Bayesian Methods

Development of effective policy interventions to stem disease outbreaks requires knowledge of the current state of affairs, e.g. how many individuals are currently infected, a strain’s virulence, etc, as well as our uncertainty of these values. A Bayesian inferential approach provides this information, but at a computational expense. We develop a sequential Bayesian approach based on an epidemiological compartment model and noisy count observations of the transitions between compartments.

Objective:

Develop fast sequential Bayesian inference for disease outbreak counts.

 

The ESSENCE demonstration module was built to help DoD health monitors make routine decisions based on disparate evidence sources such as daily counts of ILI-related chief complaints, ratios of positive lab tests for influenza, patient age distribution, and counts of antiviral prescriptions [1]. The module was a population-based (rather than individual-based) Bayesian network (PBN) in that inputs were algorithmic results from these multiple aggregate data streams, and output was the degree of belief that the combined evidence required investigation. The module reduced total alerts substantially and retained sensitivity to the majority of documented outbreaks while clarifying underlying sources of evidence. The current effort was to advance the prototype to production by refining components of the fusion methodology to improve sensitivity while retaining the reduced alert rate.

Objective

The project involves analytic combination of multiple evidence sources to monitor health at hundreds of care facilities. A demonstration module featuring a population-based Bayes Network [1] was refined and expanded for application in the Department of Defense Electronic Surveillance System for Community-Based Epidemics (ESSENCE).

Population surges or large events may cause shift of data collected by biosurveillance systems [1]. For example, the Cherry Blossom Festival brings hundreds of thousands of people to DC every year, which results in simultaneous elevations in multiple data streams (Fig. 1). In this paper, we propose an MGD model to accommodate the needs of dealing with baseline shifts.

Objective:

Outbreak detection algorithms monitoring only disease-relevant data streams may be prone to false alarms due to baseline shifts. In this paper, we propose a Multinomial-Generalized-Dirichlet (MGD) model to adjust for baseline shifts.

 

Detection and response to seasonal outbreaks of endemic diseases provides an excellent testbed for quantitative bio-surveillance. As a case study we focus on annual influenza outbreaks. To incorporate observed year-over-year variation in flu incidence cases and timing of outbreaks, we analyze a stochastic compartmental SIS model that includes seasonal forcing by a latent Markovian factor. Epidemic detection then consists in identifying the presence of the environmental factor (“high” flu season), as well as estimation of the epidemic parameters, such as contact and recovery rates.

Objective

Development of a sequential Bayesian methodology for inference and detection of seasonal infectious disease epidemics.

The choice of outbreak detection algorithm and its configuration can result in important variations in the performance of public health surveillance systems. Our work aims to characterize the performance of detectors based on outbreak types. We are using Bayesian networks (BN) to model the relationships between determinants of outbreak detection and the detection performance based on a significant study on simulated data.

Objective

To predict the performance of outbreak detection algorithms under different circumstances which will guide the method selection and algorithm configuration in surveillance systems, to characterize the dependence of the performance of detection algorithms on the type and severity of outbreak, to develop quantitative evidence about determinants of detection performance.

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.

 

Syndromic surveillance has been widely used in influenza surveillance worldwide. However, despite the potential benefits created by the large volume of data, biases due to the changes in healthcare seeking behavior and physicians’ reporting behavior, as well as the background noise caused by seasonal flu epidemics, contribute to the complexity of the surveillance system and may limit its utility as a tool for early detection. Since most current analysis methods are developed for outbreak detection, there are few tools to characterize influenza surveillance data for situational awareness purposes in a quantitative manner. Hong Kong Centre for Health Protection has a comprehensive influenza surveillance system based on healthcare providers, laboratories, schools, daycare centers and residential care homes for the elderly. Hong Kong usually experiences a summer peak in July and August, which potentially doubles the data volume and constitutes a natural experiment to assess the effect of school-age children in the influenza transmission dynamics. The richness of the available data and the unique epidemiological characteristics make Hong Kong an ideal study object to develop and evaluate our model.

Objective

Our goal is to develop a statistical model for characterizing influenza surveillance systems that will be helpful in interpreting multiple streams of influenza surveillance data in future outbreaks.

Syndromic surveillance systems often produce large numbers of detections due to excess activity (alarms) in their indicators. Few alarms are classified as alerts (public health events that may require a response). Decision-making in syndromic surveillance as to whether an alarm requires a response (alert) is often entirely based on expert knowledge. These approaches (known as heuristics) may work well and produce faster results than automated processes (known as normative), but usually rely on the expertise of a small group of experts who hold much of their knowledge implicitly. The effectiveness of syndromic surveillance systems could be compromised in the absence of experts, which may significantly impact their response during a public health emergency. Also, there may be patterns and relations in the data not recognised by the experts. Structural learning provides a mechanism to identify relations between syndromic indicators and the relations between these indicators and alerts. Their outputs could be used to help decision makers determine more effectively which alarms are most likely to lead to alerts. A normative approach may reduce the reliance of the decision making process on key individuals

Objective

To analyse the use of Bayesian network structural learning to identify relations between syndromic indicators which could inform decision-making processes