Complex systems analysis informs on the spread of COVID-19
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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.
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SciScore for 10.1101/2021.01.06.425544: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Such a progression is represented in a declining fractal dimension, and the transition from fluctuations (often associated with a torus attractor) toward limitation of new cases is expected to reduce the autocorrelation. One constraint of complex systems analysis is the need for large data sets. In this regard, the availability of about …
SciScore for 10.1101/2021.01.06.425544: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Such a progression is represented in a declining fractal dimension, and the transition from fluctuations (often associated with a torus attractor) toward limitation of new cases is expected to reduce the autocorrelation. One constraint of complex systems analysis is the need for large data sets. In this regard, the availability of about 230 data points (daily new cases March through October 2020) for each geographic area in this study is somewhat low. The robustness of pertinent studies increases with larger data sets over time. Reporting errors could have a non-trivial impact, and may be reflected in the frequent occurrence of a peak at 7 days in the spectral analysis (possibly indicating weekly totals). This problem can be addressed by utilizing moving averages. The homogeneity or heterogeneity in management by the community under study determines the noise level. The worldwide numbers of new infections have a lot of background due to varying patterns across countries.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 32, 25, 30 and 23. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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