Towards the global equilibrium of COVID‐19: Statistical analysis of country‐level data
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Abstract
In our study, we explore the COVID‐19 dynamics to test whether the virus has reached its equilibrium point and to identify the main factors explaining the current R and case fatality rate (CFR) variability across countries. We present a retrospective study of publicly available country‐level data from 50 countries having the highest number of confirmed COVID–19 cases at the end of September 2021. The mean values of country‐level moving averages of R and CFR went down respectively from 1.118 and 6.3% on June 30, 2020 to 1.083 and 3.6% on September 30, 2020 and to 1.015 and 1.8% by September 30, 2021. In parallel, the 10%–90% inter‐percentile range of R and CFR moving averages decreased, respectively, from 0.288 and 13.3% on June 30, 2020, to 0.151 and 7.7% on September 30, 2020, and to 0.107 and 3.3% by September 30, 2021. The slow decrease in the country‐level moving averages of R, approaching the level of 1.0 and accompanied by repeated outbreaks (“waves”) in various countries, may indicate that COVID‐19 has reached its point of stable endemic equilibrium. A regression analysis implies that only a prohibitively high level of herd immunity (about 63%) may stop the endemic by reaching a stable disease‐free equilibrium. It also appears that fully vaccinating about 70% of a country's population should be sufficient for bringing the CFR close to the level of the seasonal flu (about 0.1%). Thus, while the currently available vaccines prove to be effective in reducing the mortality from the existing COVID‐19 variants, they are unlikely to stop the spread of the virus in the foreseeable future. It is noteworthy that government measures restricting people's behavior (such as lockdowns) were not found to have statistically significant effects in the analyzed data.
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SciScore for 10.1101/2021.08.23.21262413: (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
Software and Algorithms Sentences Resources Pearson product-moment correlation coefficients between country-level variables were calculated using the scipy.stats.pearsonr function. scipysuggested: (SciPy, RRID:SCR_008058)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:Strengths and limitations: As opposed to numerous works analyzing the data accumulated during the first months of the pandemic, this is the first …
SciScore for 10.1101/2021.08.23.21262413: (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
Software and Algorithms Sentences Resources Pearson product-moment correlation coefficients between country-level variables were calculated using the scipy.stats.pearsonr function. scipysuggested: (SciPy, RRID:SCR_008058)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:Strengths and limitations: As opposed to numerous works analyzing the data accumulated during the first months of the pandemic, this is the first study that explores the long–term trends in country–level Reproduction Number and the Case Fatality Rate of COVID-19 from the beginning of the pandemic until July 31, 2021. We also investigate the long–term statistical dependence of the COVID-19 Reproduction Number and the Case Fatality Rate on epidemiological, demographic, economic, immunization, and government policy factors in each country. The findings of this study may have important implications for the health authorities worldwide in considering their vaccination policies, non-pharmaceutical intervention measures, and resource allocation decisions. This retrospective study suffers from several limitations. First, the officially reported numbers of daily COVID-19 confirmed cases depend on the local testing policy and usually underestimate the true number of carriers in the population. As daily virus testing of the entire population is not possible, this reporting rate is usually unknown. Second, the officially reported numbers of daily COVID-19 deaths in some countries may include all deceased individuals who tested positive for COVID-19 (people who “died with coronavirus”), disregarding their actual cause of death, and exclude some victims (people who “died from coronavirus”), because they were not tested for COVID-19 before their death. The reported numbers depend on the tim...
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.
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- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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