Surveillance Methods Research Articles

Real-time genomic epidemiologic investigation of a multispecies plasmid-associated hospital outbreak of NDM-5-producing Enterobacterales infections

New Delhi metallo-β-lactamase (NDM) is an emergent mechanism of carbapenem resistance associated with high mortality and limited treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks. Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance performed weekly. To resolve NDM-encoding plasmids, we performed long-read sequencing and constructed hybrid assemblies. WGS data for suspected outbreaks was shared with the IP&C team for assessment and intervention. We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included 7 bacterial species encoding the same NDM-5 plasmid. WGS surveillance and epidemiologic investigation characterized 10 horizontal plasmid transfer events and 6 bacterial transmission events between patients in varying hospital units. Our investigation revealed a complex, multispecies outbreak of NDM involving multiple plasmid transfer and bacterial transmission events. We highlight the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks.

TEMPORARY REMOVAL: Mortalité maternelle en France, 2016–2018, fréquence, causes et profil des femmes

Objective: To describe, for the 2016-2018 period, the frequency, causes and risk factors of maternal deaths in France.Method: Data from the National Confidential Enquiry into Maternal Deaths for 2016-2018Results: For 2016-2018, 272 maternal deaths occurred in France, i.e a maternal mortality ratio of 11,8 per 100,000 live births (95% CI 10,4-13,3), and 8.5 (IC 95 % 7,4-9,8) for maternal mortality up to 42 days. Compared to women aged 20-24, the risk of maternal death is multiplied by 2.6 for women aged 35-39, by 5 for women aged 40 and over. Obese women are twice as frequent among maternal deaths (26%) than in the general population of parturients (11%). There are territorial disparities -the maternal mortality ratio in the French overseas departments is 2 times higher than in metropolitan France (significant difference but smaller than in 2013-2015)-, and social disparities -the mortality of migrant women remains higher than that of women born in France, particularly for women born in sub-Saharan Africa whose risk is 3 times higher than that native women. One in three women who died (34%) had socio-economic vulnerability versus 22% in the overall population of parturients.Among causes of maternal deaths, the predominant role of psychiatric conditions (mostly suicides) is confirmed for the period 2016-2018, leading cause of maternal mortality considered up to 1 year (17%), MMR of 1.9 /100,000 NV. i.e. approximately one death from psychiatric causes every 3 weeks. Cardiovascular diseases are the second leading cause of maternal mortality up to one year (14%) and the leading cause up to 42 days (16%), with 1.3 deaths per 100,000 NV. Amniotic embolism ranks as the third cause (8%) (2nd cause, 11%, for MM limited to 42 days), i.e. MMR of 0.9 per 100,000 NV. After a regular decline over the last decade, maternal mortality from obstetric hemorrhage is at a stable level compared to the previous triennium 2013-2015, MMR of 0.9/100,000 NV, i.e 5th cause of MM up to one year (7%) and 4th cause of MM up to 42 days.Conclusion: The overall national maternal mortality ratio does not show a downward trend, even with constant surveillance method. Territorial inequalities persist but change in their magnitude and in the regions concerned. The profile of the causes of maternal mortality up to one year of the pregnancy end shows the leading role of suicides and cardiovascular diseases, which illustrates that the health of pregnant women or those who have recently given birth is not limited to the obstetric domain, and highlights the importance of multidisciplinarity in the management and organization of care for women in this period

Surveillance for Lyme Disease After Implementation of a Revised Case Definition - United States, 2022.

Lyme disease, a tickborne zoonosis caused by certain species of Borrelia spirochetes, is the most common vectorborne disease in the United States. Approximately 90% of all cases are reported from 15 high-incidence jurisdictions in the Northeast, mid-Atlantic, and upper-Midwest regions. After the implementation of a revised surveillance case definition in 2022, high-incidence jurisdictions report cases based on laboratory evidence alone, without need for additional clinical information. In 2022, 62,551 Lyme disease cases were reported to CDC, 1.7 times the annual average of 37,118 cases reported during 2017-2019. Annual incidence increased most in older age groups, with incidence among adults aged ≥65 years approximately double that during 2017-2019. The sharp increase in reported Lyme disease cases in 2022 likely reflects changes in surveillance methods rather than change in disease risk. Although these changes improve standardization of surveillance across jurisdictions, they preclude detailed comparison with historical data.

