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Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources

Infectious disease outbreaks occurred many times in the past and are more likely to happen in the future. In this paper the problem of allocating and scheduling limited multiple, identical or non-identical, resources employed in parallel, when there are several infected areas, is considered. A heuri...

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Autores principales: Rachaniotis, Nikolaos, Dasaklis, Thomas K., Pappis, Costas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104597/
https://www.ncbi.nlm.nih.gov/pubmed/32288410
http://dx.doi.org/10.1007/s11518-016-5327-z
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author Rachaniotis, Nikolaos
Dasaklis, Thomas K.
Pappis, Costas
author_facet Rachaniotis, Nikolaos
Dasaklis, Thomas K.
Pappis, Costas
author_sort Rachaniotis, Nikolaos
collection PubMed
description Infectious disease outbreaks occurred many times in the past and are more likely to happen in the future. In this paper the problem of allocating and scheduling limited multiple, identical or non-identical, resources employed in parallel, when there are several infected areas, is considered. A heuristic algorithm, based on Shih’s (1974) and Pappis and Rachaniotis’ (2010) algorithms, is proposed as the solution methodology. A numerical example implementing the proposed methodology in the context of a specific disease outbreak, namely influenza, is presented. The proposed methodology could be of significant value to those drafting contingency plans and healthcare policy agendas.
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spelling pubmed-71045972020-03-31 Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources Rachaniotis, Nikolaos Dasaklis, Thomas K. Pappis, Costas J Syst Sci Syst Eng Article Infectious disease outbreaks occurred many times in the past and are more likely to happen in the future. In this paper the problem of allocating and scheduling limited multiple, identical or non-identical, resources employed in parallel, when there are several infected areas, is considered. A heuristic algorithm, based on Shih’s (1974) and Pappis and Rachaniotis’ (2010) algorithms, is proposed as the solution methodology. A numerical example implementing the proposed methodology in the context of a specific disease outbreak, namely influenza, is presented. The proposed methodology could be of significant value to those drafting contingency plans and healthcare policy agendas. Springer Berlin Heidelberg 2017-01-24 2017 /pmc/articles/PMC7104597/ /pubmed/32288410 http://dx.doi.org/10.1007/s11518-016-5327-z Text en © Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2017 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Rachaniotis, Nikolaos
Dasaklis, Thomas K.
Pappis, Costas
Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources
title Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources
title_full Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources
title_fullStr Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources
title_full_unstemmed Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources
title_short Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources
title_sort controlling infectious disease outbreaks: a deterministic allocation-scheduling model with multiple discrete resources
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104597/
https://www.ncbi.nlm.nih.gov/pubmed/32288410
http://dx.doi.org/10.1007/s11518-016-5327-z
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