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Simulation

Historical data are essential for development of detection algorithms. Spatio-temporal data, however, are difficult to come by due to variety of issues concerning patient confidentiality. Several approaches have been used to generate benchmark data using statistical methods. Here, we demonstrate how to generate benchmark data using a discrete event model simulating inter- and intra-contact network transmission dynamics of infectious diseases in space and time using publicly available population data.

 

OBJECTIVE

The objective of this study is to generate benchmark data from a discrete event model simulating the transmission dynamics of an infectious disease within and between contact networks in urban settings using real population data. Such data can be used to test the performance of various temporal and spatio-temporal detection algorithms when real data are scarce or cannot be shared.

Referenced File
Submitted by elamb on

In order to be best prepared to identify health events using electronic disease surveillance systems, it is vital for users to participate in regular exercises that realistically simulate how events may present in their system following disease manifestation in the community. Furthermore, it is necessary that users exercise methods of communicating unusual occurrences to other intra and extra-jurisdictional investigators quickly and efficiently to determine first, if an event actually exits and if one does its characteristics. A simulation exercise held in the National Capital Region (NCR) in the spring of this year exercised a novel format for engaging users while testing the utility of an embedded event communication tool.

 

Objective

This is a description of an innovative design and format used to exercise public health preparedness in a tri-jurisdictional disease surveillance system in the spring of 2006.

Submitted by elamb on

Objective

Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more generally, early event detection systems. While the statistical anomaly detection methods take into account baseline characteristics of the time series, these simulated outbreaks are introduced on an ad hoc basis and do not take into account those baseline characteristics. Our objective was to develop statistical-based procedures to introduce artificial anomalies into time series, which thus would have wide applicability for evaluation of anomaly detection algorithms against widely different data streams.

Referenced File
Submitted by elamb on

Epidemiological models that simulate the spread of Foot-and-Mouth Disease within a herd are the foundation of decision support tools used by governments to help advise and inform strategy to combat outbreaks. Contact transmission data used to parameterize these models, contrary to assumption, contain a significant amount of variability and uncertainty. The implications of this finding suggest that the resultant model output might not accurately simulate the spread of an outbreak. If this is true, the potential impact due to uncertainty inherent to the decision support tools used by governments might be significant.

Objective

The objective of this project is to understand how parametric un- certainty within intra-herd Foot-and-Mouth disease epidemiological models affects the outbreak simulations and what implications this has on surveillance and control strategy and policy.

Submitted by dbedford on

The standard approaches to simulation include solving of differential equation systems. Such approach is good for obtaining general picture of epidemics (1, 2). When the detailed analysis of epidemics reasons is needed such model becomes insufficient. To overcome the limitations of standard approaches a new one has been offered. The multiagent approach has been offered to be used for representation of the society. Methods of event-driven programming give essential benefits of the processing time of the events (3).

Objective:

To develop multiagent model of hepatitis B (HBV) infection spreading.

 

Submitted by Magou on

Effective responses to epidemics of infectious diseases hinge not only on early outbreak detection, but also on an assessment of disease severity. In recent work, we combined previously developed ARI case-detection algorithms (CDA) [1] with text analyses of chest imaging reports to identify ARI patients whose providers thought had pneumonia. In this work, we asked if a surveillance system aimed at patients with pneumonia would outperform one that monitors the full severity spectrum of ARI.

Objective

To determine if influenza surveillance should target all patients with acute respiratory infections (ARI) or only track pneumonia cases.

 

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

Whilst the sensitivity and specificity of traditional laboratory-based surveillance can be readily estimated, the situation is less clear cut for syndromic surveillance. Syndromic surveillance indicators based upon presenting symptoms, chief complaints or preliminary diagnoses are designed to provide public health systems with support to detect multiple potential threats to public health. There is however, no gold standard list of all the possible ‘events’ that should have been detected. This is especially true in emergency response where systems are designed to detect possible events for which there is no directly comparable historical precedent.

Objective

To devise a methodology for validating the effectiveness of syndromic surveillance systems across a range of public health scenarios, even in the absence of historical example datasets.

Submitted by Magou on

Simulations of infectious disease spread have increasingly been used to inform public policy for planning and response to outbreaks. As these techniques have increased in sophistication a wider array of uses becomes appropriate. In particular, surveillance system design, evaluation, and interpretation can be greatly aided by simulation. This presentation will describe a style of highly detailed agent-based simulation and a synthetic information analysis platform that is well equipped for these tasks.

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