Real-time video surveillance on highways using combination of extended Kalman Filter and deep reinforcement learning

Highways, as one of the main arteries of transit and transportation in today's world, play a fundamental role in accelerating transportation, and for this reason, continuous monitoring of them is of great importance. Among these, monitoring compliance with transportation laws by vehicles is of utmost importance; for automation, efficient and vehicle-specific models can be used. In this article, a new method for video surveillance of highways is presented using an extended Kalman filter (EKF) and reinforcement learning models. There are three primary stages to the suggested approach. During the first stage, the extended Kalman filter (EKF) is used to identify and track multiple targets. Next, in the second stage, a convolutional neural network (CNN) processes each detected moving item to determine the kind of vehicle. During this stage, the CNN model's ideal configuration is ascertained using a new optimization approach that combines Particle Swarm Optimization (PSO) and reinforcement learning. After identifying the type of vehicle, in the third phase, the proposed method uses a separate CNN model for each target vehicle to assess its compliance with transportation safety principles. It should be mentioned that each vehicle's associated CNN model is configured during this phase using the suggested optimization methodology. Investigations have been conducted into the effectiveness of the suggested method in identifying violations of road safety laws as well as how well it performed in the two phases of vehicle type identification. According to the findings, the suggested approach can identify the kind of vehicle with 98.72% accuracy, which is at least 3.41% better than the approaches that were compared. On the other hand, this model can detect the violation of road safety laws for each vehicle with an average accuracy of 91.5%, which shows at least a 3.49% improvement compared to the other methods.

Automated molecular detection of West Nile Virus in mosquito pools using the Panther Fusion system

West Nile Virus (WNV) is an arthropod-borne virus that is spread through mosquito vectors. WNV emerged in the US in 1999 and has since become endemic in the US, causing the most domestically acquired arboviral disease in the country. Mosquito surveillance for WNV is useful to monitor arboviral disease burden over time and across different locations. RT-qPCR is the preferred method for WNV surveillance, but these methods are labor-intensive. The Panther Fusion System has an Open Access feature that allows for laboratory-developed tests (LDTs) to run on a fully automated system for nucleic acid extraction, RT-qPCR, and result generation. This study demonstrates the successful optimization of a WNV multiplex LDT (assay targets: ENV and NS1 genes) for high-throughput environmental surveillance testing of mosquito pool homogenates on the Panther Fusion System. Analytical sensitivity of the assay was 186 and 744 copies/PCR reaction for the ENV and NS1 targets, respectively. To assess the performance of this assay, a total of 80 mosquito pools were tested, including 60 previously tested pools and 20 spiked negative mosquito pools. Among the 60 previously tested specimens, the Panther Fusion WNV LDT demonstrated 100% positive and negative agreement with the CDC West Nile RT-qPCR assay. The Panther Fusion WNV LDT also detected all 20 spiked specimens. The Panther Fusion WNV LDT assay was successfully developed and optimized for high throughput testing with similar performance to the previously used CDC West Nile RT-qPCR assay.

Risk adjustment-based statistical control charts with a functional covariate

ABSTRACT In real applications, there are situations in which quality characteristics are significantly affected by function-valued covariates such as electrocardiogram (ECG) signals; however, previous surveillance methods with covariate adjustment mainly use scalar-valued covariates to construct control charts to investigate the stability of nonindustrial processes, such as in the medical and public health fields. Few existing approaches address the infinite-dimensional regression encountered with function-valued covariates. Thus, an applicable scheme needs to be developed. Here, by relaxing the assumption of normality, a novel surveillance strategy for functional logistic regression (FLR) is proposed. In this approach, the response is a binary variable, and the covariate information is obtained from functional data. To address the above problems, functional principal component analysis (FPCA) is used to extend the score test to situations with functional covariates. Moreover, the score statistic is integrated into an exponentially weighted moving average (EWMA) charting scheme to detect abnormal changes in the location and scale parameters based on additional information. The simulation results show that the proposed approach is more efficient than previous approaches in detecting small to moderate shifts. Finally, the proposed charting scheme is applied to a real case study to demonstrate its utility and applicability.

