Comparison of statistical algorithms for syndromic surveillance aberration detection

Syndromic 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.

January 21, 2018

Updating syndromic surveillance baselines following public health interventions

Public Health England's syndromic surveillance service monitor presentations for gastrointestinal illness to detect increases in health care seeking behaviour driven by infectious gastrointestinal disease. We use regression models to create baselines for expected activity and then identify any periods of signficant increases. The introduction of a rotavirus vaccine in England during July 2013 (Bawa, Z. et al. 2015) led to a reduction in incidence of the disease, requiring a readjustment of baselines.


January 21, 2018

Management tool to guide rabies elimination programmes

Global targets for elimination of human rabies mediated by dogs have been set for 2030. In the Americas countries are progressing towards interruption of transmission and declaration of rabies freedom1. Guidance for managing elimination programmes to ensure continued progress during the endgame is critical, yet often limited and lacking in specific recommendations. Characteristic spatiotemporal incidence patterns are indicative of progress, and through their identification, tailored guidance can be provided. 


July 12, 2017

The importance of age-specific data in routine syndromic surveillance

When monitoring public health incidents using syndromic surveillance systems, Public Health England (PHE) uses the age of the presenting patient as a key indicator to further assess the severity, impact of the incident, and to provide intelligence on the likely cause. However the age distribution of cases is usually not considered until after unusual activity has been identified in the allages population data. We assessed whether monitoring specific age groups contemporaneously could improve the timeliness, specificity and sensitivity of public health surveillance.

August 20, 2017

Identification of Sufferers of Rare Diseases Using Medical Claims Data

Patients who suffer from rare diseases can be hard to diagnose for prolonged periods of time. In the process, they are often subjected to tentative treatments for ailments they do not have, risking an escalation of their actual condition and side effects from therapies they do not need. An early and accurate detection of these cases would enable follow-ups for precise diagnoses, mitigating the costs of unnecessary care and improving patients’ outcomes. 


July 06, 2017

Beyond aberration detection, coping with multiple exceedances in a national syndromic surveillance service

Public Health England uses data from four national syndromic surveillance systems to support public health programmes and identify unusual activity. Each system monitors a wide range of respiratory, gastrointestinal and other syndromes at a local, regional and national level. As a result, over 12,000 ‘signals’ (combining syndrome and geography) need to be assessed each day to identify aberrations. In this webinar I will describe how the ‘big data’ collected daily are translated into useful information for public health surveillance.

March 15, 2017

Equine Syndromic Surveillance in Colorado Using Veterinary Laboratory Test Order Data

The Risk Identification Unit (RIU) of the US Dept. of Agriculture’s Center for Epidemiology and Animal Health (CEAH) conducts weekly surveillance of national livestock health data and routine coordination with agricultural stakeholders. In an initiative to increase the monitored species, health issues, and data sources, CEAH epidemiologists are building a surveillance system based on weekly counts of laboratory test orders along with Colorado State Univ. laboratorians and statistical analysts from the Johns Hopkins Univ. Applied Physics Lab.

August 31, 2017

Surveillance for Mass Gatherings: Super Bowl XLIX in Maricopa County, Arizona, 2015

Super Bowl XLIX took place on February 1st, 2015 in Glendale, Arizona. In preparation for this large scale public event and related activities, the Maricopa County Department of Public Health (MCDPH) developed methods for enhanced surveillance, situational awareness and early detection of public health emergencies.


September 18, 2017

Aberration Detection in Public Health Surveillance using the R package surveillance

Presented September 21, 2015.

This session is about the surveillance R package as a tool for performing prospective outbreak detection in routine collected surveillance data. Michael Höhle will discuss the data structure, invocation of implemented outbreak detection methods as well as visualization. Finally, he shall give a small outlook to handling reporting delays (nowcasting & delay-adjusted outbreak detection). Focus of the session will be on problems and R code and, hence, less on the statistical methods.

September 21, 2017

Detecting Outbreaks in Time-Series Data with RecentMax

We implemented the CDC EARS algorithms in our DADAR (Data Analysis, Detection, and Response) situational awareness platform. We encountered some skepticism among some of our partners about the efficacy of these algorithms for more than the simplest tracking of seasonal flu.

October 03, 2017


Contact Us

NSSP Community of Practice



This website is supported by Cooperative Agreement # 6NU38OT000297-02-01 Strengthening Public Health Systems and Services through National Partnerships to Improve and Protect the Nation's Health between the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. CDC is not responsible for Section 508 compliance (accessibility) on private websites.

Site created by Fusani Applications