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Analysis of COVID-19 and comorbidity co-infection model with optimal control
Andrew Omame1, Ndolane Sene2, Ikenna Nometa3
1Department of Mathematics Federal University of Technology Owerri Owerri Nigeria.
Insights
This study models COVID-19 reinfection, finding that diabetes mellitus increases complications. Preventing infection in those with diabetes is the most cost-effective control strategy.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- COVID-19 poses significant health risks, with comorbidities like diabetes mellitus potentially exacerbating complications.
- Understanding disease dynamics, including reinfection and the impact of underlying health conditions, is crucial for effective control.
Purpose of the Study:
- To develop and analyze a mathematical model for COVID-19 dynamics incorporating reinfection.
- To assess the impact of diabetes mellitus comorbidity on COVID-19 complications and disease peaks.
- To evaluate the cost-effectiveness of various COVID-19 control strategies.
Main Methods:
- Development and analysis of a mathematical model for COVID-19 with reinfection.
- Simulation of the model using data from Lagos, Nigeria.
- Application of optimal control and cost-effectiveness analysis.
Main Results:
- The model exhibits backward bifurcation due to increased susceptibility in comorbid individuals and reinfection rates.
- Decreasing reinfection rates among recovered individuals reduces infection peaks.
- Preventing COVID-19 infection in individuals with diabetes mellitus is identified as the most cost-effective strategy.
Conclusions:
- Diabetes mellitus significantly impacts COVID-19 dynamics and complications.
- Reinfection rates play a critical role in disease peaks.
- Targeted prevention strategies for high-risk groups, such as those with diabetes, are essential for efficient COVID-19 control.
Abstract:
In this work, we develop and analyze a mathematical model for the dynamics of COVID-19 with re-infection in order to assess the impact of prior comorbidity (specifically, diabetes mellitus) on COVID-19 complications. The model is simulated using data relevant to the dynamics of the diseases in Lagos, Nigeria, making predictions for the attainment of peak periods in the presence or absence of comorbidity. The model is shown to undergo the phenomenon of backward bifurcation caused by the parameter accounting for increased susceptibility to COVID-19 infection by comorbid susceptibles as well as the rate of reinfection by those who have recovered from a previous COVID-19 infection. Simulations of the cumulative number of active cases (including those with comorbidity), at different reinfection rates, show infection peaks reducing with decreasing reinfection of those who have recovered from a previous COVID-19 infection. In addition, optimal control and cost-effectiveness analysis of the model reveal that the strategy that prevents COVID-19 infection by comorbid susceptibles is the most cost-effective of all the control strategies for the prevention of COVID-19.
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