The impact of preventive measures on controlling COVID-19 pandemic: a statistical analysis study

Abstract

Background: The purpose of this paper is to investigate the primary variables associated with the COVID-19 disease and to demonstrate how to evaluate the effect of the earlier consideration of the containment measure and the massive testing policy on controlling the spread of this pandemic. We introduced and analyzed, for the first time to our knowledge, a new variable referred to as the Gap, which was defined as the time between the appearance of the first case and the implementation of the containment measure.

Methods: A correlation, linear, and nonlinear regression-based statistical analysis was conducted to determine the impact of numerous variables and factors on the spread of this pandemic.

Results: 81.3% of the variability of total cases was explained by the variability of total tests, and 72.3% of the variability of total deaths was explained by the variability of total cases. In addition, we have constructed significant nonlinear models that explain 97.8% of the total cases’ information and 89.4% of the total deaths’ information as a function of the Gap variable. Furthermore, we have found no correlation between the total number of tests and the fatality rate.

Conclusion. Consideration of earlier containment is an effective measure that enables the prevention of a catastrophic disease spread scenario. In addition, the massive testing policy has no effect on the fatality rate. However, the performance of tests is highly effective at detecting new cases earlier, before they infect a large number of individuals, and is also an effective method for controlling the spread of this disease.