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Agent-based models of malaria transmission: a systematic review

BACKGROUND: Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are i...

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Autores principales: Smith, Neal R., Trauer, James M., Gambhir, Manoj, Richards, Jack S., Maude, Richard J., Keith, Jonathan M., Flegg, Jennifer A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098619/
https://www.ncbi.nlm.nih.gov/pubmed/30119664
http://dx.doi.org/10.1186/s12936-018-2442-y
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author Smith, Neal R.
Trauer, James M.
Gambhir, Manoj
Richards, Jack S.
Maude, Richard J.
Keith, Jonathan M.
Flegg, Jennifer A.
author_facet Smith, Neal R.
Trauer, James M.
Gambhir, Manoj
Richards, Jack S.
Maude, Richard J.
Keith, Jonathan M.
Flegg, Jennifer A.
author_sort Smith, Neal R.
collection PubMed
description BACKGROUND: Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations. METHODS: A systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field. RESULTS: The rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques. CONCLUSION: Collectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-018-2442-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-60986192018-08-23 Agent-based models of malaria transmission: a systematic review Smith, Neal R. Trauer, James M. Gambhir, Manoj Richards, Jack S. Maude, Richard J. Keith, Jonathan M. Flegg, Jennifer A. Malar J Research BACKGROUND: Much of the extensive research regarding transmission of malaria is underpinned by mathematical modelling. Compartmental models, which focus on interactions and transitions between population strata, have been a mainstay of such modelling for more than a century. However, modellers are increasingly adopting agent-based approaches, which model hosts, vectors and/or their interactions on an individual level. One reason for the increasing popularity of such models is their potential to provide enhanced realism by allowing system-level behaviours to emerge as a consequence of accumulated individual-level interactions, as occurs in real populations. METHODS: A systematic review of 90 articles published between 1998 and May 2018 was performed, characterizing agent-based models (ABMs) relevant to malaria transmission. The review provides an overview of approaches used to date, determines the advantages of these approaches, and proposes ideas for progressing the field. RESULTS: The rationale for ABM use over other modelling approaches centres around three points: the need to accurately represent increased stochasticity in low-transmission settings; the benefits of high-resolution spatial simulations; and heterogeneities in drug and vaccine efficacies due to individual patient characteristics. The success of these approaches provides avenues for further exploration of agent-based techniques for modelling malaria transmission. Potential extensions include varying elimination strategies across spatial landscapes, extending the size of spatial models, incorporating human movement dynamics, and developing increasingly comprehensive parameter estimation and optimization techniques. CONCLUSION: Collectively, the literature covers an extensive array of topics, including the full spectrum of transmission and intervention regimes. Bringing these elements together under a common framework may enhance knowledge of, and guide policies towards, malaria elimination. However, because of the diversity of available models, endorsing a standardized approach to ABM implementation may not be possible. Instead it is recommended that model frameworks be contextually appropriate and sufficiently described. One key recommendation is to develop enhanced parameter estimation and optimization techniques. Extensions of current techniques will provide the robust results required to enhance current elimination efforts. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-018-2442-y) contains supplementary material, which is available to authorized users. BioMed Central 2018-08-17 /pmc/articles/PMC6098619/ /pubmed/30119664 http://dx.doi.org/10.1186/s12936-018-2442-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Smith, Neal R.
Trauer, James M.
Gambhir, Manoj
Richards, Jack S.
Maude, Richard J.
Keith, Jonathan M.
Flegg, Jennifer A.
Agent-based models of malaria transmission: a systematic review
title Agent-based models of malaria transmission: a systematic review
title_full Agent-based models of malaria transmission: a systematic review
title_fullStr Agent-based models of malaria transmission: a systematic review
title_full_unstemmed Agent-based models of malaria transmission: a systematic review
title_short Agent-based models of malaria transmission: a systematic review
title_sort agent-based models of malaria transmission: a systematic review
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6098619/
https://www.ncbi.nlm.nih.gov/pubmed/30119664
http://dx.doi.org/10.1186/s12936-018-2442-y
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