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dc.contributor.authorKinyili, Musyoka
dc.contributor.authorMunyakazi, Justin B
dc.contributor.authorMukhtar, Abdulaziz YA
dc.date.accessioned2022-04-25T07:41:23Z
dc.date.available2022-04-25T07:41:23Z
dc.date.issued2022
dc.identifier.citationKinyili, M. et al. (2022). Mathematical modeling and impact analysis of the use of COVID alert SA app. AIMS Public Health, 9(1), 106-128. 10.3934/publichealth.2022009en_US
dc.identifier.issn2327-8994
dc.identifier.uri10.3934/publichealth.2022009
dc.identifier.urihttp://hdl.handle.net/10566/7316
dc.description.abstractThe human life-threatening novel Severe Acute Respiratory Syndrome Corona-virus-2 (SARS-CoV-2) has lasted for over a year escalating and posing simultaneous anxiety day-by-day globally since its first report in the late December 2019. The scientific arena has been kept animated via continuous investigations in an effort to understand the spread dynamics and the impact of various mitigation measures to keep this pandemic diminished. Despite a lot of research works having been accomplished this far, the pandemic is still deep-rooted in many regions worldwide signaling for more scientific investigations. This study joins the field by developing a modified SEIR (SusceptibleExposed-Infectious-Removed) compartmental deterministic model whose key distinct feature is the incorporation of the COVID Alert SA app use by the general public in prolific intention to control the spread of the epidemic. Validation of the model is performed by fitting the model to the Republic of South Africa’s COVID-19 cases reported data using the Maximum Likelihood Estimation algorithm implemented in fitR package.en_US
dc.language.isoenen_US
dc.publisherAIMS Pressen_US
dc.subjectCovid-19en_US
dc.subjectCOVID alert SA appen_US
dc.subjectDigital-contact-tracing technologyen_US
dc.subjectMitigation measuresen_US
dc.subjectSouth Africaen_US
dc.subjectMathematical modelingen_US
dc.titleMathematical modeling and impact analysis of the use of COVID alert SA appen_US
dc.typeArticleen_US


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