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. 2021 Jul:12:e00844.
doi: 10.1016/j.sciaf.2021.e00844. Epub 2021 Jul 12.

Situation assessment and natural dynamics of COVID-19 pandemic in Nigeria, 31 May 2020

Ayo Stephen Adebowale  1 , Adeniyi Francis Fagbamigbe  1 , Joshua Odunayo Akinyemi  1 , Kazeem Olalekan Obisesan  2 , Emmanuel Jolaoluwa Awosanya  3 , Rotimi Felix Afolabi  1   4 , Selim Adewale Alarape  3 , Sunday Olawale Obabiyi  5
Affiliations

Situation assessment and natural dynamics of COVID-19 pandemic in Nigeria, 31 May 2020

Ayo Stephen Adebowale et al. Sci Afr. 2021 Jul.

Abstract

Background: The coronavirus disease (COVID-19) remains a global public health issue due to its high transmission and case fatality rate. There is apprehension on how to curb the spread and mitigate the socio-economic impacts of the pandemic, but timely and reliable daily confirmed cases' estimates are pertinent to the pandemic's containment. This study therefore conducted a situation assessment and applied simple predictive models to explore COVID-19 progression in Nigeria as at 31 May 2020.

Methods: Data used for this study were extracted from the websites of the European Centre for Disease Control (World Bank data) and Nigeria Centre for Disease Control. Besides descriptive statistics, four predictive models were fitted to investigate the pandemic natural dynamics.

Results: The case fatality rate of COVID-19 was 2.8%. A higher number of confirmed cases of COVID-19 was reported daily after the relaxation of lockdown than before and during lockdown. Of the 36 states in Nigeria, including the Federal Capital Territory, 35 have been affected with COVID-19. Most active cases were in Lagos (n = 4064; 59.2%), followed by Kano (n = 669; 9.2%). The percentage of COVID-19 recovery in Nigeria (29.5%) was lower compared to South Africa (50.3%), but higher compared to Kenya (24.1%). The cubic polynomial model had the best fit. The projected value for COVID-19 cumulative cases for 30 June 2020 in Nigeria was 27,993 (95% C.I: 27,001-28,986).

Conclusion: The daily confirmed cases of COVID-19 are increasing in Nigeria. Increasing testing capacity for the disease may further reveal more confirmed cases. As observed in this study, the cubic polynomial model currently offers a better prediction of the future COVID-19 cases in Nigeria.

Keywords: COVID-19; Growth curve, polynomial model; Nigeria; Simple mathematical model.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig 1
Fig. 1
Daily COVID-19 confirmed cases in Nigeria, as at 31 May 2020.
Fig 2
Fig. 2
Distribution of total confirmed cases of COVID-19, as at 31 May 2020.
Fig 3
Fig. 3
Percentage of total positive COVID-19 tests reported in Nigeria and selected countries.
Fig 4
Fig. 4
Percentage of recovered COVID-19 confirmed cases and case fatality rate (CFR) in Nigeria and elsewhere.
Fig 5
Fig. 5
COVID-19 observed and fitted cumulative cases - as at 31 May 2020.
Fig 6
Fig. 6
Observed and projected cumulative cases of COVID-19, as at 31 May 2020.
See this image and copyright information in PMC

References

    1. Nigeria Centre for Disease Control. 2020. https://ncdc.gov.ng/diseases/sitreps/?cat=14&name=An update of COVID-19 outbreak in Nigeria. Accessed April 2020.
    1. WHO. Coronavirus disease 2019 (COVID-19) situation reports. 2020.
    1. Valeri L, Patterson-Lomba O, Gurmu Y, Ablorh A, Bobb J, Townes FW. et al. Predicting Sub-national ebola virus disease epidemic dynamics from socio-demographic indicators. PLoS One. 2016;11(10) Selvey LA, editor. - PMC - PubMed
    1. Hallett TB, Gregson S, Mugurungi O, Gonese E, Garnett GP. Assessing evidence for behaviour change affecting the course of HIV epidemics: a new mathematical modelling approach and application to data from Zimbabwe. Epidemics. 2009;1(2):108–117. - PubMed
    1. Siettos CI, Russo L. Mathematical modeling of infectious disease dynamics. Virulence. 2013;4(4):295–306. - PMC - PubMed