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.

Active Surveillance of Powassan Virus in Massachusetts Ixodes scapularis Ticks, Comparing Detection Using a New Triplex Real-Time PCR Assay with a Luminex Vector-Borne Panel.

Powassan virus is an emerging tick-borne pathogen capable of causing severe neuroinvasive disease. As the incidence of human Powassan virus grows both in magnitude and geographical range, the development of sensitive detection methods for diagnostics and surveillance is critical. In this study, a Taqman-based triplex real-time PCR assay was developed for the simultaneous and quantitative detection of Powassan virus and Powassan virus lineage II (deer tick virus) in Ixodes scapularis ticks. An exon-exon junction internal control was built-in to allow for accurate detection of RNA quality and the failure of RNA extraction. The newly developed assay was also applied to survey deer tick virus in tick populations at 13 sites on Cape Cod and Martha's Vineyard Island in Massachusetts. The assay's performance was compared with the Luminex xMAP MultiFLEX Vector-borne Panel 2. The results suggested that the real-time PCR method was more sensitive. Powassan virus infection rates among ticks collected from these highly endemic tick areas ranged from 0.0 to 10.4%, highlighting the fine-scale geographic variations in deer tick virus presence in this region. Looking forward, our PCR assay could be adopted in other Powassan virus surveillance systems.

HIV-1 Drug Resistance Detected by Next-Generation Sequencing among ART-Naïve Individuals: A Systematic Review and Meta-Analysis.

There are an increasing number of articles focused on the prevalence and clinical impact of pretreatment HIV drug resistance (PDR) detected by Sanger sequencing (SGS). PDR may contribute to the increased likelihood of virologic failure and the emergence of new resistance mutations. As SGS is gradually replaced by next-generation sequencing (NGS), it is necessary to assess the levels of PDR using NGS in ART-naïve patients systematically. NGS can detect the viral variants (low-abundance drug-resistant HIV-1 variants (LA-DRVs)) of virus quasi-species at levels below 20% that SGS may fail to detect. NGS has the potential to optimize current HIV drug resistance surveillance methods and inform future research directions. As the NGS technique has high sensitivity, it is highly likely that the level of pretreatment resistance would be underestimated using conventional techniques. For the systematic review and meta-analysis, we searched for original studies published in PubMed, Web of Science, Scopus, and Embase before 30 March 2023 that focused exclusively on the application of NGS in the detection of HIV drug resistance. Pooled prevalence estimates were calculated using a random effects model using the 'meta' package in R (version 4.2.3). We described drug resistance detected at five thresholds (>1%, 2%, 5%, 10%, and 20% of virus quasi-species). Chi-squared tests were used to analyze differences between the overall prevalence of PDR reported by SGS and NGS. A total of 39 eligible studies were selected. The studies included a total of 15,242 ART-naïve individuals living with HIV. The prevalence of PDR was inversely correlated with the mutation detection threshold. The overall prevalence of PDR was 29.74% at the 1% threshold, 22.43% at the 2% threshold, 15.47% at the 5% threshold, 12.95% at the 10% threshold, and 11.08% at the 20% threshold. The prevalence of PDR to INSTIs was 1.22% (95%CI: 0.58-2.57), which is the lowest among the values for all antiretroviral drugs. The prevalence of LA-DRVs was 9.45%. At the 2% and 20% detection threshold, the prevalence of PDR was 22.43% and 11.08%, respectively. Resistance to PIs and INSTIs increased 5.52-fold and 7.08-fold, respectively, in those with a PDR threshold of 2% compared with those with PDR at 20%. However, resistance to NRTIs and NNRTIs increased 2.50-fold and 2.37-fold, respectively. There was a significant difference between the 2% and 5% threshold for detecting HIV drug resistance. There was no statistically significant difference between the results reported by SGS and NGS when using the 20% threshold for reporting resistance mutations. In this study, we found that next-generation sequencing facilitates a more sensitive detection of HIV-1 drug resistance than SGS. The high prevalence of PDR emphasizes the importance of baseline resistance and assessing the threshold for optimal clinical detection using NGS.

