Bringing accountability to the peak of the pandemic using linear response theory

Prakash, Meher K

The peak of the daily new infections in COVID-19 remained qualitative in description and elusive in arrival. Because of the lack of clarity in what to expect from the peak, apart from the hope that one day the peak will be reached, there has been no metric to describe the success of the implemented strategies. We propose a way of predicting the number of infections that can be expected after a lockdown, assuming they come from the asymptomatic cases prior to the lockdown and using linear response theory. These predictions for several western countries faithfully follow the observed infections for several weeks after the lockdown, suggesting universalities in the recovery pattern of several countries. At the same time, the gap between the quantitative predictions of the recovery patterns for New York and Milan and the observations is striking. These gaps which arise even while emulating the recovery patterns of other western countries raise the possibility of an audit of the success of the implemented strategies, and the potential newer sources of infection.