Abstract:
COVID-19 outbreak has been declared as a public health emergency of international concern, and
later as a pandemic. In most countries, the COVID-19 incidence curve rises sharply in a short period,
suggesting a transition from a disease-free (or low-burden disease) equilibrium state to a sustained
infected (or high-burden disease) state. Such a transition is often known to exhibit characteristics of
‘critical slowing down’. Critical slowing down can be, in general, successfully detected using many
statistical measures such as variance, lag-1 autocorrelation, density ratio, and skewness. Here,
we report an empirical test of this phenomena on the COVID-19 data sets for nine countries,
including India, China, and the United States. For most of the data sets, increase in variance and
autocorrelation predict the onset of a critical transition. Our analysis suggests two key features
in predicting the COVID-19 incidence curve for a specific country: a) the timing of strict social
distancing and/or lockdown interventions implemented, and b) the fraction of a nation’s population
being affected by COVID-19 at that time. Further, using satellite data of nitrogen dioxide, as an
indicator of lockdown efficacy, we find that in countries where the lockdown was implemented early
and firmly have been successful in reducing the COVID-19 spread. These results are essential for
designing effective strategies to control the spread/resurgence of infectious pandemics.
