PyData Tel Aviv 2022

12-13, 12:00–12:30 (Asia/Jerusalem), Track 2

Does the indirect protection of the vaccine biases vaccine effectiveness (VE) estimations?

SARS-CoV-2 vaccines provide high protection against infection to the vaccinated individual and indirect protection to its surroundings by blocking further transmission. Divergent results have been reported on the effectiveness of the SARS-CoV-2 vaccines. Here, we argue that this divergence is because the analyses did not consider indirect protection. Using a novel heterogeneous infection model (python) and real-world data, we demonstrate that heterogeneous vaccination rates among families and communities, both spatially and temporally, and the study design that is used may significantly skew the VE estimations

The model is a novel dynamic Monte Carlo Agent-based Model (MAM), which is based on the basic principles of statistical physics.
The basic model has 1 million particles on three different spatial networks and uses multiprocessing python tools. Using the public data from Israel and the MAM model, we created ten different immunization scenarios for 9.2 million particles in 1578 different statistical areas. Also, MAM's spatial properties ensure that most infections in the model occur within the same statistical area, with half being household infections.

As a theoretical physicist (Ph.D.), I have been modeling COVID-19 (and some Monkeypox...) for the past two years. All my predictions are presented on my Twitter account @hillaleon.