Surveillance methods and interventions implemented in American Indian and Alaska Native communities to increase child restraint device and seat belt use in motor vehicles: a systematic review

BackgroundAmerican Indian/Alaska Native (AI/AN) children are disproportionately affected by injuries and deaths related to motor vehicle crashes. We aimed to synthesise published evidence on surveillance methods and interventions implemented in...

Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking

Background Web search data have proven to bea valuable early indicator of COVID-19 outbreaks. However, the influence of co-morbid conditions with similar symptoms and the effect of media coverage on symptom-related searches are often overlooked, leading to potential inaccuracies in COVID-19 simulations. Method This study introduces a machine learning-based approach to estimate the magnitude of the impact of media coverage and comorbid conditions with similar symptoms on online symptom searches, based on two scenarios with quantile levels 10–90 and 25–75. An incremental batch learning RNN-LSTM model was then developed for the COVID-19 simulation in Australia and New Zealand, allowing the model to dynamically simulate different infection rates and transmissibility of SARS-CoV-2 variants. Result The COVID-19 infected person-directed symptom searches were found to account for only a small proportion of the total search volume (on average 33.68% in Australia vs. 36.89% in New Zealand) compared to searches influenced by media coverage and comorbid conditions (on average 44.88% in Australia vs. 50.94% in New Zealand). The proposed method, which incorporates estimated symptom component ratios into the RNN-LSTM embedding model, significantly improved COVID-19 simulation performance. Conclusion Media coverage and comorbid conditions with similar symptoms dominate the total number of online symptom searches, suggesting that direct use of online symptom search data in COVID-19 simulations may overestimate COVID-19 infections. Our approach provides new insights into the accurate estimation of COVID-19 infections using online symptom searches, thereby assisting governments in developing complementary methods for public health surveillance.

Doppler echocardiography for surveillance of acute cardiac allograft rejection: a 28-year single-center experience.

Endomyocardial biopsies (EMB) are recommended for the detection of acute cardiac rejection (ACR) despite limited sensitivity. We report the long-term post-transplant results of Doppler echocardiography as a noninvasive alternative of routine EMB. Two cohorts of heart transplantation (HT) recipients were chronologically defined as follows: the Dual Monitoring Cohort (DMC) from January 1990 to December 1997 included patients who underwent routine EMB and Doppler echocardiography within 24 hours for ACR surveillance; and the "Echo-First Cohort" (EFC), including patients transplanted from January 1998 to December 2018 with Doppler echocardiography as first-line approach for ACR surveillance. Echocardiographic measurements of interest were collected: early diastolic (E) wave peak velocity; pressure half time (PHT) and isovolumetric relaxation time (IVRT). Post-transplant outcomes were reviewed and the Kaplan-Meier approach was used for survival estimates. Inter-operator variability for ultrasound measurements was investigated. Data were collected from medical records from January 2019 to December 2020. A total of 228 patients were included, 99 patients in the DMC and 129 in the EFC. Overall, 5-, 10- and 15-year survival rates were 65.4%, 55.5% and 44.1% respectively, without any significant difference between the two cohorts (log rank test, P=0.71). Echocardiography variables and EMB findings were associated with a mean area under the receiver operating characteristic curve (AUC-ROC) of 0.73 [95% confidence interval (CI): 0.54-0.91], 0.74 (95% CI: 0.54-0.94) and 0.75 (95% CI: 0.57-0.94) respectively for E wave, PHT and IVRT. IVRT and PHT were significantly decreased, and E wave significantly increased, in case of histologically proven ACR. Inter-operator variability was not significant for E wave and IVRT measurements (P=0.13 and 0.30 respectively). Doppler echocardiography as a first-line method for surveillance of ACR did not impair long-term results after HT. These findings suggest that this non-invasive approach might be a reasonable alternative to systematic EMB, limiting risk and improving the quality of life.

