English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Here we propose and implement a generalized mathematical model to find thetime evolution of population in infectious diseases and apply the model tostudy the recent COVID-19 pandemic. Our model at the core is a non-localgeneralization of the widely used Kermack-McKendrick(KM) model where thesusceptible(S) population evolves into two other categories, namelyinfectives(I) and removed(R). This is the well-known SIR model in which wefurther divide both S and I into high and low risk categories. We firstformulate a set of non-local dynamical equations for the time evolution ofdistinct population distributions under this categorization in an attempt todescribe the general scenario of infectious disease progression. We then solvethe non-linear coupled differential equations-(i) numerically by the method ofpropagation, and (ii) a more flexible and versatile cellular automata (CA)simulation which provides a coarse-grained description of the generalizednon-local model. In order to account for multiple factors such as role ofspreaders before containment, we introduce a time dependent rate which appearsto be essential to explain the sudden spikes before the plateau observed inmany cases (for example like China). We demonstrate how this generalizedapproach allows us to handle the effects of (i) time-dependence of therate-constants of spread, (ii) different population density, (iii) the ageratio, (iv) quarantine, (v) lockdown, and (vi) social distancing. Our studyallows us to make certain predictions regarding the nature of spread withrespect to several external parameters, treated as control variables. Analysisof the model clearly shows that due to the strong heterogeneity in the epidemicprocess originating from the distribution of initial infectives, the theorymust be local in character but at the same time connect to a globalperspective.

Dynamical Theory and Cellular Automata Simulations of Pandemic Spread: Understanding Different Temporal Patterns of Infections