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Mathematical modeling of COVID-19 epidemic with effect of awareness programs

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019 (COVID-19). The epidemic has largely been controlled in China since March 2020, but continues to inflict severe public health and socioec...

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Autores principales: Musa, Salihu Sabiu, Qureshi, Sania, Zhao, Shi, Yusuf, Abdullahi, Mustapha, Umar Tasiu, He, Daihai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889444/
https://www.ncbi.nlm.nih.gov/pubmed/33619461
http://dx.doi.org/10.1016/j.idm.2021.01.012
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author Musa, Salihu Sabiu
Qureshi, Sania
Zhao, Shi
Yusuf, Abdullahi
Mustapha, Umar Tasiu
He, Daihai
author_facet Musa, Salihu Sabiu
Qureshi, Sania
Zhao, Shi
Yusuf, Abdullahi
Mustapha, Umar Tasiu
He, Daihai
author_sort Musa, Salihu Sabiu
collection PubMed
description Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019 (COVID-19). The epidemic has largely been controlled in China since March 2020, but continues to inflict severe public health and socioeconomic burden in other parts of the world. One of the major reasons for China’s success for the fight against the epidemic is the effectiveness of its health care system and enlightenment (awareness) programs which play a vital role in the control of the COVID-19 pandemic. Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs. In this paper, we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria. Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases, to assess the effect of public awareness on the dynamics of COVID-19 infection. We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020. We find that the epidemic could increase if awareness programs are not properly adopted. We presumed that the effect of awareness programs could be estimated. Further, our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond. Finally, we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic.
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spelling pubmed-78894442021-02-18 Mathematical modeling of COVID-19 epidemic with effect of awareness programs Musa, Salihu Sabiu Qureshi, Sania Zhao, Shi Yusuf, Abdullahi Mustapha, Umar Tasiu He, Daihai 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 Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019 (COVID-19). The epidemic has largely been controlled in China since March 2020, but continues to inflict severe public health and socioeconomic burden in other parts of the world. One of the major reasons for China’s success for the fight against the epidemic is the effectiveness of its health care system and enlightenment (awareness) programs which play a vital role in the control of the COVID-19 pandemic. Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs. In this paper, we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria. Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases, to assess the effect of public awareness on the dynamics of COVID-19 infection. We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020. We find that the epidemic could increase if awareness programs are not properly adopted. We presumed that the effect of awareness programs could be estimated. Further, our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond. Finally, we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic. KeAi Publishing 2021-02-18 /pmc/articles/PMC7889444/ /pubmed/33619461 http://dx.doi.org/10.1016/j.idm.2021.01.012 Text en © 2021 The Authors 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
Musa, Salihu Sabiu
Qureshi, Sania
Zhao, Shi
Yusuf, Abdullahi
Mustapha, Umar Tasiu
He, Daihai
Mathematical modeling of COVID-19 epidemic with effect of awareness programs
title Mathematical modeling of COVID-19 epidemic with effect of awareness programs
title_full Mathematical modeling of COVID-19 epidemic with effect of awareness programs
title_fullStr Mathematical modeling of COVID-19 epidemic with effect of awareness programs
title_full_unstemmed Mathematical modeling of COVID-19 epidemic with effect of awareness programs
title_short Mathematical modeling of COVID-19 epidemic with effect of awareness programs
title_sort mathematical modeling of covid-19 epidemic with effect of awareness programs
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/PMC7889444/
https://www.ncbi.nlm.nih.gov/pubmed/33619461
http://dx.doi.org/10.1016/j.idm.2021.01.012
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