Abstract WMP54: Detection of Late Complications After Stroke in Younger versus Older Adults With Ischemic Stroke Using Active Surveillance versus Usual Care: A Retrospective Cohort Study

Introduction: Late complications after stroke (LCAS) impede return to pre-stroke responsibilities and worsen quality of life, yet they can be readily treated. We previously demonstrated that an active surveillance approach could improve LCAS detection in young adults. However, it is not known if a similar strategy would be useful for older adults. We assessed differences in LCAS detection and resultant clinical practice changes (CPC) among young and older stroke survivors using both active surveillance and usual care approaches. Methods: We retrospectively collected data from the EHR at Tufts Medical Center for patients age ≥ 18 with acute ischemic stroke (AIS) and Stroke Clinic evaluations including demographics, comorbidities, presence of LCAS (including central pain, cognitive impairment, dystonia, headache, insomnia, lethargy, mood disorders, orthostasis, seizures, and spasticity), and evidence of CPC (diagnostic test, prescription, or referral). Differences in LCAS detection and in clinical practice change between age groups were compared during three-time intervals (0-3 months, 3-12 months, 0-12 months), and stratified by surveillance strategy. Descriptive statistics and multivariable logistic regression were used to identify predictors of LCAS detection and CPC. Results: Three hundred eleven AIS survivors (16% young, 44% female, 42% non-white) were included, with 43% undergoing active surveillance. Older adults were more likely to have a non-English preferred language. While LCAS were detected more frequently in young adults (86% vs 65%, p=0.003), the overall proportion was high in both age groups. Motor LCAS were detected at similar proportions (20% vs 12%, p=0.15), but nonmotor LCAS were detected more in young adults (91% vs 71%, p=0.003). Among all AIS survivors, active surveillance was associated with higher proportion of LCAS detection for nonmotor symptoms but not for motor symptoms. In analyses adjusted for surveillance method and age, active surveillance was strongly associated with any LCAS detection and with CPC for any LCAS and nonmotor LCAS. Conclusions: Active surveillance increases LCAS detection in both young and older AIS survivors compared to usual care, and it is a major determinant of CPC for nonmotor LCAS.

Birth defects surveillance: Working together to advance science

AbstractSince 2000, the National Birth Defects Prevention Network (NBDPN), in collaboration with the US National Center on Birth Defects and Developmental Disabilities at the Centers for Disease Control and Prevention, has provided a special issue of Birth Defects Research focused on advancing the field of birth defects surveillance, epidemiology, and public health practice. The annual NBDPN special issue consists of contributed manuscripts using birth defects surveillance data for epidemiologic research or for improving birth defects surveillance methods. This year, our special issue includes updated national prevalence estimates for selected major birth defects, seven original research papers, and an invited editorial. Several of the accepted manuscripts highlight collaborative efforts between birth defects surveillance systems across the country: through the NBDPN, the National Birth Defects Prevention Study, and the Muscular Dystrophy Surveillance, Tracking, and Research Network. These studies offer examples of the power of pooling data and leveraging resources to conduct high‐quality birth defects research.

Healthcare-associated infections: prospective rotational surveillance data of a training and research hospital

Aims: Monitoring and prevention of healthcare-associated infections (HAI) are considered to be one of the key measures to improve the effectiveness and quality of healthcare services. This study aimed to ascertain the prevalence of HAI in various hospital departments, identify the causative bacterial profile, risk factors, and the association with mortality. Methods: This prospective study included 3117 patients who were monitored in various departments of a Training and Research Hospital. The identified HAI cases were monitored using an active, prospective, rotational surveillance method. Patient data on HAI was daily recorded on pre-established tracking forms. Results: The mean hospital stay of patients was 9.9±7.5 days. The HAI prevalence was 4.5% and the HAI rate was 5.5%. The HAI rate showed no difference between internal medicine and surgical departments (5.7% vs 5.5%, p>0.05), but it was higher in intensive care units (ICU) (p<0.001). The majority of the isolated agents (65.2%) were gram-negative bacteria. Advanced age, intrinsic risk factors such as malignancy, and invasive procedures (use of central, peripheral, and urinary catheters) were associated to the development of HAI. The frequency of HAI was higher in deceased patients compared to survivors (25.4% vs 4.1%, p<0.001). Conclusion: HAI remains a major concern in hospital settings, particularly in ICU, and strongly correlates with intrinsic risk factors and invasive procedures. Optimized infection control measures with these risk factors can make a significant contribution to improving patient outcomes.

