Modeling the Dynamics of COVID-19 in Nigeria

被引：23

作者：

Adewole M.O. ^{[1
]}

Onifade A.A. ^{[1
]}

Abdullah F.A. ^{[2
]}

Kasali F. ^{[1
]}

Ismail A.I.M. ^{[2
]}

机构：

[1] Department of Computer Science and Mathematics, Mountain Top University, Prayer City, Ogun State

[2] School of Mathematical Sciences, Universiti Sains Malaysia, Penang

来源：

International Journal of Applied and Computational Mathematics | 2021年 / 7卷 / 3期

基金：

英国科研创新办公室;

关键词：

Contact tracing; COVID-19; Mass testing; Pontryagin’s maximum principle; Prevention guidelines;

D O I：

10.1007/s40819-021-01014-5

中图分类号：

学科分类号：

摘要：

To understand the dynamics of COVID-19 in Nigeria, a mathematical model which incorporates the key compartments and parameters regarding COVID-19 in Nigeria is formulated. The basic reproduction number is obtained which is then used to analyze the stability of the disease-free equilibrium solution of the model. The model is calibrated using data obtained from Nigeria Centre for Disease Control and key parameters of the model are estimated. Sensitivity analysis is carried out to investigate the influence of the parameters in curtailing the disease. Using Pontryagin’s maximum principle, time-dependent intervention strategies are optimized in order to suppress the transmission of the virus. Numerical simulations are then used to explore various optimal control solutions involving single and multiple controls. Our results suggest that strict intervention effort is required for quick suppression of the disease. © 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited.

引用

共 47 条

[1]

ACAPS: COVID-19 in Nigeria Vulnerabilities to COVID-19 and Containment Measures, (2020)

[2]

Adegboye O.A., Adekunle A.I., Gayawan E., Early transmission dynamics of novel coronavirus (COVID-19) in Nigeria, Int. J. Environ. Res. Public Health, 17, 3054, pp. 1-10, (2020)

[3]

Agusto F.B., Leite M.C.A., Optimal control and cost-effective analysis of the 2017 meningitis outbreak in Nigeria, Infect Dis Model, 4, pp. 161-187, (2019)

[4]

Blower S.M., Dowlatabadi H., Sensitivity and uncertainty analysis of complex-models of disease transmission: an HIV model, as an example, Int. Stat. Rev., 62, pp. 229-243, (1994)

[5]

CDC 2019: Novel Coronavirus, (2020)

[6]

Chen D., Xu W., Lei Z., Huang Z., Liu J., Gao Z., Peng L., Recurrence of positive SARS-CoV-2 RNA in COVID-19: a case report, Int. J. Infect. Dis., 93, pp. 297-299, (2020)

[7]

Diekmann O., Heesterbeek J.A.P., Metz J.A.J., On the definition and the computation of the basic reproduction ratio R in models for infectious diseases in heterogeneous populations, J. Math. Biol., 28, 4, pp. 365-382, (1990)

[8]

Eikenberry S.E., Mancuso M., Iboi E., Phan T., Eikenberry K., Kuang Y., Kostelich E., Gumel A.B., To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic, Infect Dis Model, 5, pp. 293-308, (2020)

[9]

Ferguson N.M., Laydon D., Nedjati-Gilani G., Imai N., Ainslie K., Baguelin M., Bhatia S., Boonyasiri A., Cucunuba Z., Cuomo-Dannenburg G., Dighe A., Dorigatti I., Fu H., Gaythorpe K., Green W., Hamlet A., Hinsley W., Okell L.C., Elsland S.V., Thompson H., Verity R., Volz E., Wang H., Wang Y., Walker P.G.T., Walters C., Winskill P., Whittaker C., Donnelly C.A., Riley S., Ghani A.C., Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand

[10]

Geering H.P., Optimal Control with Engineering Applications, (2007)

← 1 2 3 4 5 →