Analysing pandemics in phase-space

TL;DR

This paper introduces a phase-space based SIRD model with time-delay to analyze COVID-19 outbreaks, demonstrating improved death predictions and highlighting limitations of classical models during the first wave.

Contribution

A novel phase-space SIRD model with time-delay is proposed, providing more accurate pandemic predictions and insights into model limitations.

Findings

Predicted total deaths within 10% accuracy after infection peak.

Classical SIRD models with constant parameters fail to describe the first wave accurately.

Phase-space representation reveals limitations of traditional models.

Abstract

Based on the SIRD-model a new model including time-delay is proposed for a description of the outbreak of the novel coronavirus Sars-CoV-2 pandemic. All data were analysed by representing all quantities as a function of the susceptible population, as opposed to the usual dependence on time. The total number of deaths could be predicted for the first, second and third wave of the pandemic in Germany with an accuracy of about 10\%, shortly after the maximum of infectious people was reached. By using the presentation in phase space, it could be shown that a classical SEIRD- and SIRD-model with constant parameters will not be able to describe the first wave of the pandemic accurately.