Many countries, especially low and middle income countries, are facing limited vaccination supply and fearing more contagious and deadly SARS-CoV-2 virus mutants.
The majority of available vaccines require two immunization doses to provide maximum protection. Yet, the immunological response to the first (''prime'') dose may already provide a substantial degree of protection against infection and severe disease. Thus, it may be epidemiologically more effective to vaccinate as many people as possible with one dose, instead of administering a person a second (''booster'') dose. Such a strategic vaccination campaign may help to more effectively slow down the spread of SARS-CoV-2, thereby reducing fatalities and the risk of collapsing health care systems.
To study the conditions which make prime-first vaccination favourable over prime-boost protocols, we combine epidemiological modeling, random sampling techniques, and decision tree learning.
A schematic of prime-first and prime-boost vaccination campaigns and our SEIR model extension is shown below. Panels (A,B) show the evolution of the number of prime and prime-boost vaccianted individuals. Model compartments and transitions are shown in panel (C).
Vaccination-campaign-preference diagrams can be generated by running the scripts in model/vaccination_preference_diagrams. A simple example that shows the evolution of different model compartments is stored in model/example. If you are interested in generating input files for the decision tree analysis, please check the files in model/datasetA and model/datasetB.
A summary of our research can be found here.
- L. Böttcher, J. Nagler, Decisive Conditions for Strategic Vaccination against SARS-CoV-2, Chaos 31, 101105 (2021)
@article{bottcher2021decisive,
title={Decisive Conditions for Strategic Vaccination against SARS-CoV-2},
author={B{\"o}ttcher, Lucas and Nagler, Jan},
journal={Chaos},
volume={31},
pages={101105},
year={2021}
}