scholarly journals Comparison of statistical algorithms for syndromic surveillance aberration detection

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
R. Morbey ◽  
A. Noufaily ◽  
F. D. Colón-González ◽  
A. Elliot ◽  
S. Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Sensitivity And Specificity ◽  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Outbreak Detection ◽  
Aberration Detection ◽  
Day Of The Week ◽  
Multi Level ◽  
Public Holidays

ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE's syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–2122

scholarly journals The Application of a Novel Statistical Method for Syndromic Surveillance in England

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Roger Morbey ◽  
Helen Hughes ◽  
Alex Elliot ◽  
Neville Verlander ◽  
Nick Andrews ◽  
...  
Keyword(s):  
Public Health ◽  
Statistical Method ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Local Models ◽  
Multi Level ◽  
First Time ◽  
Design And Application

This paper describes the design and application of a new statistical method for real-time syndromic surveillance, used by Public Health England. The Rising Activity, Multi-level Mixed effects, Indicator Emphasis (RAMMIE) statistical method was developed and tested alongside existing methods before being applied to a suite of syndromic surveillance in operation in England. The RAMMIE method has proved to be a reliable, effective method for generating automated alarms for syndromic surveillance. The multi-level models have enabled local models to be created for the first time across all systems and models have proved themselves to be robust across all the signals.

scholarly journals Comparison of statistical algorithms for daily syndromic surveillance aberration detection

2019 ◽  
Vol 35 (17) ◽  
pp. 3110-3118
Author(s):  
Angela Noufaily ◽  
Roger A Morbey ◽  
Felipe J Colón-González ◽  
Alex J Elliot ◽  
Gillian E Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Performance Metrics ◽  
High Specificity ◽  
Outbreak Detection ◽  
Supplementary Information ◽  
Surveillance Systems ◽  
Detection Algorithms ◽  
Aberration Detection ◽  
Wide Range

Abstract Motivation Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the ‘rising activity, multilevel mixed effects, indicator emphasis’ (RAMMIE) method and the improved quasi-Poisson regression-based method known as ‘Farrington Flexible’ both currently used at Public Health England, and the ‘Early Aberration Reporting System’ (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. Results We conclude that amongst the algorithm variants that have a high specificity (i.e. >90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2–3 days earlier. Availability and implementation R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmComparison Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Localized Surveillance: A Fresh Perspective for Regional Syndromic Surveillance

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Mansi Agarwal ◽  
Nimi Idaikkadar ◽  
José Lojo ◽  
Kristen Soto ◽  
Robert Mathes
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Syndromic Surveillance ◽  
Local Scale ◽  
Surveillance Data ◽  
Fresh Perspective

This roundtable will discuss successful syndromic surveillance data sharing efforts that have been used on a local scale for faster, more efficient, and long-term collaboration between neighboring public health jurisdictions.

scholarly journals The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England

2015 ◽  
pp. btv418 ◽  
Author(s):  
Roger A. Morbey ◽  
Alex J. Elliot ◽  
Andre Charlett ◽  
Neville Q. Verlander ◽  
Nick Andrews ◽  
...  
Keyword(s):  
Syndromic Surveillance ◽  
Mixed Effects ◽  
Multi Level

scholarly journals Correcting for day of the week and public holiday effects: improving a national daily syndromic surveillance service for detecting public health threats

2017 ◽  
Vol 17 (1) ◽  
Author(s):  
Elizabeth Buckingham-Jeffery ◽  
Roger Morbey ◽  
Thomas House ◽  
Alex J. Elliot ◽  
Sally Harcourt ◽  
...  
Keyword(s):  
Public Health ◽  
Syndromic Surveillance ◽  
Health Threats ◽  
Day Of The Week ◽  
Holiday Effects

scholarly journals Hypertension in Guatemala’s Public Primary Care System: A Needs Assessment Using the Health System Building Blocks Framework

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meredith P. Fort ◽  
William Mundo ◽  
Alejandra Paniagua-Avila ◽  
Sayra Cardona ◽  
Juan Carlos Figueroa ◽  
...  
Keyword(s):  
Public Health ◽  
Health System ◽  
Needs Assessment ◽  
Public Health System ◽  
Building Blocks ◽  
Hypertension Control ◽  
The Public ◽  
Multi Level ◽  
Health System Building Blocks

