Cargando…
Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models
BACKGROUND: A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has bee...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374423/ https://www.ncbi.nlm.nih.gov/pubmed/34426791 http://dx.doi.org/10.1186/s43088-021-00136-5 |
_version_ | 1783740113434443776 |
---|---|
author | Khan, Firdos Lounis, Mohamed |
author_facet | Khan, Firdos Lounis, Mohamed |
author_sort | Khan, Firdos |
collection | PubMed |
description | BACKGROUND: A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days. RESULTS: The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of − 89 to 214 and 108–186, respectively. Deaths’ forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1–17 and 4–20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of − 106 to 185 and 44–239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively. CONCLUSION: The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments. |
format | Online Article Text |
id | pubmed-8374423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-83744232021-08-19 Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models Khan, Firdos Lounis, Mohamed Beni Suef Univ J Basic Appl Sci Research BACKGROUND: A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days. RESULTS: The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of − 89 to 214 and 108–186, respectively. Deaths’ forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1–17 and 4–20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of − 106 to 185 and 44–239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively. CONCLUSION: The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments. Springer Berlin Heidelberg 2021-08-19 2021 /pmc/articles/PMC8374423/ /pubmed/34426791 http://dx.doi.org/10.1186/s43088-021-00136-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Research Khan, Firdos Lounis, Mohamed Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models |
title | Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models |
title_full | Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models |
title_fullStr | Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models |
title_full_unstemmed | Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models |
title_short | Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models |
title_sort | short-term forecasting of daily infections, fatalities and recoveries about covid-19 in algeria using statistical models |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374423/ https://www.ncbi.nlm.nih.gov/pubmed/34426791 http://dx.doi.org/10.1186/s43088-021-00136-5 |
work_keys_str_mv | AT khanfirdos shorttermforecastingofdailyinfectionsfatalitiesandrecoveriesaboutcovid19inalgeriausingstatisticalmodels AT lounismohamed shorttermforecastingofdailyinfectionsfatalitiesandrecoveriesaboutcovid19inalgeriausingstatisticalmodels |