Anticipating the novel coronavirus disease (COVID-19) pandemic

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

We observe that the 35-40 days window from the arrival of the first case in each country signaled an impending transition. In the vicinity of 35-40 days, an increase in the short term memory (at lag 1) of the data as well as a decrease in the return rate indicates the phenomenon of critical slowing down. Our work suggests that while non-pharmaceutical interventions are necessary to mitigate such epidemic, the timing of initiation of concerned actions can strongly influence the outcome of the situation. Owing to the time lag in the detection of symptomatic cases, the statistical indicators suggest that the initial 20-25 days being crucial to suppress the loss of public health. The controlled response of the epidemic growth curve for China and South Korea can be associated with the time distance between implementation of interventions and the transition point. Thus, EWSs analyses is crucial while defining the onset of the interventions and suppress the rise of daily cases. Importantly, another crucial aspect is the proportion of affected cases in each country, i.e., a measure of the fraction of the country's population, and not the absolute numbers, which is infected at the time of interventions such as a strict lockdown. As probability of the propagation of disease can be thought of as mostly similar or equal amongst individuals across the globe, it depends upon the fraction of infected cases in each country during the beginning of interventions. For instance, a decrease in the return rate and increased autocorrelation anticipated the upcoming rise in the growth curves for both India as well as Italy, and interestingly, both the countries imposed individual nationwide lockdown near the situation close to the transition (Table I) . However, the control in India depicts better results in altering the growth curve than that in Italy. The alterations in the growth curve is most likely to be a consequence of a difference in the proportion of cases affected by the epidemic at the beginning of mitigation strategies. India resembling China in terms of the total population density accounted for approximately 2.36 × 10 −7 cases of the total population, while Italy with relatively less population density crossed 1.22 × 10 −4 cases of their total population. Thus, even with imposition of the public health measures near the signals of CSD, the outcome for both the countries can dramatically vary. Furthermore, the disruptive situation in the US is indicated by EWSs, as the EWSs indicators were visible quite early for the US. Thus, sharp rise in the number of cases for the country is a consequence of both the delay in effective social distancing interventions . CC-BY-NC 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.