Chaos of COVID-19 Superspreading Events: An Analysis Via a Data-driven Approach-Reference-Cited by-同舟云学术

Chaos of COVID-19 Superspreading Events: An Analysis Via a Data-driven Approach

Published:2023-03-15 Issue:3 Volume:25 Page:514-525
ISSN:0972-0634
Container-title:Journal of Health Management
language:en
Short-container-title:Journal of Health Management

Author:

Ganegoda N. C.¹,Perera S. S. N.²

Affiliation:

1. Department of Mathematics, University of Sri Jayewardenepura, Nugegoda, Sri Lanka

2. Department of Mathematics, University of Colombo, Colombo, Sri Lanka

Abstract

Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative ( ≈difference of total confirmed cases) and the second derivative ( ≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.

Publisher

SAGE Publications

Subject

Health Policy

Link

http://journals.sagepub.com/doi/pdf/10.1177/09720634221150964

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