NDLI: EpiCaster: an integrated web application for situation assessment and forecasting of global epidemics

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EpiCaster: an integrated web application for situation assessment and forecasting of global epidemics

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EpiCaster: an integrated web application for situation assessment and forecasting of global epidemics

Content Provider	ACM Digital Library
Author	Barrett, Chris Deodhar, Suruchi Wilson, Mandy Marathe, Madhav Chen, Jiangzhuo Bisset, Keith
Abstract	Public health decision makers need access to high resolution situation assessment tools for understanding the extent of various epidemics in different regions of the world. In addition, they need insights into the future course of epidemics by way of forecasts. Such forecasts are essential for planning the allocation of limited resources and for implementing several policy-level and behavioral intervention strategies. The need for such forecasting systems became evident in the wake of the recent Ebola outbreak in West Africa. We have developed EpiCaster, an integrated Web application for situation assessment and forecasting of various epidemics, such as Flu and Ebola, that are prevalent in different regions of the world. Using EpiCaster, users can assess the magnitude and severity of different epidemics at highly resolved spatio-temporal levels. EpiCaster provides time-varying heat maps and graphical plots to view trends in the disease dynamics. EpiCaster also allows users to visualize data gathered through surveillance mechanisms, such as Google Flu Trends (GFT) and the World Health Organization (WHO). The forecasts provided by EpiCaster are generated using different epidemiological models, and the users can select the models through the interface to filter the corresponding forecasts. EpiCaster also allows the users to study epidemic propagation in the presence of a number of intervention strategies specific to certain diseases. Here we describe the modeling techniques, methodologies and computational infrastructure that EpiCaster relies on to support large-scale predictive analytics for situation assessment and forecasting of global epidemics.
Starting Page	156
Ending Page	165
Page Count	10
File Format	PDF
ISBN	9781450338530
DOI	10.1145/2808719.2808735
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-09
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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