Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies

TL;DR

This study uses a compartmental epidemic model with real-world data to analyze how infection fatality ratios and social contact patterns influence optimal COVID-19 vaccine prioritization strategies, emphasizing early vaccination and demographic factors.

Contribution

It provides a detailed analysis of how epidemiological variables and social contact matrices affect vaccine prioritization, highlighting non-linear dependencies and the importance of early vaccination.

Findings

Early vaccination significantly reduces fatality.

Prioritization effectiveness varies with epidemiological parameters.

Demographics and contact patterns influence strategy outcomes.

Abstract

Effective strategies of vaccine prioritization are essential to mitigate the impacts of severe infectious diseases. We investigate the role of infection fatality ratio (IFR) and social contact matrices on vaccination prioritization using a compartmental epidemic model fueled by real-world data of different diseases and countries. Our study confirms that massive and early vaccination is extremely effective to reduce the disease fatality if the contagion is mitigated, but the effectiveness is increasingly reduced as vaccination beginning delays in an uncontrolled epidemiological scenario. The optimal and least effective prioritization strategies depend non-linearly on epidemiological variables. Regions of the epidemiological parameter space, in which prioritizing the most vulnerable population is more effective than the most contagious individuals, depend strongly on the IFR age profile being, for example, substantially broader for COVID-19 in comparison with seasonal influenza. Demographics and social contact matrices deform the phase diagrams but do not alter their qualitative shapes.