Evidence map›Paper›PMID 25530914›Full record

ArticleACM transactions on management information systems2014

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

Suruchi Deodhar, Keith R Bisset, Jiangzhuo Chen, Yifei Ma, Madhav V Marathe

Abstract read
In one paragraph

Article in ACM transactions on management information systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Suruchi DeodharNDSSL, Virginia Bioinformatics Institute, Virginia Tech , Department of Computer Science, Virginia Tech.
Keith R BissetNDSSL, Virginia Bioinformatics Institute, Virginia Tech.
Jiangzhuo ChenNDSSL, Virginia Bioinformatics Institute, Virginia Tech.
Yifei MaNDSSL, Virginia Bioinformatics Institute, Virginia Tech , Department of Computer Science, Virginia Tech.
Madhav V MaratheNDSSL, Virginia Bioinformatics Institute, Virginia Tech, Department of Computer Science, Virginia Tech.

Funding

Synthetic Information Systems for Better Informing Public Health PolicymakersU01GM070694 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI EUBANK, STEPHEN G, MARATHE, MADHAV VISHNU · 2004 to 2016
$8.6M
NIGMS NIH HHS U01 GM070694
6 · The paper itself

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.

Indexed as

Computational epidemiologyComputational steeringDesignExperimentationInteractive computationsNetwork-based epidemiological modelingPerformanceService oriented architecturesUsabilityUser productivity

Identifiers

PMID25530914
PMCPMC4270291

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.