Spread of COVID-19 Pandemic in Zambia: A Mathematical Model

Eddie M. Mulenga

doi:10.29333/aquademia/8375

Full Text (PDF)

Eddie M. Mulenga ¹ ^*

More Detail

¹ PhD Candidate, School of Doctoral Studies, University of Valladolid, SPAIN^* Corresponding Author

Abstract

There has been a cumulative increase in the number of COVID-19 confirmed cases, recoveries as well as deaths in Zambia as declared by the Ministry of Health on regular basis. Based on the available statistical information, this article will discuss a mathematical model concerning the spread of COVID-19 in Zambia. To predict the spread of COVID-19 in Zambian provinces, a multiple regression analysis describing the inter-play of factors influencing the increase in the number of cases is used to formalize the relationship. To ascertain the accuracy of the mathematical model used, exponential graphs regarding the spread of coronavirus (COVID-19) since its arrival over time in Zambia were obtained.

Keywords

COVID-19
Mathematical model
predictor variables
regression model
infection rate
fatality rate
susceptible population

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

AQUADEMIA, Volume 4, Issue 2, 2020, Article No: ep20019

https://doi.org/10.29333/aquademia/8375

Publication date: 30 Jun 2020

Article Views: 3080

Article Downloads: 2047

Open Access References How to cite this article

References

Amanso, O., Aguegboh, N., Achimugwu, P., Okeke, C., Chukwuemeka, B., Nnamaga, K., & Oshilim, E. (2020). Mathematical model of the early incidence and spread of COVID-19 in Nigeria combined with control measure. International Journal of Scientific and Engineering Research, 11(4), 1110.
Basilaia, G., & Kvavadze, D. (2020). Transition to Online Education in Schools during a SARS-CoV-2 Coronavirus (COVID-19) Pandemic in Georgia. Pedagogical Research, 5(4), em0060. https://doi.org/10.29333/pr/7937
Baud, D., Qi, X., Nielsen-Saines, K., Musso, D., Pomar, L., & Favre, G. (2020). Real estimates of mortality following COVID-19 infection. The Lancet Infectious Diseases, 20(July 2020), 773. https://doi.org/10.1016/S1473-3099(20)30195-X
Cakir, Z., & Savas, H. B. (2020). A Mathematical Modelling Approach in the Spread of the Novel 2019 Coronavirus SARS-CoV-2 (COVID-19) Pandemic. Electronic Journal of General Medicine, 17(4), em205. https://doi.org/10.29333/ejgm/7861
Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Rev. Mod. Phys., 81(2), 591-646. https://doi.org/10.1103/RevModPhys.81.591
Crokidakis, N. (In Press). Modeling the early evolution of the COVID-19 in Brazil: results from a Susceptible-Infectious-Quarantined-Recovered (SIQR) model. International Journal of Modern Physics C. https://doi.org/10.1142/S0129183120501351
Donsimoni, J. R., Glawion, R., Plachter, B., & Wälde, K. (2020). Projecting the spread of COVID-19 for Germany. German Economic Review, 21(2), 181-216. https://doi.org/10.1515/ger-2020-0031
Helbing, D. (2010). Quantitative Sociodynamics. Stochastic Methods and Models of Social Interaction Processes. Springer, Berlin. https://doi.org/10.1007/978-3-642-11546-2
Helbing, D., Brockmann, D., Chadefaux, T., Donnay, K., Blanke, U., Woolley-Meza, O., Moussaid, M., Johansson, A., Krause, J., Schutte, S., & Perc, M. (2015). Saving Human Lives: What Complexity Science and Information Systems can Contribute. Journal of Statistical Physics, 158(3), 735-781. https://doi.org/10.1007/s10955-014-1024-9
Jiang, R. (2020). Inside China and COVID-19: Questions and answers. Travel Medicine and Infectious Disease, 34(March-April 2020), 101640. https://doi.org/10.1016/j.tmaid.2020.101640
Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., ... Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497. https://doi.org/10.1126/science.abb4218
Koltsova, E., Kurkina, E., & Vasetsky, A. (2020). Mathematical modeling of the spread of COVID-19 in Moscow. Computational Nanotechnology, 7, 99-105. https://doi.org/10.33693/2313-223X-2020-7-1-99-105
Lucero, J. (2020). Mathematical modeling of COVID-19 in Brazil. https://doi.org/10.13140/RG.2.2.25266.56004
McCloskey, B., Zumla, A., Ippolito, G., Blumberg, L., Arbon, P., Cicero, A., Endericks, T., Lim, P., & Borodina, M. (2020). Mass gathering events and reducing further global spread of COVID-19: a political and public health dilemma. The Lancet, 395(April 4, 2020), 1096-1099. https://doi.org/10.1016/S0140-6736(20)30681-4
Perc, M., Miksić, N., Slavinec, M., & Stozer, A. (2020). Forecasting COVID-19. Frontiers in Physics, 8, 127. https://doi.org/10.3389/fphy.2020.00127
Roy, S., & Roy Bhattacharya, K. (2020). Spread of COVID-19 in India: A Mathematical Model. Journal of Science and Technology, 5(3), 41-47. https://doi.org/10.46243/jst.2020.v5.i3.pp41-47
Short, M. B., Brantingham, P. J., Bertozzi, A. L., & Tita, G. E. (2010). Dissipation and displacement of hotspots in reaction-diffusion models of crime. Proc. Natl. Acad. Sci. USA, 107(9), 3961-3965. https://doi.org/10.1073/pnas.0910921107
Shrivastava, S., & Shrivastava, P. (2020). Resorting to mathematical modelling approach to contain the coronavirus disease 2019 (COVID-19) outbreak. Journal of Acute Disease, 9(2), 49-50. https://doi.org/10.4103/2221-6189.281311
Sugiyanto, S., & Abrori, M. (2020). A Mathematical Model of the Covid-19 Cases in Indonesia (Under and Without Lockdown Enforcement). Biology, Medicine, & Natural Product Chemistry, 9(1), 15-19. https://doi.org/10.14421/biomedich.2020.91.15-19
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M., Li, B., Cai, J., Xu, B., Yang, Q., Wang, B., Yang, P., Cui, Y., Song, Y., Zheng, P., Wang, Q., Bjornstad, O., Yang, R., Grenfell, B., & Dye, C. (2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science, 368(6491), eabb6105. https://doi.org/10.1126/science.abb6105
Weidlich, W. (2000). Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. Tyler and Francis, London.

