Understanding and Interpreting Measures of Central Location, Dispersion and Confidence Intervals in Pandemics The Case of the Novel Coronavirus (Covid-19)

Abstract

Understanding the application of statistical methods is an essential component in the analysis of pandemics. Although the management of emergencies that stem from such pandemics can be a challenging task, the effects caused by such emergencies can be mitigated by improved preparedness, the implementation of preventive measures, timely response and the implementation of recovery strategies. Statistical methods provide tools that can access the relative magnitude of pandemics, such as Covid-19. A key feature of public health management is an understanding of data and analysis to quantify the extent of such pandemics, and to be successful in their mandate. The statistics used in the current coronavirus pandemic involve clinical characteristics of the infected population, and are reported in the form of means, modes, medians, interquartile ranges and confidence intervals. These measures form part of descriptive and inferential statistics, and more specifically measures of central location and variation, as well as measures of precision. The aim of this article is to review these measures, which are typically used when reporting a new epidemic outbreak. First, the difference between descriptive statistics and inferential statistics is described, after which a thorough theoretical explanation is given regarding measures of central location and variation, as well as measures of precision. Straightforward explanations of these statistical methods may promote understanding among public administrators and managers about how epidemiological data is being analysed. This increased understanding could assist managers in responding to the pandemic, its characteristics and descriptive statistics appropriately and thus enhance their ability to formulate effective strategies.