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Niemi Jarad

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

This report describes an exploratory analysis of the 2009-2010 Zimbabwe measles outbreak based on data publicly available in the World Health Organization's Zimbabwe cholera epidemiological bulletin archive. As of December 12th 2010, the outbreak appears to have ended after it is suspected to have caused 13,783 infections, 693 of those being confirmed IgM positive, and 631 deaths.

Objective

To systematically organize the World Health Organization data on the 2010 measles outbreak in Zimbabwe. To perform a post-hoc exploratory analysis to understand how the outbreak spread geographically and evaluate the effectiveness of a mass vaccination campaign.

Submitted by elamb on
Description

Optimal sequential management of disease outbreaks has been shown to dramatically improve the realized outbreak costs when the number of newly infected and recovered individuals is assumed to be known (1,2). This assumption has been relaxed so that infected and recovered individuals are sampled and therefore the rate of information gain about the infectiousness and morbidity of a particular outbreak is proportional to the sampling rate (3). We study the effect of no recovered sampling and signal delay, features common to surveillance systems, on the costs associated with an outbreak.

Objective

Development of general methodology for optimal decisions during disease outbreaks that incorporate uncertainty in both parameters governing the outbreak and the current outbreak state in terms of the number of current infected, immune, and susceptible individuals.

Submitted by elamb on
Description

Interactive tools for visualization of disease outbreaks has been improving markedly in the past few years. With the flagships Google Flutrends1 and HealthMap2 providing prime examples. These tools provide interactive access to the general public concerning the current state-of-affairs for disease outbreaks generally and specifically for influenza. For example, while browsing HealthMap I learned of a case of tuberculosis on my campus, Iowa State University. While extremely sophisticated, these tools do not utilize modern statistical algorithms for detection or forecasting. In addition, the development cost and perhaps the maintenance cost is not trivial. We aim to build a similar visualization tool that incorporates modern algorithms for detection and forecasting but has low development and maintenance cost. Due to the low cost this tool is appropriate for quick deployment in developing countries for emerging outbreaks as well as public health agencies with declining operating budgets.

Objective

To build a zero-cost tool for disease outbreak visualization, detection, and forecasting incorporating modern tools.

Submitted by knowledge_repo… on
Description

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.

 

Submitted by Magou on
Description

The CDC provides data on incidences of diseases on its website (https://data.cdc.gov/). Data is available at national, regional, and state levels, and is uploaded to the CDC’s website on a weekly basis. The CDCPlot web application (available at https://michaud.shinyapps.io/ CDCPlot/), built using the Shiny package in R, provides a quick and user-friendly method of visualizing this data. Users are able to the select timeframes, locations, and diseases which they wish to view, and plots are produced. There is an optional alert threshold, which will alert users when a disease increases significantly from one week to the next. In addition, CDCPlot provides visualizations of CDC data on Pneumonia and Influenza mortality.

Objective

To demonstrate the current features and functionality of the CDCPlot application, and to introduce potential new features of the application. 

Submitted by rmathes on
Description

Timely monitoring and prediction of the trajectory of seasonal influenza epidemics allows hospitals and medical centers to prepare for, and provide better service to, patients with influenza. The CDC’s ILINet system collects data on influenza-like illnesses from over 3,300 health care providers, and uses this data to produce accurate indicators of current influenza epidemic severity. However, ILINet indicators are typically reported at a lag of 1-2 weeks. Another source of severity data, Google Flu Trends, is calculated by aggregating Google searches for certain influenza related terms. Google Flu Trends data is provided in near-real time, but is a less direct measurement of severity than ILINet indicators, and is likely to suffer from bias. We create a hierarchical model to estimate epidemic severity for the 2014 - 2015 epidemic season which incorporates current and historical data from both ILINet and Google Flu Trends, allowing our model to benefit both from the recency of Google Flu Trends data and the accuracy of ILINet data.

Objective

To use multiple data sources of influenza epidemic severity to inform a model which can estimate and forecast severity for the current influenza epidemic season by accounting for the bias from each source.

Submitted by teresa.hamby@d… on