Infection Units: A Novel Approach for Modeling COVID-19 Spread
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Abstract
A novel mechanistic model of COVID-19 spread is presented. The pool of infected individuals is not homogeneously mixed but is viewed as a passage into which individuals enter upon the contagion, through which they pass (in the manner of “plug flow”) and exit at their recovery points within a fixed time. Our novel concept of infection unit is defined. The model separately considers various population pools: two of symptomatic and asymptomatic infected patients; three different pools of recovered individuals; of assisted hospitalized patients; of the quarantined; and of those who die from COVID-19. Transmission of this disease is described by an infection rate function, modulated by an encounter frequency function. This definition makes redundant the addition of a separate pool for the exposed, as done in several other models. Simulations are presented. The effects of social restrictions and of quarantine policies on pandemic spread are demonstrated. The model differs conceptually from others of the kind in the description of the transmission dynamics of the disease. A set of experimental data is used to calibrate our model, which predicts the dynamic behavior of each of the defined pools during pandemic spread.
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SciScore for 10.1101/2021.05.01.21256433: (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 10.1101/2021.05.01.21256433: (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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