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Predictive modelling of COVID-19 confirmed cases in Nigeria

The coronavirus outbreak is the most notable world crisis since the Second World War. The pandemic that originated from Wuhan, China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come. This necessitates the need t...

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Autores principales: Ogundokun, Roseline O., Lukman, Adewale F., Kibria, Golam B.M., Awotunde, Joseph B., Aladeitan, Benedita B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428444/
https://www.ncbi.nlm.nih.gov/pubmed/32835145
http://dx.doi.org/10.1016/j.idm.2020.08.003
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author Ogundokun, Roseline O.
Lukman, Adewale F.
Kibria, Golam B.M.
Awotunde, Joseph B.
Aladeitan, Benedita B.
author_facet Ogundokun, Roseline O.
Lukman, Adewale F.
Kibria, Golam B.M.
Awotunde, Joseph B.
Aladeitan, Benedita B.
author_sort Ogundokun, Roseline O.
collection PubMed
description The coronavirus outbreak is the most notable world crisis since the Second World War. The pandemic that originated from Wuhan, China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come. This necessitates the need to monitor and predict COVID-19 prevalence for adequate control. The linear regression models are prominent tools in predicting the impact of certain factors on COVID-19 outbreak and taking the necessary measures to respond to this crisis. The data was extracted from the NCDC website and spanned from March 31, 2020 to May 29, 2020. In this study, we adopted the ordinary least squares estimator to measure the impact of travelling history and contacts on the spread of COVID-19 in Nigeria and made a prediction. The model was conducted before and after travel restriction was enforced by the Federal government of Nigeria. The fitted model fitted well to the dataset and was free of any violation based on the diagnostic checks conducted. The results show that the government made a right decision in enforcing travelling restriction because we observed that travelling history and contacts made increases the chances of people being infected with COVID-19 by 85% and 88% respectively. This prediction of COVID-19 shows that the government should ensure that most travelling agency should have better precautions and preparations in place before re-opening.
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spelling pubmed-74284442020-08-16 Predictive modelling of COVID-19 confirmed cases in Nigeria Ogundokun, Roseline O. Lukman, Adewale F. Kibria, Golam B.M. Awotunde, Joseph B. Aladeitan, Benedita B. Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu The coronavirus outbreak is the most notable world crisis since the Second World War. The pandemic that originated from Wuhan, China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come. This necessitates the need to monitor and predict COVID-19 prevalence for adequate control. The linear regression models are prominent tools in predicting the impact of certain factors on COVID-19 outbreak and taking the necessary measures to respond to this crisis. The data was extracted from the NCDC website and spanned from March 31, 2020 to May 29, 2020. In this study, we adopted the ordinary least squares estimator to measure the impact of travelling history and contacts on the spread of COVID-19 in Nigeria and made a prediction. The model was conducted before and after travel restriction was enforced by the Federal government of Nigeria. The fitted model fitted well to the dataset and was free of any violation based on the diagnostic checks conducted. The results show that the government made a right decision in enforcing travelling restriction because we observed that travelling history and contacts made increases the chances of people being infected with COVID-19 by 85% and 88% respectively. This prediction of COVID-19 shows that the government should ensure that most travelling agency should have better precautions and preparations in place before re-opening. KeAi Publishing 2020-08-15 /pmc/articles/PMC7428444/ /pubmed/32835145 http://dx.doi.org/10.1016/j.idm.2020.08.003 Text en © 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Ogundokun, Roseline O.
Lukman, Adewale F.
Kibria, Golam B.M.
Awotunde, Joseph B.
Aladeitan, Benedita B.
Predictive modelling of COVID-19 confirmed cases in Nigeria
title Predictive modelling of COVID-19 confirmed cases in Nigeria
title_full Predictive modelling of COVID-19 confirmed cases in Nigeria
title_fullStr Predictive modelling of COVID-19 confirmed cases in Nigeria
title_full_unstemmed Predictive modelling of COVID-19 confirmed cases in Nigeria
title_short Predictive modelling of COVID-19 confirmed cases in Nigeria
title_sort predictive modelling of covid-19 confirmed cases in nigeria
topic Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7428444/
https://www.ncbi.nlm.nih.gov/pubmed/32835145
http://dx.doi.org/10.1016/j.idm.2020.08.003
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