Transformed time series analysis of first-wave COVID-19: universal similarities found in the Group of Twenty (G20) Countries

Summary As of April 30, 2020, the number of cumulative confirmed coronavirus disease 2019 (COVID-19) cases exceeded 3 million worldwide and 1 million in the US with an estimated fatality rate of more than 7 percent. Because the patterns of the occurrence of new confirmed cases and deaths over time are complex and seemingly country-specific, estimating the long-term pandemic spread is challenging. I developed a simple transformation algorithm to investigate the characteristics of the case and death time series per nation, and described the universal similarities observed in the transformed time series of 19 nations in the Group of Twenty (G20). To investigate the universal similarities among the cumulative profiles of confirmed cases and deaths of 19 individual nations in the G20, a transformation algorithm of the time series data sets was developed with open-source software programs. The algorithm was used to extract and analyze statistical information from daily updated COVID-19 pandemic data sets from the European Centre for Disease Prevention and Control (ECDC). Two new parameters for each nation were suggested as factors for time-shifting and time-scaling to define reduced time, which was used to quantify the degree of universal similarities among nations. After the cumulative confirmed case and death profiles of a nation were transformed by using reduced time, most of the 19 nations, with few exceptions, had transformed profiles that closely converged to those of Italy after the onset of cases and deaths. The initial profiles of the cumulative confirmed cases per nation universally showed 3–4 week latency periods, during which the total number of cases remained at approximately ten. The latency period of the cumulative number of deaths was approximately half the latency number of cumulative cases, and subsequent uncontrollable increases in human deaths seemed unavoidable because the coronavirus had already widely spread. Immediate governmental actions, including responsive public-health policy-making and enforcement, are observed to be critical to minimize (and possibly stop) further infections and subsequent deaths. In the pandemic spread of infectious viral diseases, such as COVID-19 studied in this work, different nations show dissimilar and seemingly uncorrelated time series profiles of infected cases and deaths. After these statistical phenomena were viewed as identical events occurring at a distinct rate in each country, the reported algorithm of the data transformation using the reduced time revealed a nation-independent, universal profile (especially initial periods of the pandemic spread) from which a nation-specific, predictive estimation could be made and used to assist in immediate public-health policy-making. Research in context Evidence before this study The open data set were obtained from the website of the European Center for Disease Control and Prevention (ECDC). Although the data include the number of new cases and deaths per day per nation, extracting any apparent correlations between unique time-series of nations in different continents is challenging. Nevertheless, cumulative and non-cumulative statistics are, in principle, equivalent, and hence one can be obtained from the other. Because the non-cumulative profiles report instantaneous variations in the pandemic time series, estimation of future trends by extrapolating recent data is often intractable and limited to short-term extrapolations. Added value of this study A data transformation method for the cumulative confirmed cases (CCC) and cumulative confirmed deaths (CCD) was developed and used to directly compare the pandemic statuses of multiple nations, especially G20 nations. This model requires data for the nation with the greatest CCC and CCD (especially, during the initial burst of 90–120 days), which, in the case of the COVID-19 pandemic spread, is Italy. Two parameters for time-shifting (m) and time-scaling (β) are newly introduced and used to define the reduced time τ. After the transformation, most nations’ cumulative profiles converge with those of Italy regardless of their geographical locations. Implications of all the available evidence The discovery of the universality of the transformed CCC and CCD profiles of multiple countries provides new insight into analyzing pandemic time series, including the current COVID-19 pandemic spread. By shifting and scaling a nation’s pandemic data into the reduced time frame, the nation’s CCC and CCD profiles can be predicted as long as the reference country’s cumulative data are available in the linear time domain. After the extraction of meaningful information from the transformed data, the overall implication is that most nations will reach the same state as Italy’s current state soon, depending on a specific nation’s population and human dynamics.