Collective infectivity of the pandemic over time and association with vaccine coverage and economic development

Nick James and Max Menzies

Chaos, Solitons & Fractals, 2023, vol. 176, issue C

Abstract: This paper uses new and existing methods to study collective trends across countries throughout the pandemic, with a focus on the multivariate time series of reproduction numbers and vaccine proliferation. We begin with a time-varying analysis of the collective nature of infectivity, where we evaluate the eigenspectrum and collective magnitude of reproduction number time series on a country-by-country basis. Next, we study the topology of this eigenspectrum, measuring the deviation between all points in time, and introduce a graph-theoretic methodology to reveal a clear partition in global infectivity dynamics. Then, we compare countries’ vaccine rollouts with economic indicators such as their GDP and HDI in a collective fashion. We investigate time-varying consistency and determine points in time where there is the greatest discrepancy between these indicators as a whole. Our two primary findings are a considerable increase in collective infectivity in the latter half of the period, and a concave-up (“down then up”) pattern in the collective consistency between vaccine coverage and economic/development indicators across countries.

Keywords: COVID-19 infectivity; Time series analysis; Graph theory; Nonlinear dynamics; Rolling correlation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010408

DOI: 10.1016/j.chaos.2023.114139

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