How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates

Abstract

In this work, we assess the global impact of COVID-19 showing how demographic factors, testing policies and herd immunity are key for saving lives. We extend a standard epidemiological SEIR model in order to: (a) identify the role of demographics (population size and population age distribution) on COVID-19 fatality rates; (b) quantify the maximum number of 15 lives that can be saved according to different testing strategies, different levels of herd immunity, and specific population characteristics; and (d) infer from the observed case fatality rates (CFR) what the true fatality rate might be. Different from previous SEIR model extensions, we implement a Bayesian Melding method in our calibration strategy which enables us to account for data limitation on the total number of deaths. We derive a distribution of the set of parameters that best 20 replicate the observed evolution of deaths by using information from both the model and the data.One Sentence Summary: Demographics factors, testing policies and herd immunity are key for quantifying the maximum number of lives that can be saved from COVID-19.

An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States

“…One main advantage of the proposed method is to report uncertainty levels to the number of infected people. As has been previously indicated in other studies, the age distribution of the population is important to explain the variation in the estimated IFRs and the reported CFRs across populations [32][33][34]36]. For Florida, the most aged population in our study, we estimate an IFR of 1.25% (0.39-2.16%, 90% CI), which is almost twofold higher than that of the youngest population of Utah, with an estimated IFR of 0.69% (0.21-1.19%, 90% CI).…”

“…The approach combines IFRs that are estimated through a SEIR (susceptible-exposed-infected-removed) model with deaths and reported case-fatality rates (CFR) reported by countries. We first estimate the IFRs by fitting a SEIR model that takes into account demographic characteristics, such as the age distribution of the population and underlying age-specific COVID-19 and non-COVID-19 mortality rates [32][33][34], through a Bayesian melding approach [35]. The main advantage of using the Bayesian melding is to better manage the high degree of uncertainty in COVID-19 data, since it derives the distribution of the set of parameters that best replicates the observed evolution of deaths by using information from the model and the data.…”