UNASSIGNED: The use of existing data to provide surveillance intelligence is widely advocated but often presents considerable challenges. Two data sources could be used as proxies for the mortality experienced by the Scottish cattle population: deaths recorded in the mandatory register [Cattle Tracing System (CTS)] and fallen stock collections by the National Fallen Stock Company (NSFCo) with a nationwide voluntary membership.
UNASSIGNED: Data for the period 2011-2016 were described and compared to establish their strengths and limitations. Similarities and differences in their temporal, seasonal and spatial patterns were examined overall, at postcode area level and for different age groups. Temporal aberration detection algorithms (TADA) were fitted.
UNASSIGNED: Broadly, similar patterns were observed in the two datasets; however, there were some notable differences. The observed seasonal, annual and spatial patterns match expectations, given knowledge of Scottish cattle production systems. The registry data provide more comprehensive coverage of all areas of Scotland, while collections data provide a more comprehensive measure of the mortality experienced in 0-1-month-old calves.
UNASSIGNED: Consequently, estimates of early calf mortality and their impact on the livestock sector made using CTS, or successor registers, will be under-estimates. This may apply to other registry-based systems. Fitted TADA detected points of deviations from expected norms some of which coincided in the two datasets; one with a known external event that caused increased mortality. We have demonstrated that both data sources do have the potential to be utilized to provide measures of mortality in the Scottish cattle population that could inform surveillance activities. While neither is perfect, they are complementary. Each has strengths and weaknesses, so ideally, a system where they are analyzed and interpreted in parallel would optimize the information obtained for surveillance purposes for epidemiologists, risk managers, animal health policy-makers and the wider livestock industry sector. This study provides a foundation on which to build an operational system. Further development will require improvements in the timeliness of data availability and further investment of resources.

BACKGROUND: Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.
OBJECTIVE: This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.
METHODS: Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO2) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient\'s unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.
RESULTS: A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.
CONCLUSIONS: In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.

OBJECTIVE: This study aims to determine the alarm thresholds in influenza outbreaks and aberration detection in the influenza trend in Iran by using cumulative sum control chart (CUSUM) and period regression.
METHODS: We used the weekly reported influenza-positive (types A and B) cases from Iran between January 2015 and November 2019. The period regression model and CUSUM chart were used as detection algorithms to figure out the alarm thresholds.
RESULTS: The mean ± SD and the median (95% CI) of the determined threshold per week were 34.85 ± 15.29 and 28.30 (17.67-64.62). According to the period regression, there were nine epidemic periods of influenza from 2015 to 2019. By using the CUSUM and considering a different h (h is an appropriate value that leads to the desired estimation for upper control limit) for the calculation of the upper control limit, 88, 84, 73 and 67 weeks were determined as the epidemic period.
CONCLUSIONS: According to the current study, the incidence of influenza showed a cyclic pattern and the epidemic recurred each year. Understanding this cyclical pattern can help health policymakers launch prevention programs such as vaccination during certain months of the year.
UNASSIGNED: تهدف هذه الدراسة لتحديد الحدود المقلقة لتفشي الإنفلونزا وكشف الانحرافات في اتجاه الإنفلونزا في إيران باستخدام المبلغ التراكمي وانحدار المرحلة.
UNASSIGNED: قمنا باستخدام حالات الإصابة بفيروس الإنفلونزا (النوع أ، وب) المبلغ عنها أسبوعيا في إيران خلال الفترة من يناير ٢٠١٥ إلى نوفمبر ٢٠١٨. تم استخدام نموذج انحدار الفترة، وجدول المجموع التراكمي للكشف الحسابي لمعرفة الحدود المقلقة.
UNASSIGNED: كان المتوسط ± الانحراف المعياري والوسيط (فاصل الثقة ٩٥٪) من البداية المحددة في الأسبوع ٣٤.٨٥± ١٥.٢٩و ٢٨.٣(١٧.٦٧-٦٤.٦٢). تبعا لانحدار الفترة، وكان هناك تسع فترات وبائية للإنفلونزا من ٢٠١٥-٢٠١٩. باستخدام المجموع التراكمي ومراعاة مختلف القيم المناسبة التي تؤدي إلى التقدير المطلوب للحد الأعلى للتحكم، وتم تحديد الأسابيع ٦٧، و٧٣، و٨٤، و٨٨ كفترات وبائية.
UNASSIGNED: وفقا للدراسة الحالية، أظهرت الإصابة بالإنفلونزا نمطا دوريا وحدوث الوباء كل عام. يمكن أن يساعد فهم هذا النمط الدوري صانعي السياسات الصحية لإطلاق برامج الوقاية مثل التطعيم في أشهر معينة من العام.

