Informal dissemination scenarios and the effectiveness of evacuation warning dissemination of households:a simulation study


Timely warning of the public during large scale emergencies is essential to ensure safety and save lives. This ongoing study proposes an agent-based simulation model to simulate the warning message dissemination among the public considering both official channels and unofficial channels The proposed model was developed in NetLogo software for a hypothetical area, and requires input parameters such as effectiveness of each official source (%), estimated time to begin informing others, estimated time to inform others and estimated percentage of people (who do not relay the message). This paper demonstrates a means of factoring the behaviour of the public as informants into estimating the effectiveness of warningdissemination during large scale emergencies. The model provides a tool for the practitioner to test the potential impact of the informal channels on the overall warning time and sensitivity of the modelling parameters. The tool would help the practitioners to persuade evacuees to disseminate the warning message informing others similar to the ’Run to thy neighbour campaign conducted by the Red cross.

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Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) Funding: European Commission under the Directorate-General Justice Freedom and Security (JLS/2007/CIPS/025).
Uncontrolled Keywords: agent-based model,mass emergency warning dissemination,simulation model for warning dissemination,NetLogo,evacuation warning,Engineering(all)
Publication ISSN: 1877-7058
Last Modified: 08 May 2024 07:08
Date Deposited: 13 Oct 2014 10:55
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2010
Published Online Date: 2010-08-20
Authors: Nagarajan, Magesh
Shaw, Duncan
Albores, Pavel (ORCID Profile 0000-0001-7509-9381)


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