How to cite this article

APA

Mulenga, E. M. (2020). Spread of COVID-19 Pandemic in Zambia: A Mathematical Model. Aquademia, 4(2), ep20019. https://doi.org/10.29333/aquademia/8375

Vancouver

Mulenga EM. Spread of COVID-19 Pandemic in Zambia: A Mathematical Model. AQUADEMIA. 2020;4(2):ep20019. https://doi.org/10.29333/aquademia/8375

AMA

Mulenga EM. Spread of COVID-19 Pandemic in Zambia: A Mathematical Model. AQUADEMIA. 2020;4(2), ep20019. https://doi.org/10.29333/aquademia/8375

Chicago

Mulenga, Eddie M.. "Spread of COVID-19 Pandemic in Zambia: A Mathematical Model". Aquademia 2020 4 no. 2 (2020): ep20019. https://doi.org/10.29333/aquademia/8375

Harvard

Mulenga, E. M. (2020). Spread of COVID-19 Pandemic in Zambia: A Mathematical Model. Aquademia, 4(2), ep20019. https://doi.org/10.29333/aquademia/8375

MLA

Mulenga, Eddie M. "Spread of COVID-19 Pandemic in Zambia: A Mathematical Model". Aquademia, vol. 4, no. 2, 2020, ep20019. https://doi.org/10.29333/aquademia/8375