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Controlling the Spread of COVID-19: Optimal Control Analysis
Chinwendu E Madubueze1, Sambo Dachollom2, Isaac Obiajulu Onwubuya3
1Department of Mathematics/Statistics/Computer Science, University of Agriculture Makurdi, P.M.B. 2373, Markurdi, Nigeria.
Computational and Mathematical Methods in Medicine
|September 23, 2020
Summary
Implementing time-dependent non-pharmacological strategies like quarantine and isolation early in the COVID-19 pandemic significantly reduced infections. Combined interventions were most effective, especially within the first 100 days of the outbreak.
Area of Science:
- Epidemiology
- Mathematical Biology
- Public Health
Background:
- Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic with no approved treatments or vaccines.
- The increasing spread and mortality necessitated urgent non-pharmacological control strategies.
Purpose of the Study:
- To evaluate the impact of time-dependent non-pharmacological interventions (quarantine, isolation, public health education) on COVID-19 transmission dynamics.
- To determine the optimal control strategy for curtailing the spread of COVID-19 using mathematical modeling.
Main Methods:
- Developed and analyzed a mathematical model for COVID-19 transmission with time-dependent interventions.
- Calculated basic and effective reproduction numbers to assess intervention impact.
- Employed Pontryagin's maximum principle for optimal control analysis and sensitivity analysis.
Main Results:
- Time-dependent interventions were more effective than time-independent ones in reducing exposed and infected individuals.
- Interventions demonstrated optimal effectiveness when implemented within the initial 100 days of the outbreak.
- Combined interventions, particularly all three, showed superior results in infection reduction, though not complete eradication.
Conclusions:
- Early and combined deployment of non-pharmacological interventions is crucial for minimizing COVID-19 spread.
- Time-bound strategies are essential for effective pandemic control.
- Mathematical modeling provides valuable insights for optimizing public health interventions.
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