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Ludkovski Michael

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

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

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

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.

Submitted by ynwang@ufl.edu on
Description

Currently Centers for Disease Control and Prevention (CDC) employ threshold rules to declare epidemic outbreaks, such as influenza, separately in each population. However each year influenza starts in one population and spreads population-to-population throughout the country. Therefore there is a need for an algorithm to declare the epidemic that uses information from multiple populations.

Objective

Detect epidemics over multiple Populations using computational methods

Submitted by teresa.hamby@d… on