Anticipating the novel coronavirus disease (COVID-19) pandemic

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.