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Outbreak Detection

The evolution of novel influenza viruses in humans is a bio- logical phenomenon that can not be stopped. All existing data suggest that vaccination against the morbidity and mortality of the novel influenza viruses is our best line of defence. Unfortunately, vaccination requires that the infectious agent to be quickly identified and a safe vaccine in large quantities is produced and administered. As was witnessed with the 2009 H1N1 influenza pandemic, these steps took a frustratingly long period during which the novel influenza virus continued its unstoppable and rapid global spreading. In addition to the different vaccination strategies ( e.g. random mass vaccination, age structured vaccination), isolation and quarantining of infected individuals is another effective method used by the public health agencies to control the spreading of infectious diseases. Isolation is effective against any infectious disease, however it can be very hard to detect infectious individuals in the population when: 1. Symptoms are ambiguous or easily misdiagnosed ( e.g. 2009 H1N1 influenza outbreak shared many symptoms with many other influenza like illnesses) 2. When the symptoms emerge after the individual become infectious.

Objective

The purpose of our work is to develop a system for automatic contact tracing with the goal of identifying individuals who are most likely infected, even if we do not have direct diagnostic information on their health status. Control of the spread of respiratory pathogens (e.g. novel influenza viruses) in the population using vaccination is a challenging problem that requires quick identification of the infectious agent followed by large-scale production and administration of a vaccine. This takes a significant amount of time. A complementary approach to control transmission is contact tracing and quarantining, which are currently applied to sexually transmitted diseases (STDs). For STDs, identifying the contacts that might have led to disease transmission is relatively easy; however, for respiratory pathogens, the contacts that can lead to transmission include a huge number of face-to-face daily social interactions that are impossible to trace manually.

 



 

Submitted by Magou on

There has been much interest in the use of statistical surveillance systems over the last decade, prompted by concerns over bio-terrorism, the emergence of new pathogens such as SARS and swine flu, and the persistent public health problems of infectious disease outbreaks. In the United Kingdom (UK), statistical surveillance methods have been in routine use at the Health Protection Agency (HPA) since the early 1990s and at Health Protection Scotland (HPS) since the early 2000s (1,2). These are based on a simple yet robust quasi-Poisson regression method (1). We revisit the algorithm with a view to improving its performance.

Objective

To improve the performance of the England and Wales large scale multiple statistical surveillance system for infectious disease outbreaks with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks.

 

Submitted by Magou on

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. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.

Objective:

To investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.

Submitted by elamb on

On October 2016, the Indian Ocean Regional Health Agency was alerted about an increase in ED visits related to adverse reactions associated with use of SC on Mayotte Island. In this context, an investigation based on a syndromic surveillance system was implemented by the regional unit of the French national public health agency.

Objective:

To confirm and to characterize the increase in emergency department (ED) visits related to the use of synthetic cannabinoids (SC).

Submitted by elamb on

An estimated one in six Americans experience illness from the consumption of contaminated food (foodborne illness) annually; most are neither diagnosed nor reported to health departments1. Eating food prepared outside of the home is an established risk factor for foodborne illness2. New York City (NYC) has approximately 24,000 restaurants and >8.5 million residents, of whom 78% report eating food prepared outside of the home at least once per week3. Residents and visitors can report incidents of restaurant-associated foodborne illness to a citywide non-emergency information service, 311. In 2012, the NYC Department of Health and Mental Hygiene (DOHMH) began collaborating with Columbia University to improve the detection of restaurant-associated foodborne illness complaints using a machine learning algorithm and a daily feed of Yelp reviews to identify reports of foodborne illness4. Annually, DOHMH manages over 4,000 restaurant-associated foodborne illness reports received via 311 and identified on Yelp which lead to the detection of about 30 outbreaks associated with a restaurant in NYC. Given the small number of foodborne illness outbreaks identified, it is probable that many restaurant-associated foodborne illness incidents remain unreported. DOHMH sought to incorporate and evaluate an additional data source, Twitter, to enhance foodborne illness complaint and outbreak detection efforts in NYC.

