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Background: COVID-19 disease has persisted since it was declared a global pandemic by the World health organization (WHO) in February 2020. Kenya had notified a total of 203,213 cases, reported 3,931 deaths by July 26, 2021. Currently, Kenya is experiencing the fourth wave of the pandemic that is driven by the delta variant which has become dominant in the country. Non-pharmaceutical and vaccination interventions have been ongoing in the country. With the emergence of new variants, it’s challenging to know the future of the pandemic and its effects on the healthcare systems, vaccination plans, public readiness, and social behavior. To attain realistic predictions, data-driven modeling approaches are paramount. The goal of this study was to model COVID-19 cases in Kenya using the already available data to estimate parameters
Methods: An SEIR compartmental model was developed to predict the daily new cases, severe, critically ill, and death cases of COVID-19. The model had 8 compartments containing sub-populations of: Susceptible, Exposed, Symptomatic Infectious, Asymptomatic Infectious, Hospitalized, Intensive Care Unit, Deaths and Recovered. The model equations were then solved to obtain the number of cases that would be infected on a daily basis beginning March 14th to July 2021.
Results: The results demonstrated evidence of three peaks, first in mid July 2020, second in mid-October 2020 and third in early March of 2021. The number of daily cases in wave 1 was 1128 then increased to 1344 in the second wave and finally a decline was observed in wave 3 with 1057 number of daily of infections. The number of severely, critical and deaths followed a similar pattern. This therefore means that with the absence of herd immunity in the general human population, relaxation of the mitigation measures will eventually result to progression of COVID-19 cases.
Conclusion: Increased vigilance on the COVID-19 curve is indispensable. Continued interventions such as testing, social distancing measures, vaccination, and health facilities preparedness are imperative to ensure that new infections are isolated in real-time. Use of real time data to estimate the pandemic trends is a more realistic way of informing new and appropriate interventions amidst the ongoing pandemic.
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