Mathematical estimation of COVID-19 prevalence in Latin America
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Abstract
After the rapid spread with severe consequences in Europe and China, the SARS-CoV-2 virus is now manifesting itself in more vulnerable countries, including those in Latin America. In order to guide political decision-making via epidemiological criteria, it is crucial to assess the real impact of the epidemic. However, the use of large-scale population testing is unrealistic or not feasible in some countries. Based on a newly developed mathematical model, we estimated the seroprevalence of SARS-CoV-2 in Latin American countries. The results show that the virus spreads unevenly across countries. For example, Ecuador and Brazil are the most affected countries, with approximately 3% of the infected population. Currently, the number of new infections is increasing in all countries examined, with the exception of some Caribbean countries as Cuba. Moreover, in these countries, the peak of newly infected patients has not yet been reached.
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SciScore for 10.1101/2020.06.09.20126326: (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.
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:- Th…
SciScore for 10.1101/2020.06.09.20126326: (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.
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.
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