Differential Diagnoses and Management Approaches for Gastric Polyposis

Multiple gastric polyps are observed in various polyposis syndromes and conditions associated with polypoid lesion development in the stomach. Polyposis syndromes often occur concurrently with specific malignant tumors and can manifest at any point in an individual’s lifespan, thus explaining the diversity in surveillance methods. Furthermore, genetic counseling and surveillance are essential not only for the patients themselves but also for their blood relatives. Therefore, the accurate diagnosis and appropriate surveillance of multiple gastric polyps are crucial for improving patient outcomes. This review aims to provide essential information on such lesions along with representative endoscopic images of familial adenomatous polyposis, Peutz-Jeghers syndrome, Cowden syndrome, Cronkhite-Canada syndrome, juvenile polyposis syndrome, gastric adenocarcinoma and proximal polyposis of the stomach, neuroendocrine tumors in autoimmune gastritis, proton pump inhibitor-related gastric mucosal changes, and multiple submucosal heterotopic glands. We wish for this review to serve as a valuable resource for endoscopists seeking to deepen their comprehension of gastric polyposis.

Digital identification and pattern recognition capabilities using machine learning methods, navigation systems, and video surveillance

The objects of the study are unmanned vehicles and branches of the bridge of the city of Kyiv (Ukraine), which connects the Great Ring Road, Zhytomyr Highway and Peremogy Avenue. The built routes were analyzed using the technology of recognition of road signs, people and vehicles. The important problem of this research is to analyze the possibilities of detecting obstacles by an unmanned vehicle using pattern recognition, which combines the methods of machine communication, navigation and real-time video surveillance. Based on the study, the results of detecting and avoiding obstacles on the road, where a study was conducted to investigate the main reasons that can cause time delays (traffic jams, weather conditions, accidents). The results of planning and navigation are obtained to determine the appropriate road route, which allows detecting and eliminating obstacles on the road, as well as building a map plan of the route in advance using online map services (Google Maps). It is shown that recognition of road signs (based on the classification using a road sign map consisting of 7 categories), people and vehicles minimizes the occurrence of road accidents, traffic jams and time delays. To recognize the images of road signs, people and vehicles, we studied the road sections connecting to the branched bridge. Thus, the authors have reviewed and analyzed the digital capabilities of pattern identification and recognition using machine learning methods, navigation and video surveillance systems, where the safety of vehicles with detection of road signs and obstacles on the way is of great importance. The results obtained can complement the possibilities of using unmanned vehicles to avoid obstacles and road accidents based on a trained pattern recognition system. This system, using convolutional neural networks and video surveillance navigation systems, will be able to provide the driver and the people around it with safe driving conditions.

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.

Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts. This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods. A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers. Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses. The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

BackgroundDengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.MethodsTo evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document}, Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.ResultsIn the simulation study, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${G}_{i}^{*}$$\\end{document} and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.ConclusionsBayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

Assessing Different Chronic Wasting Disease Training Aids for Use with Detection Dogs.

Chronic wasting disease (CWD) is a highly infectious, fatal prion disease that affects cervid species. One promising method for CWD surveillance is the use of detection dog-handler teams wherein dogs are trained on the volatile organic compound signature of CWD fecal matter. However, using fecal matter from CWD-positive deer poses a biohazard risk; CWD prions can bind to soil particles and remain infectious in contaminated areas for extended periods of time, and it is very difficult to decontaminate the affected areas. One solution is to use noninfectious training aids that can replicate the odor of fecal matter from CWD-positive and CWD-negative deer and are safe to use in the environment. Trained CWD detection dogs' sensitivity and specificity for different training aid materials (cotton, GetXent tubes, and polydimethylsiloxane, or PDMS) incubated with fecal matter from CWD-positive and CWD-negative deer at two different temperatures (21 °C and 37 °C) for three different lengths of time (6 h, 24 h, and 48 h) were evaluated. Cotton incubated at 21 °C for 24 h was identified as the best aid for CWD based on the dogs' performance and practical needs for training aid creation. Implications for CWD detection training and for training aid selection in general are discussed.