Objective:

To incorporate data from Twitter into the New York City Department of Health and Mental Hygiene foodborne illness surveillance system and evaluate its utility and impact on foodborne illness complaint and outbreak detection.

Submitted by elamb on

Measles is a vaccine preventable, highly transmissible viral infection that affects mostly under-five year children. The disease is caused by a Morbillivirus; member of the Paramyxovirus family.

Objective:

We reviewed measles specific Integretaged Disease Surveillance and Response (IDSR) data from Nigeria over a five-year period to highlights its burden and trends, and make recommendations for improvements.

Submitted by elamb on

Outbreaks of waterborne gastrointestinal disease occur routinely in North America, resulting in considerable morbidity, mortality, and cost (Hrudey, Payment et al. 2003). Outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. We seek to develop a framework for both detection and classification of outbreaks using information in both space and time. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection.

Objective

To develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of Montreal, with the goal of improving early outbreak detection using simulated surveillance data.

Submitted by rmathes on

Salmonellosis is the zoonotic disease caused by Salmonella bacteria. These are food-borne pathogens, which require improvement of diagnostics and surveillance measures. Prior to implementation of a PCR-based system for monitoring Salmonella, presence and differentiation of the agent was validated under Office International Epizootical (O.I.E.) requirements.

Objective

This study aimed to perform interlaboratory testing and clarification of the PCR-based test for its implementation in Ukraine.

Submitted by teresa.hamby@d… on

Social media is of considerable interest as a sensor into the thoughts, interests and health of a population. We consider three types of health events that an analyst may wish to be made aware of:

- Given a known disease, such as MERS, SARS, Measles, etc., an event corresponds to individuals contracting the disease.

- Given a set of symptoms (fever, stomach pain, etc.), an event is an unusual number of individuals1 complaining of the symptoms.

- Most generally: an event is an unusually large group of individuals who can be identified as being effected by some personal illness.

Note that to detect an “unusual number” of something, we need to count the indicators of the event, and we need to compare the current count with past counts. Further, we are generally interested in geographically constrained events, and so for this work we will focus on county-based counts. We will count the number of items (tweets or individuals) expressing the event indicator (a disease name, symptom, or classified as “personal health related” as indicated by our classifier). Our approach to detecting health related events is: filter -> classify -> detect. We first filter out tweets that contain no “health related” terms, then apply a classifier to each tweet. This classifier is designed to flag a tweet as being about “personal health” or not. We then aggregate the positive instances per day at the county level and detect as an event any county/day pair with an unusually high count (as compared to the recent past).

Objective

In this work we investigate the extent to which social media, in particular Twitter, can be used to detect an outbreak of a disease or illness. We term these outbreaks “events”, and we will describe methodologies for detecting events.

Submitted by teresa.hamby@d… on

Zanzibar is comprised primarily of two large islands with a population of 1.3 million. Indoor Residual Spraying (IRS) campaigns, distribution of long-lasting insecticide treated bed nets (LLINs), ensuring treatment medication is available, and use of Rapid Diagnostic Tests (RDTs) have reduced Malaria prevalence from 39% in 2005[1] to less than 1% in 2011-2012. This is the third time Zanzibar has been close to eliminating malaria, but there are serious challenges. These include vector resistance to pyrethroids, the shortlived efficacy of LLINs, and resistance to behavior change. Constant traffic with mainland Tanzania and foreign countries also poses the risk of outbreaks. An effective and sustained surveillance and rapid response system is essential to control outbreaks and optimize interventions.

Objective

This presentation aims to share the results of a six-year effort to use mobile health (mHealth) technology to help eliminate malaria from a well-defined geographic area. This presentation will review the history, technology, results, lessons-learned, and applicability to other contexts.

Submitted by uysz on
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