Mathematical Modelling of COVID-19 Transmission in Kenya: A Model with Reinfection Transmission Mechanism
Isaac Mwangi Wangari1,2, Stanley Sewe2, George Kimathi2
1Bomet University College, School of Pure and Applied Sciences, Department of Mathematics and Computer Science, P.O. Box 701 20400, Bomet, Kenya.
Insights
COVID-19 reinfection may increase asymptomatic cases and critical illness, leading to more deaths. Wearing face masks is more effective than social distancing at curbing COVID-19 spread.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Diseases
Background:
- Coronavirus Disease 2019 (COVID-19) reinfection is possible, impacting pandemic dynamics.
- Mathematical models are crucial for understanding and predicting infectious disease spread.
- Stratifying infectious populations is key to accurate epidemiological modeling.
Purpose of the Study:
- To develop and analyze a mathematical model for COVID-19 dynamics, incorporating reinfection.
- To investigate the impact of COVID-19 reinfection on pandemic trajectories.
- To evaluate the effectiveness of non-pharmaceutical interventions (NPIs) and detection strategies.
Main Methods:
- Developed a compartmental mathematical model for COVID-19, stratifying populations into asymptomatic, mild symptomatic, and severe symptomatic individuals.
- Fitted the model to the COVID-19 dataset from Kenya to estimate parameter values.
- Conducted numerical simulations to explore the effects of reinfection and NPIs.
Main Results:
- COVID-19 reinfection is predicted to increase asymptomatic cases, leading to more mild and severe symptomatic individuals and a rise in cumulative deaths.
- Wearing face masks demonstrates a more significant reduction in COVID-19 prevalence compared to maintaining social distance.
- Enhanced detection rates of asymptomatic cases through contact tracing and testing can substantially decrease COVID-19 surges, particularly for critically ill patients.
Conclusions:
- COVID-19 reinfection poses a significant threat, potentially prolonging the pandemic and increasing mortality.
- Non-pharmaceutical interventions, especially mask-wearing and robust contact tracing, are vital for controlling COVID-19 spread.
- Mathematical modeling provides valuable insights for public health strategies against emerging infectious diseases like COVID-19.
Abstract:
In this study we propose a Coronavirus Disease 2019 (COVID-19) mathematical model that stratifies infectious subpopulations into: infectious asymptomatic individuals, symptomatic infectious individuals who manifest mild symptoms and symptomatic individuals with severe symptoms. In light of the recent revelation that reinfection by COVID-19 is possible, the proposed model attempt to investigate how reinfection with COVID-19 will alter the future dynamics of the recent unfolding pandemic. Fitting the mathematical model on the Kenya COVID-19 dataset, model parameter values were obtained and used to conduct numerical simulations. Numerical results suggest that reinfection of recovered individuals who have lost their protective immunity will create a large pool of asymptomatic infectious individuals which will ultimately increase symptomatic individuals with mild symptoms and symptomatic individuals with severe symptoms (critically ill) needing urgent medical attention. The model suggests that reinfection with COVID-19 will lead to an increase in cumulative reported deaths. Comparison of the impact of non pharmaceutical interventions on curbing COVID19 proliferation suggests that wearing face masks profoundly reduce COVID-19 prevalence than maintaining social/physical distance. Further, numerical findings reveal that increasing detection rate of asymptomatic cases via contact tracing, testing and isolating them can drastically reduce COVID-19 surge, in particular individuals who are critically ill and require admission into intensive care.
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