COVID-19: The unreasonable effectiveness of simple models

TL;DR

This paper demonstrates that COVID-19 outbreak dynamics can be effectively modeled using simple SIR models, highlighting the importance of early and extensive testing to accurately identify the true epidemic peak and reduce overall deaths.

Contribution

It reveals the universality of simple SIR models in describing COVID-19 dynamics and introduces a method to quantify testing biases from data.

Findings

COVID-19 dynamics belong to the simple SIR universality class.

Testing bias can be quantified and used to extract accurate epidemic information.

Early and extensive testing correlates with earlier true peaks and fewer deaths.

Abstract

When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.