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Integer time series models for tuberculosis in Africa

Tuberculosis, an airborne disease, is the deadliest human infectious disease caused by one single agent. The African region is among the most affected and most burdensome area in terms of tuberculosis cases. In this paper, we modeled the number of new cases of tuberculosis for 2000–2021 by integer t...

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Autores principales: Ojo, Oluwadare O., Nadarajah, Saralees, Kebe, Malick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349835/
https://www.ncbi.nlm.nih.gov/pubmed/37454188
http://dx.doi.org/10.1038/s41598-023-38707-4
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author Ojo, Oluwadare O.
Nadarajah, Saralees
Kebe, Malick
author_facet Ojo, Oluwadare O.
Nadarajah, Saralees
Kebe, Malick
author_sort Ojo, Oluwadare O.
collection PubMed
description Tuberculosis, an airborne disease, is the deadliest human infectious disease caused by one single agent. The African region is among the most affected and most burdensome area in terms of tuberculosis cases. In this paper, we modeled the number of new cases of tuberculosis for 2000–2021 by integer time series. For each African country, we fitted twenty different models and selected the model that best fitted the data. The twenty models were mostly based on the number of new cases following either the Poisson or negative binomial distribution with the rate parameter allowed to vary linearly or quadratically with respect to year. The best fitted models were used to give predictions for 2022–2031.
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spelling pubmed-103498352023-07-17 Integer time series models for tuberculosis in Africa Ojo, Oluwadare O. Nadarajah, Saralees Kebe, Malick Sci Rep Article Tuberculosis, an airborne disease, is the deadliest human infectious disease caused by one single agent. The African region is among the most affected and most burdensome area in terms of tuberculosis cases. In this paper, we modeled the number of new cases of tuberculosis for 2000–2021 by integer time series. For each African country, we fitted twenty different models and selected the model that best fitted the data. The twenty models were mostly based on the number of new cases following either the Poisson or negative binomial distribution with the rate parameter allowed to vary linearly or quadratically with respect to year. The best fitted models were used to give predictions for 2022–2031. Nature Publishing Group UK 2023-07-15 /pmc/articles/PMC10349835/ /pubmed/37454188 http://dx.doi.org/10.1038/s41598-023-38707-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ojo, Oluwadare O.
Nadarajah, Saralees
Kebe, Malick
Integer time series models for tuberculosis in Africa
title Integer time series models for tuberculosis in Africa
title_full Integer time series models for tuberculosis in Africa
title_fullStr Integer time series models for tuberculosis in Africa
title_full_unstemmed Integer time series models for tuberculosis in Africa
title_short Integer time series models for tuberculosis in Africa
title_sort integer time series models for tuberculosis in africa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349835/
https://www.ncbi.nlm.nih.gov/pubmed/37454188
http://dx.doi.org/10.1038/s41598-023-38707-4
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