Integer time series models for tuberculosis in Africa
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-38707-4 |
| _version_ | 1797778939530182656 |
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| author | Oluwadare O. Ojo Saralees Nadarajah Malick Kebe |
| author_facet | Oluwadare O. Ojo Saralees Nadarajah Malick Kebe |
| author_sort | Oluwadare O. Ojo |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-12T23:23:48Z |
| format | Article |
| id | doaj.art-f7a681afa3b84a69b52da5dfd84559ca |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T23:23:48Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f7a681afa3b84a69b52da5dfd84559ca2023-07-16T11:17:33ZengNature PortfolioScientific Reports2045-23222023-07-0113112110.1038/s41598-023-38707-4Integer time series models for tuberculosis in AfricaOluwadare O. Ojo0Saralees Nadarajah1Malick Kebe2Department of Statistics, Federal University of TechnologyDepartment of Mathematics, University of ManchesterDepartment of Mathematics, Howard UniversityAbstract 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.https://doi.org/10.1038/s41598-023-38707-4 |
| spellingShingle | Oluwadare O. Ojo Saralees Nadarajah Malick Kebe Integer time series models for tuberculosis in Africa Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-023-38707-4 |
| work_keys_str_mv | AT oluwadareoojo integertimeseriesmodelsfortuberculosisinafrica AT saraleesnadarajah integertimeseriesmodelsfortuberculosisinafrica AT malickkebe integertimeseriesmodelsfortuberculosisinafrica |