Novel deterministic epidemic model considering mass vaccination and lockdown against coronavirus disease 2019 spread in Israel: A numerical study
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Abstract
Why public health intervention by the Israeli government against coronavirus disease 2019 spread has been successful while the majority of other countries are still coping with it? To give a quantitative answer, a simple numerical epidemic model is prepared to simulate the entire trend of various infection-related variables considering the 1 st and 2 nd vaccination campaigns against the alpha variant and simultaneous lockdown. This model is an extension of our previously published deterministic physical model, i.e. Apparent Time Lag Model, which aims at predicting an entire trend of variables in a single epidemic. The time series data of both vaccine dose ratio and lockdown period are employed in the model. Predictions have been compared with observed data in terms of daily new cases, isolated people, infections at large and effective reproductive number, and, further, the model is verified. Moreover, parameter survey calculations for several scenarios have clarified the synergy effects of vaccination and lockdown. In particular, the key element of Israel’s success has been suggested to lie in a high-dose vaccination rate that prevents the onset of a rebound in daily new cases on the rescission of the lockdown.
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SciScore for 10.1101/2021.05.15.21257264: (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: We detected the following sentences addressing limitations in the study:Limitations and future work: Limitations of the present method follow. First, the present vaccination model is established based on efficacy data of BNT162b2 mRNA COVID-19 vaccine (Pfizer-BioNTech COVID-19 vaccine). It should be extended to accommodate other COVID-19 vaccines. Also, it is assumed activation time needed after injection …
SciScore for 10.1101/2021.05.15.21257264: (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: We detected the following sentences addressing limitations in the study:Limitations and future work: Limitations of the present method follow. First, the present vaccination model is established based on efficacy data of BNT162b2 mRNA COVID-19 vaccine (Pfizer-BioNTech COVID-19 vaccine). It should be extended to accommodate other COVID-19 vaccines. Also, it is assumed activation time needed after injection ξ=10. Since other observations exist, wide parameter survey is requisite to get a conservative solution for policy making. In summary, this study is the first work of a full simulation of the 3rd wave of epidemic in Israel accompanied by mass vaccination campaign and simultaneous lockdown. Existing data and policy making elsewhere would be aided by inclusion of the present model.On its occasion potential users of the present model are advised to reassess the values of the delay constants T,H,V,ξin the model when the application to other countries is attempted because they are influenced by the difference in medical regulations or practice.
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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