effective intervention and prevention initiatives.

Enterovirus characterized from cerebrospinal fluid in a cohort from the Eastern United States.

Enteroviruses (EVs) are predominant causes of a spectrum of neurological diseases. To better understand the origins of the outbreaks of disease associated with EV, it is essential to develop an efficient surveillance system that identifies the circulating EVs and correlate their genomic evolution with the disease presentations. The clinical presentations of patients with positive EV from cerebrospinal fluid (CSF) between 2014 and 2022, diagnosed at the Johns Hopkins Medical Microbiology Laboratory, were compared from year to year. EV typing and whole genome sequencing were performed and correlated to the spectrum of disease. A total of 95 CSF specimens were positive for EV between 2014 and 2022. The percentage positivity ranged from the lowest of 1.1% in 2020 to the highest of 3.2% in 2015. The median ages declined from 22 years in 2014 to less than one year starting in 2016 to 34 in 2022. Typing using VP1 sequencing revealed that E30 and E6 were associated with meningitis in adults but coxsackieviruses (CVs-B3 and B5) were detected from pediatric patients with fever. Whole genome sequencing revealed multiple recombination events. In 2020, a recombinant CV-A9 was detected in a CSF sample associated with unusual presentation of sepsis, profound acute bilateral sensory neural hearing loss, and myofasciitis. EV genomic surveillance is needed for a better understanding of the genetic determinants of neurovirulence. Whole genome sequencing can reveal recombination events missed by traditional molecular surveillance methods.

Leveraging Electronic Health Record Data for Timely Chronic Disease Surveillance: The Multi-State EHR-Based Network for Disease Surveillance.

Electronic health record (EHR) data can potentially make chronic disease surveillance more timely, actionable, and sustainable. Although use of EHR data can address numerous limitations of traditional surveillance methods, timely surveillance data with broad population coverage require scalable systems. This report describes implementation, challenges, and lessons learned from the Multi-State EHR-Based Network for Disease Surveillance (MENDS) to help inform how others work with EHR data to develop distributed networks for surveillance. Funded by the Centers for Disease Control and Prevention (CDC), MENDS is a data modernization demonstration project that aims to develop a timely national chronic disease sentinel surveillance system using EHR data. It facilitates partnerships between data contributors (health information exchanges, other data aggregators) and data users (state and local health departments). MENDS uses query and visualization software to track local emerging trends. The program also uses statistical and geospatial methods to generate prevalence estimates of chronic disease risk measures at the national and local levels. Resulting data products are designed to inform public health practice and improve the health of the population. MENDS includes 5 partner sites that leverage EHR data from 91 health system and clinic partners and represents approximately 10 million patients across the United States. Key areas of implementation include governance, partnerships, technical infrastructure and support, chronic disease algorithms and validation, weighting and modeling, and workforce education for public health data users. MENDS presents a scalable distributed network model for implementing national chronic disease surveillance that leverages EHR data. Priorities as MENDS matures include producing prevalence estimates at various geographic and subpopulation levels, developing enhanced data sharing and interoperability capacity using international data standards, scaling the network to improve coverage nationally and among underrepresented geographic areas and subpopulations, and expanding surveillance of additional chronic disease measures and social determinants of health.

Surveillance for Avocado Sunblotch Viroid Utilizing the European Honey Bee (Apis mellifera).

Avocado is one of the world's fastest growing tropical fruit industries, and the pathogen avocado sunblotch viroid (ASBVd) is a major threat to both production and access to international export markets. ASBVd is seed transmissible, with infection possible via either the male (pollen) or female gametes. Surveillance for ASBVd across commercial orchards is a major logistical task, particularly when aiming to meet the stringent standards of evidence required for a declaration of pest freedom. As with many fruit crops, insect pollination is important for high avocado yields, and honey bee (Apis mellifera) hives are typically moved into orchards for paid pollination services. Exploiting the foraging behavior of honey bees can provide a complementary strategy to traditional surveillance methods. High-throughput sequencing (HTS) of bee samples for plant viruses shows promise, but this surveillance method has not yet been tested for viroids or in a targeted plant biosecurity context. Here, we tested samples of bees and pollen collected from pollination hives in two ASBVd orchard locations, one in Australia, where only four trees in a block were known to be infected, and a second in South Africa, where the estimated incidence of infection was 10%. Using real-time RT-PCR and HTS (total RNA-seq and small RNA-seq), we demonstrated that ASBVd can be confidently detected in bees and pollen samples from hives within 100 m of infected trees. The potential for using this approach in ASBVd surveillance for improved orchard management and supporting market access is discussed.

Social Media Communication and Network Correlates of HIV Infection and Transmission Risks Among Black Sexual Minority Men: Cross-sectional Digital Epidemiology Study.

In the United States, HIV disproportionately affects Black cisgender sexual minority men (BSMM). Although epidemiological and behavioral surveillance are integral to identifying BSMM at risk of HIV infection and transmission, overreliance on self-reported data, inability to observe social contexts, and neglect of populations with limited engagement in health care systems limits their effectiveness. Digital epidemiological approaches drawing on social media data offer an opportunity to overcome these limitations by passively observing in organic settings activities, beliefs, behaviors, and moods that indicate health risks but are otherwise challenging to capture. The primary aim of this study was to determine whether features of Facebook communication and networks were associated with biological, behavioral, and psychological indicators of HIV infection and transmission risk. Facebook and survey data were collected from BSMM aged 18 to 35 years living in Chicago (N=310). Participants' Facebook posts were characterized using 4 culturally tailored topic dictionaries related to aspects of HIV protection and risk among BSMM (sexual health; substance use; sex behavior; and ballroom culture, a salient subculture in lesbian, gay, bisexual, transgender, and queer communities of color). Social network methods were used to capture structural features of BSMM's Facebook friendships (centrality, brokerage, and local clustering) and Facebook group affiliations. Multivariable regressions revealed relationships between these Facebook features and 5 ground truth indicators of HIV infection and transmission risk (sexually transmitted infection incidence, condomless sex, sex drug use, biomedical prevention, and depression). Although analysis of participants' Facebook posts revealed that HIV-related topics occupied a small portion of the total messages posted by each participant, significant associations were found between the following HIV risk indicators and Facebook features: Condomless sex, including communication about sexual health (odds ratio [OR] 1.58, 95% CI 1.09-2.29), ballroom culture (OR 0.76, 95% CI 0.63-0.93), and friendship centrality (OR 0.69, 95% CI 0.52-0.92); Sex drug use, including communication about substance use (OR 1.81, 95% CI 1.17-2.79) and friendship centrality (OR 0.73, 95% CI 0.55-0.96) and brokerage (OR 0.71, 95% CI 0.51-0.99); Biomedical prevention, including communication about ballroom culture (OR 0.06, 95% CI 0.01-0.71); and Depression, including communication about sexual health (β=-0.72, 95% CI -1.42 to -0.02), ballroom culture (β=.80, 95% CI 0.27-1.34), friendship centrality (β=-0.90, 95% CI -1.60 to -0.21), and Facebook group affiliations (β=.84, 95% CI 0.25-1.43). Facebook features provided no significant explanatory value for sexually transmitted infection incidence. Finding innovative strategies to detect BSMM at risk of contracting or transmitting HIV is critical to eliminating HIV disparities in this community. The findings suggest that social media data enable passive observance of social and communicative contexts that would otherwise go undetected using traditional HIV surveillance methods. As such, social media data are promising complements to more traditional data sources.