A COVID-19 model with a nonlinear incidence rate and intervention exit strategies for Kenya

Abstract The coronavirus disease (COVID-19) is a novel infection caused by SARS-CoV-2, a corona virus type that has previously not been seen in humans. The speedy spread of COVID-19 globally has greatly affected the socio-economic environments and health systems. To effectively address this rapid spread, it is imperative to have a clear understanding of the COVID-19 transmission dynamics. In this study we evaluate a COVID-19 epidemic model with a nonlinear incidence function and a saturating treatment rate. We propose an SLIRD data driven COVID 19 model which incorporates individual self initiated behavior change of the susceptible individuals. The proposed model allows the evaluation of the impact of easing intervention measures at specific times. To estimate the model parameters, the model was fitted to the daily reported COVID-19 cases in Kenya. Self initiated behavioral responses by individuals and large scale persistent testing proved to be the most effective measures to flatten the epidemic infection curve. Evidence from the simulations points out that, return to normalcy from COVID -19 pandemic will require individual behavior change in adhering to intervention measures and especially proper wearing of face masks and personal hygiene, alongside effective contact-tracing and active testing. The results have significant impact on the management of COVID-19 and implementation of the intervention exit strategies.