A universal generic description of the dynamics of the current COVID-19 pandemic

TL;DR

This paper introduces a simple, universal empirical model for COVID-19 pandemic dynamics based on total infected counts, enabling effective predictions across diverse regions with minimal parameters.

Contribution

It proposes a novel, simplified approach to model pandemic spread using only three variables, applicable globally and robust against undetected cases.

Findings

Model applies to around 50 countries with diverse data

Separates different pandemic waves effectively

Provides a measure for government measure effectiveness

Abstract

The ongoing COVID-19 pandemic is challenging every part of society. From a scientific point of view the first major task is to predict the dynamics of the pandemic, allowing governments to allocate proper resources and measures to fight it, as well as gauging the success of these measures by comparison with the predictions in hindsight. The vast majority of pandemic models are based on extensive models with large numbers of fit parameters, leading to individual descriptions for every hot spot on the world. This makes predictions and comparisons cumbersome, if not impossible. We here propose a different approach, by moving away from a description over time, and instead choosing the total number of infected people in an enclosed area as the independent variable. Analyzing a few hot spots data, we derive an empirical formula for the dynamics, dependent only on three variables. The final number of infections is strictly connected to one fit parameter we call mitigation factor, which in turn is mostly dependent only on the enclosed population. Despite its simpleness, this description applies to every of the around 50 countries we have analyzed, allows to separate different waves of the pandemic, provides a figure of merit for the overall usefulness of government measures, and shows when a pandemic is ending. Our model is robust against undetected cases, and allows all nations, in particular those with fewer resources, to reasonably predict the outcome of the pandemic in their country.