Towards Understanding the COVID-19 Case Fatality Rate

TL;DR

This study investigates how population factors shape COVID-19 case fatality rates (CFR) across countries using two timepoints in 2020. By fitting regressions on the log-scale, the authors model log(CFR) as a function of age and a GDP proxy, comparing a base age-only model to one that includes GDP. They find a robust exponential age effect with a slope around 0.07 per age-group that is nearly invariant across countries and time, while GDP shifts from being less influential in July to more significant by December, reflecting improvements in public health responses. These results help explain cross-country CFR differences and suggest policy emphasis on protecting older populations, with evolving contributions from health-system capacity over the course of the pandemic.

Abstract

An important parameter for COVID-19 is the case fatality rate (CFR). It has been applied to wide applications, including the measure of the severity of the infection, the estimation of the number of infected cases, risk assessment etc. However, there remains a lack of understanding on several aspects of CFR, including population factors that are important to CFR, the apparent discrepancy of CFRs in different countries, and how the age effect comes into play. We analyze the CFRs at two different time snapshots, July 6 and Dec 28, 2020, with one during the first wave and the other a second wave of the COVID-19 pandemic. We consider two important population covariates, age and GDP as a proxy for the quality and abundance of public health. Extensive exploratory data analysis leads to some interesting findings. First, there is a clear exponential age effect among different age groups, and, more importantly, the exponential index is almost invariant across countries and time in the pandemic. Second, the roles played by the age and GDP are a little surprising: during the first wave, age is a more significant factor than GDP, while their roles have switched during the second wave of the pandemic, which may be partially explained by the delay in time for the quality and abundance of public health and medical research to factor in.

Figures (7)

• Figure 1: CFR by age groups for selected countries (as of July 6, 2020).
• Figure 2: Log-scaled CFR by age groups for selected countries as of July 6, 2020.
• Figure 3: Scatter plot of CFR by median ages for individual countries as of Jul 6, 2020. The solid line is the regression line.
• Figure 4: Regression diagnostics plots under Model I. The left and right panel are the QQ-norm plot of regression residuals and the residual plot, respectively. The dashed line in the QQ-plot is the y=x line.
• Figure 5: Changes in CFR from July 6, 2020 to Dec 28, 2020. The countries are sorted by median ages in an increasing order from left to right in the figure. The numbers on the x-axis are the median age.