Mukherjee, S and Mondal, S and Bagchi, B (2021) Stochastic formulation of multiwave pandemic: decomposition of growth into inherent susceptibility and external infectivity distributions. In: Journal of Chemical Sciences, 133 (4).
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Abstract
Many known and unknown factors play significant roles in the persistence of an infectious disease, but two that are often ignored in theoretical modelling are the distributions of (i) inherent susceptibility (�inh) and (ii) external infectivity (ιext), in a population. While the former is determined by the immunity of an individual towards a disease, the latter depends on the exposure of a susceptible person to the infection. We model the spatio-temporal propagation of a pandemic as a chemical reaction kinetics on a network using a modified SAIR (Susceptible-Asymptomatic-Infected-Removed) model to include these two distributions. The resulting integro-differential equations are solved using Kinetic Monte Carlo Cellular Automata (KMC-CA) simulations. Coupling between �inh and ιext are combined into a new parameter Ω, defined as Ω = �inh� ιext; infection occurs only if the value of Ω is greater than a Pandemic Infection Parameter (PIP), Ω. Not only does this parameter provide a microscopic viewpoint of the reproduction number R0 advocated by the conventional SIR model, but it also takes into consideration the viral load experienced by a susceptible person. We find that the neglect of this coupling could compromise quantitative predictions and lead to incorrect estimates of the infections required to achieve the herd immunity threshold. Graphical abstract: Figure not available: see fulltext. © 2021, Indian Academy of Sciences.
| Item Type: | Journal Article |
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| Publication: | Journal of Chemical Sciences |
| Publisher: | Springer |
| Additional Information: | The copyright for this article belongs to Authors |
| Keywords: | Cell proliferation; Cellular automata; Diseases; Integrodifferential equations; Monte Carlo methods; Reaction kinetics; Stochastic models; Stochastic systems, Cellular automatons; Infectious disease; Infectivity; Pandemic; SARS-CoV-2; SIR model; Spatio-temporal; Stochastic formulation; Susceptibility; Theoretical modeling, SARS |
| Department/Centre: | Division of Chemical Sciences > Solid State & Structural Chemistry Unit |
| Date Deposited: | 21 Dec 2021 05:50 |
| Last Modified: | 21 Dec 2021 05:50 |
| URI: | http://eprints.iisc.ac.in/id/eprint/70686 |
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