Abstract
Abstract Objectives The non-linear progression of new infection numbers in a pandemic poses challenges to the evaluation of its management. The tools of complex systems research may aid in attaining information that would be difficult to extract with other means. Methods To study the COVID-19 pandemic, we utilize the reported new cases per day for the globe, nine countries and six US states through October 2020. Fourier and univariate wavelet analyses inform on periodicity and extent of change. Results Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections. Bivariate wavelet analysis and return plots give indications of containment vs. exacerbation. Homogeneity or heterogeneity in the population response, uptick vs. suppression, and worsening or improving trends are discernible, in part by plotting various time lags in three dimensions. Conclusions The analysis of epidemic or pandemic progression with the techniques available for observed (noisy) complex data can extract important characteristics and aid decision making in the public health response.
Highlights
IntroductionThe spread of infectious diseases depends on pathogen factors (virulence), host factors (immunity), and – on the population level – on countermeasures taken by the community
The spread of infectious diseases depends on pathogen factors, host factors, and – on the population level – on countermeasures taken by the community
Evaluating time-lagged data sets of various lag lengths, we find that the autocorrelation function, average mutual information and box counting dimension represent good quantitative readouts for the progression of new infections
Summary
The spread of infectious diseases depends on pathogen factors (virulence), host factors (immunity), and – on the population level – on countermeasures taken by the community. Such measures cover a broad spectrum of possible engagements, and they may be highly consequential for the course of an epidemic or a pandemic (Christakis 2020). The differential equations of the frequently applied SIR (susceptible, infectious, recovered individuals) and SEIR models (susceptible, exposed, infectious, recovered individuals) do not capture the complex nature of epidemiologic progression, even when additional variables are taken into account (Al-Raeei, El-Daher, and Solieva 2021; Stehlé et al 2011), such as the inclusion of quarantine Stochastic transition models and newer mathematical models to characterize imported escaper and asymptomatic patients have displayed similar limitations (Sun and Wang 2020)
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