Limitations and Implications of Doubling Time Approach in COVID-19 Infection Spreading Study

Abstract

To control the spread of COVID-19, around the world, many countries imposed lockdowns. Numerous studies were reported on COVID-19 in different disciplines with various aspects. The doubling time is a mathematical technique to estimate the current rate of spread of the disease. Researchers used the doubling technique to address the COVID-19 pandemic situation. The larger doubling period represents a low spreading rate, whereas the smaller doubling period represents a high spreading rate. In other words, high infection implies the low doubling period and low infection implies the high doubling period. So, there is an inverse relationship between doubling time and the infection rate. But the real-life data does not follow such a rule properly in various domains. The data shows that after a certain time when the infection is high, the doubling period is also high, which misleads our general concept of doubling time. This chapter addressed this issue by investigating the real-time COVID-19 data. To overcome this limitation, a gradient smoothing technique has been proposed.

Correlation Coefficient-based Breakpoint detection @Piecewise Linear Regression

“…It does not perform well in the case of non-linear data points but most of the real-life data are not linear in nature. To address this issue, researchers have developed a modified version of linear regression known as the piecewise linear regression model [6,7]. The primary concern of the model is to find the breakpoints and partitioned the data based on these breakpoints.…”