Reliably quantifying the evolving worldwide dynamic state of the COVID-19 outbreak from death records, clinical parametrization, and demographic data
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Abstract
The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time.
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SciScore for 10.1101/2020.11.26.20239434: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A prominent feature of the approach is its ability to provide reliable results even for low death counts, which overcomes the major limitations of choosing between unreliable infection case data (highly dependent on testing rates) or noisy death counts as input to the inference problem [9]. The approach assumes a general …
SciScore for 10.1101/2020.11.26.20239434: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A prominent feature of the approach is its ability to provide reliable results even for low death counts, which overcomes the major limitations of choosing between unreliable infection case data (highly dependent on testing rates) or noisy death counts as input to the inference problem [9]. The approach assumes a general age-stratified IFR. In general, these quantities are expected to depend potentially on specific features of the population and the medical care facilities available. The available studies show a minimal variability among different countries and other locations that reported on prevalence [23]. It also assumes an age-uniform exposure (attack rate), which is consistent with data for other respiratory diseases [5] and holds to a large extent when there is information available for COVID-19 [19, 24, 25]. We have also assumed a constant generation interval typical of non-confinement locations, which has been observed to shorten in some cases by NPIs [26]. Prevalence studies can also depend on the diminishing antibody levels after infection [14, 27], collecting and processing specimens for analysis [28], and potential biases towards specific population groups [19]. In addition, there might be a degree of under-reporting of COVID-19 deaths, as suggested by excess mortality not attributable to other causes than COVID-19 [21]. Our results show that all these potential deviations on the assumptions, on the data, and on prevalence studies collectively have only a restri...
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: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- 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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