Abstract Background Uncontrolled hypertension represents a substantial and growing burden in Guatemala and other low and middle-income countries. As a part of the formative phase of an implementation research study, we conducted a needs assessment to define short- and long-term needs and opportunities for hypertension services within the public health system. Methods We conducted a multi-method, multi-level assessment of needs related to hypertension within Guatemala’s public system using the World Health Organization’s health system building blocks framework. We conducted semi-structured interviews with stakeholders at national (n = 17), departmental (n = 7), district (n = 25), and community (n = 30) levels and focus groups with patients (3) and frontline auxiliary nurses (3). We visited and captured data about infrastructure, accessibility, human resources, reporting, medications and supplies at 124 health posts and 53 health centers in five departments of Guatemala. We conducted a thematic analysis of transcribed interviews and focus group discussions supported by matrix analysis. We summarized quantitative data observed during visits to health posts and centers. Results Major challenges for hypertension service delivery included: gaps in infrastructure, insufficient staffing and high turnover, limited training, inconsistent supply of medications, lack of reporting, low prioritization of hypertension, and a low level of funding in the public health system overall. Key opportunities included: prior experience caring for patients with chronic conditions, eagerness from providers to learn, and interest from patients to be involved in managing their health. The 5 departments differ in population served per health facility, accessibility, and staffing. All but 7 health posts had basic infrastructure in place. Enalapril was available in 74% of health posts whereas hydrochlorothiazide was available in only 1 of the 124 health posts. With the exception of one department, over 90% of health posts had a blood pressure monitor. Conclusions This multi-level multi-method needs assessment using the building blocks framework highlights contextual factors in Guatemala’s public health system that have been important in informing the implementation of a hypertension control trial. Long-term needs that are not addressed within the scope of this study will be important to address to enable sustained implementation and scale-up of the hypertension control approach.

scholarly journals Seasonal patterns of dengue fever in rural Ecuador: 2009—2016 Seasonality of dengue fever in Ecuador

2018 ◽  
Author(s):  
Rachel Sippy ◽  
Diego Herrera ◽  
David Gaus ◽  
Ronald E. Gangnon ◽  
Jonathan A. Patz ◽  
...  
Keyword(s):  
Dengue Fever ◽  
Health Planning ◽  
Seasonal Patterns ◽  
Total Precipitation ◽  
First Report ◽  
School Schedules ◽  
Short And Long Term ◽  
Day Of The Week ◽  
Public Holidays

AbstractSeason is a major determinant of infectious disease rates, including arboviruses spread by mosquitoes, such as dengue, chikungunya, and Zika. Seasonal patterns of disease are driven by a combination of climatic or environmental factors, such as temperature or rainfall, and human behavioral time trends, such as school year schedules, holidays, and weekday-weekend patterns. These factors affect both disease rates and healthcare-seeking behavior. Seasonality of dengue fever has been studied in the context of climatic factors, but short- and long-term time trends are less well-understood. With 2009—2016 medical record data from patients diagnosed with dengue fever at two hospitals in rural Ecuador, we used Poisson generalized linear modeling to determine short- and long-term seasonal patterns of dengue fever, as well as the effect of day of the week and public holidays. In a subset analysis, we determined the impact of school schedules on school-aged children. With a separate model, we examined the effect of climate on diagnosis patterns. In the first model, the most important predictors of dengue fever were annual sinusoidal fluctuations in disease, long-term trends (as represented by a spline for the full study duration), day of the week, and hospital. Seasonal trends showed single peaks in case diagnoses, during mid-March. Compared to the average of all days, cases were more likely to be diagnosed on Tuesdays (risk ratio (RR): 1.26, 95% confidence interval (CI) 1.05—1.51) and Thursdays (RR: 1.25, 95% CI 1.02—1.53), and less likely to be diagnosed on Saturdays (RR: 0.81, 95% CI 0.65—1.01) and Sundays (RR: 0.74, 95% CI 0.58—0.95). Public holidays were not significant predictors of dengue fever diagnoses, except for an increase in diagnoses on the day after Christmas (RR: 2.77, 95% CI 1.46—5.24). School schedules did not impact dengue diagnoses in school-aged children. In the climate model, important climate variables included the monthly total precipitation, an interaction between total precipitation and monthly absolute minimum temperature, an interaction between total precipitation and monthly precipitation days, and a three-way interaction between minimum temperature, total precipitation, and precipitation days. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. These results can inform local disease prevention efforts, public health planning, as well as global and regional models of dengue fever trends.Author summaryDengue fever exhibits a seasonal pattern in many parts of the world, much of which has been attributed to climate and weather. However, additional factors may contribute to dengue seasonality. With 2009— 2016 medical record data from rural Ecuador, we studied the short- and long-term seasonal patterns of dengue fever, as well as the effect of school schedules and public holidays. We also examined the effect of climate on dengue. We found that dengue diagnoses peak once per year in mid-March, but that diagnoses are also affected by day of the week. Dengue was also impacted by regional climate and complex interactions between local weather variables. This is the first report of long-term dengue fever seasonality in Ecuador, one of few reports from rural patients, and one of very few studies utilizing daily disease reports. This is the first report on the impacts of school schedules, holidays, and weekday-weekend patterns on dengue diagnoses. These results suggest a potential impact of human behaviors on dengue exposure risk. More broadly, these results can inform local disease prevention efforts and public health planning, as well as global and regional models of dengue fever trends.

scholarly journals An Aberration Detection-Based Approach for Sentinel Syndromic Surveillance of COVID-19 and Other Novel Influenza-Like Illnesses

2020 ◽  
Author(s):  
Andrew Wen ◽  
Liwei Wang ◽  
Huan He ◽  
Sijia Liu ◽  
Sunyang Fu ◽  
...  
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Syndromic Surveillance ◽  
Lag Time ◽  
First Line ◽  
Aberration Detection ◽  
Symptom Prevalence ◽  
Global Concern ◽  
The Common ◽  
Second Wave

AbstractCoronavirus Disease 2019 (COVID-19) has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods are tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019-2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

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