Mathematical modelling of COVID-19 transmission and control strategies in the population of Bauchi State, Nigeria

Published: October 5, 2020
Abstract Views: 56
PDF: 26
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

The novel SARS-COV-2 has since been declared a pandemic by the World Health Organization (WHO). The virus has spread from Wuhan city in China in December 2019 to no fewer than 200 countries as at June 2020 and still counting. Nigeria is currently experiencing a rapid spread of the virus amidst weak health system and more than 80% of population leaving on less than 1USD per day. To help understand the behavior of the virus in resource limited settings, we modelled the outbreak of COVID-19 and effects of control strategies in Bauchi state at north-eastern Nigeria. Using the real data of Bauchi state COVID-19 project, this research work extends the epidemic SEIR model by introducing new parameters based on the transmission dynamics of the novel COVID-19 pandemic and preventive measures. The total population of Bauchi State at the time of the study, given by is compartmentalized into five (5) different compartments as follows: Susceptible (S), Exposed (E), Infectious (I), Quarantined (Q) and Recovered (R). The new model is SEIQR. N = S → E → I → Q → R Data was collected by accessing Bauchi state electronic database of COVID-19 project to derive all the model parameters, while analysis and model building was done using Maple software. At the time of this study, it was found that the reproduction number R, for COVID-19 in Bauchi state, is 2.6 × 10-5. The reproduction number R decreased due to the application of control measures. The compartmental SEIRQ model in this study, which is a deterministic system of linear differential equations, has a continuum of disease-free equilibria, which is rigorously shown to be locallyasymptotically stable as the epidemiological threshold, known as the control reproduction number R= 0.0000026 is less than unity. The implication of this study is that the COVID-19 pandemic can be effectively controlled in Bauchi, since is R<1. Contact tracing and isolation must be increased as the models shows, the rise in infected class is a sign of high vulnerability of the population. Unless control measures are stepped up, despite high rate of recovery as shown by this study, infection rate will keep increasing as currently there is a no vaccine for COVID-19.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Bauchi MOH. (2020). COVID-19 Update, EOC, Bauchi State MOH.
Butcher, J. (2005). Runge–Kutta methods for ordinary differential equations. Runge–Kutta Methods for Ordinary Differential Equations. https://www.math.auckland.ac.nz/~butcher/CONFERENCES/JAPAN/KYUSHU/kyushu-slides.pdf
Cucinotta, D., & Vanelli, M. (2020). WHO Declares COVID-19 a Pandemic. Acta Bio Med, 91(1), 157–160. https://www.mattioli1885journals.com/index.php/actabiomedica/article/view/9397
Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990). On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. Journal of Mathematical Biology, 28(4), 365–382. https://doi.org/10.1007/BF00178324
Diekmann, O., Heesterbeek, J. A. P., & Roberts, M. G. (2010). The construction of next-generation matrices for compartmental epidemic models. Journal of the Royal Society Interface, 7(47), 873–885. https://doi.org/10.1098/rsif.2009.0386
Grundmann, H., & Hellriegel, B. (2006). Mathematical modelling: A tool for hospital infection control. In Lancet Infectious Diseases. https://doi.org/10.1016/S1473-3099(05)70325-X
Liang, K. (2020). Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS. Infection, Genetics and Evolution. https://doi.org/10.1016/j.meegid.2020.104306
Mandal, S., Bhatnagar, T., Arinaminpathy, N., Agarwal, A., Chowdhury, A., Murhekar, M., Gangakhedkar, R. R., & Sarkar, S. (2020). Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach. The Indian Journal of Medical Research. https://doi.org/10.4103/ijmr.IJMR_504_20
NCDC. (2020). COVID-19 Nigeria. COVID-19 Nigeria Update. https://covid19.ncdc.gov.ng/
Rothan, H. A., & Byrareddy, S. N. (2020). The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. In Journal of Autoimmunity. https://doi.org/10.1016/j.jaut.2020.102433
Tuite, A. R., Fisman, D. N., & Greer, A. L. (2020). Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada. Canadian Medical Association Journal. https://doi.org/10.1503/cmaj.200476
Wordmeters. (2020). COVID-19 CORONAVIRUS PANDEMIC. https://www.worldometers.info/coronavirus/
Wu, Y. C., Chen, C. S., & Chan, Y. J. (2020). The outbreak of COVID-19: An overview. In Journal of the Chinese Medical Association. https://doi.org/10.1097/JCMA.0000000000000270
Zhang, J., Litvinova, M., Wang, W., Wang, Y., Deng, X., Chen, X., Li, M., Zheng, W., Yi, L., Chen, X., Wu, Q., Liang, Y., Wang, X., Yang, J., Sun, K., Longini, I. M., Halloran, M. E., Wu, P., Cowling, B. J., … Yu, H. (2020). Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. The Lancet Infectious Diseases. https://doi.org/10.1016/S1473-3099(20)30230-9
Zhongwei, J., & Zuhong, L. (2020). Modelling COVID-19 transmission: from data to intervention. The Lancet. Infectious Diseases. https://doi.org/10.1016/S1473-3099(20)30258-9

How to Cite

Abdu Misau, Y. ., Nansak, N. ., Maigoro, A. ., Malami, S. ., Mogere, D. ., Mbaruk, S. ., … Usman, S. U. . (2020). Mathematical modelling of COVID-19 transmission and control strategies in the population of Bauchi State, Nigeria. Annals of African Medical Research, 3(1). https://doi.org/10.4081/aamr.2020.120