Anticipating the novel coronavirus disease (COVID-19) pandemic

Taranjot Kaur; Sukanta Sarkar; Sourangsu Chowdhury; Sudipta Kumar Sinha; Mohit Kumar Jolly; Partha Sharathi Dutta

The COVID-19 disease can spread in a population through infected symptomatic/asymptomatic individuals who come in contact directly/indirectly [4] . Thus, concerned with the public health and well-being affected due to COVID-19, various countries have adopted comprehensive strategies converging to social distancing such as the closure of schools, ban of large gatherings, isolation of symptomatic individuals, and monitoring travelers, particularly to those from COVID-19 hotspots. Large scale lockdowns of populations have been implemented to prevent social contacts and reduce reproduction of the infected cases [5] [6] [7] . Evidence also highlights the impor-often irreversible transition to an alternative stable state [11, 20] . In particular, the phenomenon of CSD can be captured as a large time taken by a system to return to its previous states due to which the rate of return of a system decreases prior to a transition. Moreover, it leads to an increase in the short term memory of a system, this feature can be identified by the changes in the correlation structure of a time-series preceding a critical transition [13, 14, [21] [22] [23] . Thus, it is crucial to understand whether these indicators capture the characteristics of slowing down in the epidemic growth curves.