Why a Globally Fair COVID-19 Vaccination? An Analysis based on Agent-Based Simulation
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Abstract
In this paper, an Agent-Based Model (ABM) is proposed to evaluate the impact of COVID-19 vaccination drive in different settings. The main focus is to evaluate the counter-effectiveness of disparity in vaccination drive among different regions/countries. The model proposed is simple yet novel in the sense that it captures the spatial transmission-induced activity into consideration, through which we are able to relate the transmission model to the mutated variations of the virus. Some important what-if questions are asked in terms of the number of deaths, and time required and the percentage of the population needed to be vaccinated before the pandemic is eradicated. The simulation results have revealed that it is necessary to maintain a global (rather than regional or country-oriented) vaccination drive in case of a new pandemic or continual efforts against COVID-19.
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SciScore for 10.1101/2021.10.03.21264494: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. 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:…
SciScore for 10.1101/2021.10.03.21264494: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. 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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