Impact of asymptomatic COVID-19 carriers on pandemic policy outcomes

TL;DR

This paper develops a mathematical model showing that misestimating the fraction of asymptomatic COVID-19 carriers significantly hampers effective pandemic policy predictions and interventions.

Contribution

It introduces a SE(A+O)R model with data calibration demonstrating the unidentifiability of key parameters like r, affecting policy outcome predictions.

Findings

Parameter r is unidentifiable from standard data.

Incorrect estimates of r lead to flawed policy predictions.

Optimal policies may be ineffective if r is misestimated.

Abstract

This paper provides a mathematical model to show that the incorrect estimation of r, the fraction of asymptomatic COVID-19 carriers in the general population, can account for much of the world's failure to contain the pandemic in its early phases. The SE(A+O)R model with infectives separated into asymptomatic and ordinary carriers, supplemented by a model of the data generation process, is calibrated to standard datasets for several countries. It is shown that certain fundamental parameters, notably r, are unidentifiable with this data. A number of potential types of policy intervention are analyzed. It is found that the lack of parameter identifiability implies that only some, but not all, potential policy interventions can be correctly predicted. In an example representing Italy in March 2020, a hypothetical optimal policy of isolating confirmed cases that aims to reduce the basic reproduction number of the outbreak to R0 = 0.8 assuming r = 10%, only achieves R0 = 1.4 if it turns out that r = 40%.