A Semi-quantitative Covid-19 Individual Risk Model

TL;DR

This paper presents a semi-quantitative Covid-19 risk model designed for proactive use in warning apps, aiming to provide uniform individual risk assessments and better understanding of risks before actions are taken.

Contribution

The paper introduces a new risk model that can be integrated into warning apps for pre-action risk assessment, improving uniformity and interpretability of individual risks.

Findings

Model can be implemented on a single app screen

Allows for individual preferences and scenario adjustments

Calibration is feasible but challenging

Abstract

This paper introduces a new basic risk model that could also be utilized by Covid-19 warning apps a priori, before an action is performed. Today the common warning apps estimate risk a posteriori and give no advice on particular scenarios. The new model also has the advantage that the individual risks behind the decision-making process would be uniform (in contrast to some current regulations) and it could help to understand the risks better and could also help to reduce risks a priori. It could be easily implemented on a single app screen, needing only some individual preferences to be set and a handful of adjustments to the particular scenario that shall be assessed. The disadvantage as of any simplified semi-quantitative risk models is that calibration is not easy (as some calibration points may even contradict) and that cumulative effects are hard to integrate e. g. the joint effect of combined scenarios. But, in principle calibration is feasible and it may be a good decision to calibrate the model conservatively.