Infection arbitrage

TL;DR

The paper explores how intentionally increasing infection risk early in an epidemic can be socially and individually beneficial by flattening the peak and reducing overall harm, based on Covid-19 data calibration.

Contribution

It introduces the concept of infection arbitrage, showing that early infection can be optimal and potentially reduce total societal loss during an epidemic.

Findings

Early infection can be individually optimal under certain conditions.

Infection arbitrage can flatten epidemic peaks and reduce healthcare overload.

Calibrations to Covid-19 data support the potential benefits of early infection strategies.

Abstract

Increasing the infection risk early in an epidemic is individually and socially optimal under some parameter values. The reason is that the early patients recover or die before the peak of the epidemic, which flattens the peak. This improves welfare if the peak exceeds the capacity of the healthcare system and the social loss rises rapidly enough in the number infected. The individual incentive to get infected early comes from the greater likelihood of receiving treatment than at the peak when the disease has overwhelmed healthcare capacity. Calibration to the Covid-19 pandemic data suggests that catching the infection at the start was individually optimal and for some loss functions would have reduced the aggregate loss.