Ghosh, I and Martcheva, M (2021) Modeling the effects of prosocial awareness on COVID-19 dynamics: Case studies on Colombia and India. In: Nonlinear Dynamics, 104 . pp. 4681-4700.
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Abstract
The ongoing COVID-19 pandemic has affected most of the countries on Earth. It has become a pandemic outbreak with more than 50 million confirmed infections and above 1 million deaths worldwide. In this study, we consider a mathematical model on COVID-19 transmission with the prosocial awareness effect. The proposed model can have four equilibrium states based on different parametric conditions. The local and global stability conditions for awareness-free, disease-free equilibrium are studied. Using Lyapunov function theory and LaSalle invariance principle, the disease-free equilibrium is shown globally asymptotically stable under some parametric constraints. The existence of unique awareness-free, endemic equilibrium and unique endemic equilibrium is presented. We calibrate our proposed model parameters to fit daily cases and deaths from Colombia and India. Sensitivity analysis indicates that the transmission rate and the learning factor related to awareness of susceptibles are very crucial for reduction in disease-related deaths. Finally, we assess the impact of prosocial awareness during the outbreak and compare this strategy with popular control measures. Results indicate that prosocial awareness has competitive potential to flatten the COVID-19 prevalence curve. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
| Item Type: | Journal Article |
|---|---|
| Publication: | Nonlinear Dynamics |
| Publisher: | Springer Science and Business Media B.V. |
| Additional Information: | The copyright for this article belongs to Authors |
| Keywords: | Lyapunov functions; Sensitivity analysis; Transmissions, Disease-free equilibrium; Endemic equilibrium; Globally asymptotically stable; LaSalle invariance principle; Local and global stabilities; Parametric conditions; Parametric constraints; Transmission rates, Epidemiology |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 27 Jul 2021 10:41 |
| Last Modified: | 27 Jul 2021 10:41 |
| URI: | http://eprints.iisc.ac.in/id/eprint/68961 |
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