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Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data
Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group A Streptococcus (GAS) contribute substantially to the disease burden in these areas. D...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7561265/ https://www.ncbi.nlm.nih.gov/pubmed/33017395 http://dx.doi.org/10.1371/journal.pcbi.1007838 |
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author | Lydeamore, Michael J. Campbell, Patricia T. Price, David J. Wu, Yue Marcato, Adrian J. Cuningham, Will Carapetis, Jonathan R. Andrews, Ross M. McDonald, Malcolm I. McVernon, Jodie Tong, Steven Y. C. McCaw, James M. |
author_facet | Lydeamore, Michael J. Campbell, Patricia T. Price, David J. Wu, Yue Marcato, Adrian J. Cuningham, Will Carapetis, Jonathan R. Andrews, Ross M. McDonald, Malcolm I. McVernon, Jodie Tong, Steven Y. C. McCaw, James M. |
author_sort | Lydeamore, Michael J. |
collection | PubMed |
description | Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group A Streptococcus (GAS) contribute substantially to the disease burden in these areas. Despite this, estimates for the force of infection, infectious period and basic reproductive ratio—all necessary for the construction of dynamic transmission models—have not been obtained. By utilising three datasets each containing longitudinal infection information on individuals, we estimate each of these epidemiologically important parameters. With an eye to future study design, we also quantify the optimal sampling intervals for obtaining information about these parameters. We verify the estimation method through a simulation estimation study, and test each dataset to ensure suitability to the estimation method. We find that the force of infection differs by population prevalence, and the infectious period is estimated to be between 12 and 20 days. We also find that optimal sampling interval depends on setting, with an optimal sampling interval between 9 and 11 days in a high prevalence setting, and 21 and 27 days for a lower prevalence setting. These estimates unlock future model-based investigations on the transmission dynamics of skin sores. |
format | Online Article Text |
id | pubmed-7561265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75612652020-10-21 Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data Lydeamore, Michael J. Campbell, Patricia T. Price, David J. Wu, Yue Marcato, Adrian J. Cuningham, Will Carapetis, Jonathan R. Andrews, Ross M. McDonald, Malcolm I. McVernon, Jodie Tong, Steven Y. C. McCaw, James M. PLoS Comput Biol Research Article Prevalence of impetigo (skin sores) remains high in remote Australian Aboriginal communities, Fiji, and other areas of socio-economic disadvantage. Skin sore infections, driven primarily in these settings by Group A Streptococcus (GAS) contribute substantially to the disease burden in these areas. Despite this, estimates for the force of infection, infectious period and basic reproductive ratio—all necessary for the construction of dynamic transmission models—have not been obtained. By utilising three datasets each containing longitudinal infection information on individuals, we estimate each of these epidemiologically important parameters. With an eye to future study design, we also quantify the optimal sampling intervals for obtaining information about these parameters. We verify the estimation method through a simulation estimation study, and test each dataset to ensure suitability to the estimation method. We find that the force of infection differs by population prevalence, and the infectious period is estimated to be between 12 and 20 days. We also find that optimal sampling interval depends on setting, with an optimal sampling interval between 9 and 11 days in a high prevalence setting, and 21 and 27 days for a lower prevalence setting. These estimates unlock future model-based investigations on the transmission dynamics of skin sores. Public Library of Science 2020-10-05 /pmc/articles/PMC7561265/ /pubmed/33017395 http://dx.doi.org/10.1371/journal.pcbi.1007838 Text en © 2020 Lydeamore et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lydeamore, Michael J. Campbell, Patricia T. Price, David J. Wu, Yue Marcato, Adrian J. Cuningham, Will Carapetis, Jonathan R. Andrews, Ross M. McDonald, Malcolm I. McVernon, Jodie Tong, Steven Y. C. McCaw, James M. Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data |
title | Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data |
title_full | Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data |
title_fullStr | Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data |
title_full_unstemmed | Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data |
title_short | Estimation of the force of infection and infectious period of skin sores in remote Australian communities using interval-censored data |
title_sort | estimation of the force of infection and infectious period of skin sores in remote australian communities using interval-censored data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7561265/ https://www.ncbi.nlm.nih.gov/pubmed/33017395 http://dx.doi.org/10.1371/journal.pcbi.1007838 |
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