A primer on using mathematics to understand COVID-19 dynamics : modeling, analysis and simulations

dc.contributor.authorGumel, Abba B.
dc.contributor.authorIboi, Enahoro A.
dc.contributor.authorNgonghala, Calistus N.
dc.contributor.authorElbasha, Elamin H.
dc.date.accessioned2022-04-01T10:25:38Z
dc.date.available2022-04-01T10:25:38Z
dc.date.issued2021-11-30
dc.description.abstractThe novel coronavirus (COVID-19) pandemic that emerged from Wuhan city in December 2019 overwhelmed health systems and paralyzed economies around the world. It became the most important public health challenge facing mankind since the 1918 Spanish flu pandemic. Various theoretical and empirical approaches have been designed and used to gain insight into the transmission dynamics and control of the pandemic. This study presents a primer for formulating, analysing and simulating mathematical models for understanding the dynamics of COVID-19. Specifically, we introduce simple compartmental, Kermack-McKendrick-type epidemic models with homogeneously- and heterogeneously-mixed populations, an endemic model for assessing the potential population-level impact of a hypothetical COVID-19 vaccine. We illustrate how some basic non-pharmaceutical interventions against COVID-19 can be incorporated into the epidemic model. A brief overview of other kinds of models that have been used to study the dynamics of COVID-19, such as agent-based, network and statistical models, is also presented. Possible extensions of the basic model, as well as open challenges associated with the formulation and theoretical analysis of models for COVID-19 dynamics, are suggested.en_ZA
dc.description.departmentMathematics and Applied Mathematicsen_ZA
dc.description.librarianam2022en_ZA
dc.description.sponsorshipThe Simons Foundation and the National Science Foundation.en_ZA
dc.description.urihttp://www.keaipublishing.com/idmen_ZA
dc.identifier.citationGumel, A.B., Iboi, E.A., Ngonghala, C.N. et al. 2021, ' A primer on using mathematics to understand COVID-19 dynamics : modeling, analysis and simulations', Infectious Disease Modelling, vol. 6, pp. 148-168.en_ZA
dc.identifier.issn2468-0427
dc.identifier.other10.1016/j.idm.2020.11.005
dc.identifier.urihttp://hdl.handle.net/2263/84764
dc.language.isoenen_ZA
dc.publisherKeAi Communicationsen_ZA
dc.rights© 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license.en_ZA
dc.subjectNon-pharmaceutical interventionsen_ZA
dc.subjectFace masken_ZA
dc.subjectReproduction numberen_ZA
dc.subjectCOVID-19 pandemicen_ZA
dc.subjectCoronavirus disease 2019 (COVID-19)en_ZA
dc.subjectSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)en_ZA
dc.titleA primer on using mathematics to understand COVID-19 dynamics : modeling, analysis and simulationsen_ZA
dc.typeArticleen_ZA

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