Literature DB >> 25530914

An Interactive, Web-based High Performance Modeling Environment for Computational Epidemiology.

Suruchi Deodhar1, Keith R Bisset2, Jiangzhuo Chen2, Yifei Ma1, Madhav V Marathe3.   

Abstract

We present an integrated interactive modeling environment to support public health epidemiology. The environment combines a high resolution individual-based model with a user-friendly web-based interface that allows analysts to access the models and the analytics back-end remotely from a desktop or a mobile device. The environment is based on a loosely-coupled service-oriented-architecture that allows analysts to explore various counter factual scenarios. As the modeling tools for public health epidemiology are getting more sophisticated, it is becoming increasingly hard for non-computational scientists to effectively use the systems that incorporate such models. Thus an important design consideration for an integrated modeling environment is to improve ease of use such that experimental simulations can be driven by the users. This is achieved by designing intuitive and user-friendly interfaces that allow users to design and analyze a computational experiment and steer the experiment based on the state of the system. A key feature of a system that supports this design goal is the ability to start, stop, pause and roll-back the disease propagation and intervention application process interactively. An analyst can access the state of the system at any point in time and formulate dynamic interventions based on additional information obtained through state assessment. In addition, the environment provides automated services for experiment set-up and management, thus reducing the overall time for conducting end-to-end experimental studies. We illustrate the applicability of the system by describing computational experiments based on realistic pandemic planning scenarios. The experiments are designed to demonstrate the system's capability and enhanced user productivity.

Entities:  

Keywords:  Computational epidemiology; Computational steering; Design; Experimentation; Interactive computations; Network-based epidemiological modeling; Performance; Service oriented architectures; Usability; User productivity

Year:  2014        PMID: 25530914      PMCID: PMC4270291          DOI: 10.1145/2629692

Source DB:  PubMed          Journal:  ACM Trans Manag Inf Syst        ISSN: 2158-656X


  15 in total

1.  Percolation and epidemics in a two-dimensional small world.

Authors:  M E J Newman; I Jensen; R M Ziff
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2002-01-16

2.  Modelling disease outbreaks in realistic urban social networks.

Authors:  Stephen Eubank; Hasan Guclu; V S Anil Kumar; Madhav V Marathe; Aravind Srinivasan; Zoltán Toroczkai; Nan Wang
Journal:  Nature       Date:  2004-05-13       Impact factor: 49.962

3.  Epinome: a visual-analytics workbench for epidemiology data.

Authors:  Yarden Livnat; Theresa-Marie Rhyne; Matthew H Samore
Journal:  IEEE Comput Graph Appl       Date:  2012 Mar-Apr       Impact factor: 2.088

4.  Indemics: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling.

Authors:  Keith R Bisset; Jiangzhuo Chen; Suruchi Deodhar; Xizhou Feng; Yifei Ma; Madhav V Marathe
Journal:  ACM Trans Model Comput Simul       Date:  2014-01       Impact factor: 1.075

5.  A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission.

Authors:  Jon Parker; Joshua M Epstein
Journal:  ACM Trans Model Comput Simul       Date:  2011-12       Impact factor: 1.075

6.  FluTE, a publicly available stochastic influenza epidemic simulation model.

Authors:  Dennis L Chao; M Elizabeth Halloran; Valerie J Obenchain; Ira M Longini
Journal:  PLoS Comput Biol       Date:  2010-01-29       Impact factor: 4.475

7.  Dynamic health policies for controlling the spread of emerging infections: influenza as an example.

Authors:  Reza Yaesoubi; Ted Cohen
Journal:  PLoS One       Date:  2011-09-06       Impact factor: 3.240

8.  Strategies for mitigating an influenza pandemic.

Authors:  Neil M Ferguson; Derek A T Cummings; Christophe Fraser; James C Cajka; Philip C Cooley; Donald S Burke
Journal:  Nature       Date:  2006-04-26       Impact factor: 49.962

Review 9.  Closure of schools during an influenza pandemic.

Authors:  Simon Cauchemez; Neil M Ferguson; Claude Wachtel; Anders Tegnell; Guillaume Saour; Ben Duncan; Angus Nicoll
Journal:  Lancet Infect Dis       Date:  2009-08       Impact factor: 25.071

Review 10.  Planning for smallpox outbreaks.

Authors:  Neil M Ferguson; Matt J Keeling; W John Edmunds; Raymond Gani; Bryan T Grenfell; Roy M Anderson; Steve Leach
Journal:  Nature       Date:  2003-10-16       Impact factor: 49.962

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Authors:  Xiaoyan Li; Alexander Doroshenko; Nathaniel D Osgood
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3.  A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.

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  3 in total

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