Anticipating the novel coronavirus disease (COVID-19) pandemic

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

To test the significance of our statistical analyses, we estimate Kendall rank correlation-τ test statistic for both the generic indicators. We generate 1000 surrogate time-series of the same length as the analysed real data sets to test the likelihood of obtaining the computed trends by chance. The surrogate records are obtained on bootstrapping the real datasets by shuffling the original residual time-series and sampling the data with replacement. This method generates the surrogate time-series with a similar distribution of the original time-series [18] . For each surrogate, we consider the Kendall-τ estimate as the test statistic to measure the robustness of the outcomes. Further, we calculate the fraction of the surrogates having the same or higher (lower, for return rates) test static value than the original data and measure the probability P (τ * ≤ τ ) to calculate that the observed test statistic is by chance.