Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors
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Abstract
The impact of the COVID-19 epidemic on the socio-economic status of countries around the world should not be underestimated, when we consider the role it has played in various countries. Many people were unemployed, many households were careful about their spending, and a greater social divide in the population emerged in 14 different countries from the Organization for Economic Co-operation and Development (OECD) and from Africa (that is, in developed and developing countries) for which we have considered the epidemiological data on the spread of infection during the first and second waves, as well as their socio-economic data. We established a mathematical relationship between Theil and Gini indices, then we investigated the relationship between epidemiological data and socio-economic determinants, using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiological parameters and we predicted three of the socio-economic variables in order to validate our results. These results show a clear difference between the first and the second wave of the pandemic, confirming the impact of the real dynamics of the epidemic’s spread in several countries and the means by which it was mitigated.
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SciScore for 10.1101/2021.09.30.21264356: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2021.09.30.21264356: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
Results from scite Reference Check: We found no unreliable references.
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