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

TL;DR

This study analyzes global pandemic infectivity trends over time, examining how they relate to vaccine coverage and economic development, revealing increased collective infectivity and fluctuating consistency with economic indicators.

Contribution

It introduces a graph-theoretic approach to analyze the eigenspectrum of infectivity data and compares vaccine rollout with economic indicators across countries over time.

Findings

Increase in collective infectivity in the latter half of the pandemic period.

A concave-up pattern in the relationship between vaccine coverage and economic indicators.

Identification of key time points with greatest discrepancies between vaccine coverage and economic development.

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.