Skip to main content

Disease Outbreak

Description

Management policies for influenza outbreaks balance the expected morbidity and mortality costs versus the cost of intervention policies under outbreak parameter uncertainty. Previous approaches have not updated parameter estimates as data arrives or have had a limited set of possible intervention policies. We present a methodology for dynamic determination of optimal policies in a stochastic compartmental model with sequentially updated parameter uncertainty that searches the full set of sequential control strategies.

Objective

This abstract highlights a methodology to build optimal management policy maps for use in influenza outbreaks in small populations.

Submitted by uysz on
Description

Our laboratory previously established the value of over-the-counter (OTC) sales data for the early detection of disease outbreaks. We found that thermometer sales (TS) increased significantly and early during influenza (flu) season. Recently, the 2009 H1N1 outbreak has highlighted the need for developing methods that not only detect an outbreak but also estimate incidence so that public-health decision makers can allocate appropriate resources in response to an outbreak. Although a few studies have tried to estimate the H1N1 incidence in the 2009 outbreak, these were done months afterward and were based on data that are either not easy to collect or not available in a timely fashion (for example, surveys or confirmed laboratory cases).

Here, we explore the hypothesis that OTC sales data can also be used for predicting a disease activity. Towards that end, we developed a model to predict the number of Emergency Departments (ED) flu cases in a region based on TS. We obtain sales information from the National Retail Data Monitor (NRDM) project. NRDM collects daily sales data of 18 OTC categories across the US.

 

Objective

We developed a model that predicts the incidence of flu cases that present to ED in a given region based on TS.

Submitted by hparton on
Description

Disease outbreak detection based on traditional surveillance datasets, such as disease cases reported from hospitals, is potentially limited in that the collection of clinic information is costly and time consuming. However, social media provides the vast amount of data available in real time on the internet at almost no cost. Our solution, NPHGS, provides a nonparametric statistical approach for outbreak detection that well addresses the key technical challenges in social media streams.

Objective

We present a new method for disease outbreak detection, the 'Non-Parametric Heterogeneous Graph Scan (NPHGS)'. NPHGS enables fast and accurate detection of emerging space-time clusters using Twitter and other social media streams where standard parametric model assumptions are incorrect.

Submitted by knowledge_repo… on

In WA, we've been using a series of increasingly broad queries to monitor measles. The number of visits mentioning measles increases during an outbreak as a result of people seeking care because they were (or think) they were exposed, seeking titers, vaccinations, or having seen reports of measles on the news and concerns than an illness could be measles. As a result, it is important to focus in on visits of highest suspicion as mentions of measles increase.

Submitted by Anonymous on
Description

National telephone health advice service data have been investigated as a source for syndromic surveillance of influenza-like illness and gastroenteritis . Providing a high level of coverage, the system might serve as an early outbreak detection tool. We have previously found that telephone triage service data of acute gastroenteritis was superior to web queries as well as over-the-counter pharmacy sales of anti-diarrhea medication to detect large water- and foodborne outbreaks of gastrointestinal illness in Sweden during the years 2007–2011 (4). However, information is limited regarding the usefulness, characteristics, and signal properties of local telephone triage data for monitoring and identifying outbreaks at the community level.

Objective

Our aim was to use telephone triage data to develop a model for community-level syndromic surveillance that can detect local outbreaks of acute gastroenteritis (AGE) and influenza-like illness (ILI) and allow targeted local disease control information.

Submitted by knowledge_repo… on
Description

Following the heat wave that scorched France in August 2003 a national daily gathering of mortality data was decided in link with the National Institute for Statistics and Economic Studies (Insee). Such gathering is based on Public Records Office equipped with the appropriate software in order to transmit their data to Insee. Then data received daily are transmitted automatically to the National Institut for Health Surveillance. Data are encrypted and transmitted 7 days per week through direct FTP in a pretermined format. For each death certificate, the following information are recorded: zip code, age, sex, date of death.

A pilot test started in June, 2004 with 147 cities for one year. The good evaluation of the system pushed to enlarge it to all eligible cities in France. The enlarged system started on November 1, 2005 and concerned 1,152 Public Records Office which represents around 75% of the daily French mortality.

Reunion Island (population 770,000) is being affected by the most important outbreak of chikungunya disease ever described in the medical literature. Between March 1, 2005 and May 30, 2006, an estimated 255,000 cases have been reported in this French territory located in the Indian Ocean. The vast majority of the cases have been occurring from mid-December, with a peak of 45000 cases week 5, 2006.

The disease is a self-limiting febrile viral disease characterised by arthralgia or arthritis. The symptoms may last for several months but recovery was, until now, considered universal.

 

Objective

This paper describes the on going surveillance of mortality during the largest outbreak of chikungunya ever known. It is based on a new automatic gathering of mortality data and it is also the first opportunity to test this system in real condition.

Submitted by elamb on
Description

We developed a probabilistic model of how clinicians are expected to detect a disease outbreak due to an outdoor release of anthrax spores, when the clinicians only have access to traditional clinical information (e.g., no computer-based alerts). We used this model to estimate an upper bound on the amount of time expected for clinicians to detect such an outbreak. Such estimates may be useful in planning for outbreaks and in assessing the usefulness of various computer-based outbreak detection algorithms.

Submitted by elamb on
Description

In November of 2001 a syndromic surveillance system was established in Los Angeles (LA) County to analyze emergency department (ED) chief complaints in select hospitals. Chief complaints were analyzed and categorized into a syndrome (rash, respiratory, neurological, gastrointestinal), and an algorithm was developed to create a daily threshold for each category. Questions remain as to what events can be detected by the system in a timely manner. On the community level, of interest is whether an outbreak with a wide epidemiological curve would have the intensity of case visits needed to trigger a signal. On the individual level, of interest is the length of time it takes for a person with a given disease characteristic to seek medical attention, whether medical care is sought in the ED first, and how the syndromic system classifies them upon visiting the ED. To address these questions the 2004 LA County West Nile community-wide outbreak was selected for review, with a focus on the more severe neuro-invasive cases.

 

Objective

To evaluate the effectiveness of monitoring emergency room chief complaints as an indicator for a neuro-invasive disease outbreak.

Submitted by elamb on
Description

Our objective in this research is to take advantage of a supercomputer grid (TeraGrid) to develop a distributed memory national scale agent-based model (ABM) to study disease outbreaks at the micro level. This has data needs at both the national data surveillance and the local community structure and outbreak levels.

Submitted by elamb on