The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms.
Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework.
We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review.
The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms.
The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.

This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain.

Early detection of infectious disease outbreaks is one of the important and significant issues in syndromic surveillance systems. It helps to provide a rapid epidemiological response and reduce morbidity and mortality. In order to upgrade the current system at the Korea Centers for Disease Control and Prevention (KCDC), a comparative study of state-of-the-art techniques is required. We compared four different temporal outbreak detection algorithms: the CUmulative SUM (CUSUM), the Early Aberration Reporting System (EARS), the autoregressive integrated moving average (ARIMA), and the Holt-Winters algorithm. The comparison was performed based on not only 42 different time series generated taking into account trends, seasonality, and randomly occurring outbreaks, but also real-world daily and weekly data related to diarrhea infection. The algorithms were evaluated using different metrics. These were namely, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, symmetric mean absolute percent error (sMAPE), root-mean-square error (RMSE), and mean absolute deviation (MAD). Although the comparison results showed better performance for the EARS C3 method with respect to the other algorithms, despite the characteristics of the underlying time series data, Holt⁻Winters showed better performance when the baseline frequency and the dispersion parameter values were both less than 1.5 and 2, respectively.

To compare the performance of the standard Historical Limits Method (HLM), with a modified HLM (MHLM), the Farrington-like Method (FLM), and the Serfling-like Method (SLM) in detecting simulated outbreak signals. We used weekly time series data from 12 infectious diseases from the U.S. Centers for Disease Control and Prevention\'s National Notifiable Diseases Surveillance System (NNDSS). Data from 2006 to 2010 were used as baseline and from 2011 to 2014 were used to test the four detection methods. MHLM outperformed HLM in terms of background alert rate, sensitivity, and alerting delay. On average, SLM and FLM had higher sensitivity than MHLM. Among the four methods, the FLM had the highest sensitivity and lowest background alert rate and alerting delay. Revising or replacing the standard HLM may improve the performance of aberration detection for NNDSS standard weekly reports.

BACKGROUND: We describe a veterinary syndromic surveillance system developed in Sweden based on laboratory test requests.
METHODS: The system is a desktop application built using free software.
RESULTS: Development took 1 year. During the first year of operation, utility was demonstrated by the detection of statistically significant increases in the number of laboratory submissions. The number of false alarms was considered satisfactory in order to achieve the desired sensitivity.
CONCLUSIONS: Besides the demonstrated benefit for disease surveillance, the system contributed to improving data quality and communication between the diagnostic departments and the epidemiology department.

National syndromic surveillance systems require optimal anomaly detection methods. For method performance comparison, we injected multi-day signals stochastically drawn from lognormal distributions into time series of aggregated daily visit counts from the U.S. Centers for Disease Control and Prevention\'s BioSense syndromic surveillance system. The time series corresponded to three different syndrome groups: rash, upper respiratory infection, and gastrointestinal illness. We included a sample of facilities with data reported every day and with median daily syndromic counts ⩾1 over the entire study period. We compared anomaly detection methods of five control chart adaptations, a linear regression model and a Poisson regression model. We assessed sensitivity and timeliness of these methods for detection of multi-day signals. At a daily background alert rate of 1% and 2%, the sensitivities and timeliness ranged from 24 to 77% and 3.3 to 6.1days, respectively. The overall sensitivity and timeliness increased substantially after stratification by weekday versus weekend and holiday. Adjusting the baseline syndromic count by the total number of facility visits gave consistently improved sensitivity and timeliness without stratification, but it provided better performance when combined with stratification. The daily syndrome/total-visit proportion method did not improve the performance. In general, alerting based on linear regression outperformed control chart based methods. A Poisson regression model obtained the best sensitivity in the series with high-count data.