Data-Driven outbreak forecasting with a simple non-linear growth model
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Part I by Chet Preston: Predicting the spread of a disease during an outbreak is crucial for devising a plan to counteract it. The accuracy of a predicting model is generally dependent on the accuracy of the parameters and information present to feed into a model. However, in the absence of either computational power or the necessary information regarding the disease transmission characteristics, a simple model may prove valuable if it can provide robust and competitively accurate predictions. This paper develops and evaluates a model that makes predictions to the peak, duration and total number of cases in an outbreak founded only on cumulative number of reported cases. Original publication by Jocelyn Lega and Heidi Brown 2016
Part II by Kathleen Lasick: Understanding the transmission of infectious disease is key to formulating methods of combatting it. Scientists have worked to describe disease transmission accurately enough to help develop public health policies for decades. I will be discussing the development of early models of disease transmission, such as the SIR model, as preparation for discussion about more modern disease transmission modeling methods.
Zoom: https://arizona.zoom.us/j/96503157705 Password: “arizona” (all lower case)