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A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance
The evolutionary responses of infectious pathogens often have ruinous consequences for the control of disease spread in the population. Drug resistance is a well-documented instance that is generally driven by the selective pressure of drugs on both the replication of the pathogen within hosts and i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier Ltd.
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127136/ https://www.ncbi.nlm.nih.gov/pubmed/19344730 http://dx.doi.org/10.1016/j.jtbi.2009.03.029 |
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author | Alexander, Murray E. Dietrich, Sarah M. Hua, Yi Moghadas, Seyed M. |
author_facet | Alexander, Murray E. Dietrich, Sarah M. Hua, Yi Moghadas, Seyed M. |
author_sort | Alexander, Murray E. |
collection | PubMed |
description | The evolutionary responses of infectious pathogens often have ruinous consequences for the control of disease spread in the population. Drug resistance is a well-documented instance that is generally driven by the selective pressure of drugs on both the replication of the pathogen within hosts and its transmission between hosts. Management of drug resistance therefore requires the development of treatment strategies that can impede the emergence and spread of resistance in the population. This study evaluates various treatment strategies for influenza infection as a case study by comparing the long-term epidemiological outcomes predicted by deterministic and stochastic versions of a homogeneously mixing (mean-field) model and those predicted by a heterogeneous model that incorporates spatial pair-wise correlation. We discuss the importance of three major parameters in our evaluation: the basic reproduction number, the population level of treatment, and the degree of clustering as a key parameter determining the structure of heterogeneous interactions. The results show that, as a common feature in all models, high treatment levels during the early stages of disease outset can result in large resistant outbreaks, with the possibility of a second wave of infection appearing in the pair-approximation model. Our simulations demonstrate that, if the basic reproduction number exceeds a threshold value, the population-wide spread of the resistant pathogen emerges more rapidly in the pair-approximation model with significantly lower treatment levels than in the homogeneous models. We tested an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. The findings indicate that the overall disease incidence is reduced as the degree of clustering increases, and a longer delay should be considered for implementing the large-scale treatment. |
format | Online Article Text |
id | pubmed-7127136 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Published by Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71271362020-04-06 A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance Alexander, Murray E. Dietrich, Sarah M. Hua, Yi Moghadas, Seyed M. J Theor Biol Article The evolutionary responses of infectious pathogens often have ruinous consequences for the control of disease spread in the population. Drug resistance is a well-documented instance that is generally driven by the selective pressure of drugs on both the replication of the pathogen within hosts and its transmission between hosts. Management of drug resistance therefore requires the development of treatment strategies that can impede the emergence and spread of resistance in the population. This study evaluates various treatment strategies for influenza infection as a case study by comparing the long-term epidemiological outcomes predicted by deterministic and stochastic versions of a homogeneously mixing (mean-field) model and those predicted by a heterogeneous model that incorporates spatial pair-wise correlation. We discuss the importance of three major parameters in our evaluation: the basic reproduction number, the population level of treatment, and the degree of clustering as a key parameter determining the structure of heterogeneous interactions. The results show that, as a common feature in all models, high treatment levels during the early stages of disease outset can result in large resistant outbreaks, with the possibility of a second wave of infection appearing in the pair-approximation model. Our simulations demonstrate that, if the basic reproduction number exceeds a threshold value, the population-wide spread of the resistant pathogen emerges more rapidly in the pair-approximation model with significantly lower treatment levels than in the homogeneous models. We tested an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. The findings indicate that the overall disease incidence is reduced as the degree of clustering increases, and a longer delay should be considered for implementing the large-scale treatment. Published by Elsevier Ltd. 2009-07-21 2009-04-01 /pmc/articles/PMC7127136/ /pubmed/19344730 http://dx.doi.org/10.1016/j.jtbi.2009.03.029 Text en Crown copyright © 2009 Published by Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Alexander, Murray E. Dietrich, Sarah M. Hua, Yi Moghadas, Seyed M. A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
title | A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
title_full | A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
title_fullStr | A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
title_full_unstemmed | A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
title_short | A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
title_sort | comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127136/ https://www.ncbi.nlm.nih.gov/pubmed/19344730 http://dx.doi.org/10.1016/j.jtbi.2009.03.029 |
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