Estimating global and country-specific excess mortality during the COVID-19 pandemic

Overview

Estimating the true mortality burden of COVID-19 for every
country in the world is a difficult, but crucial, public health endeavor.
Attributing deaths, direct or indirect, to COVID-19 is problematic. A more
attainable target is the “excess deaths”, the number of deaths in a particular
period, relative to that expected during “normal times”, and we estimate
this for all countries on a monthly time scale for 2020 and 2021. The excess
mortality requires two numbers, the total deaths and the expected deaths, but
the former is unavailable for many countries, and so modeling is required for
these countries, and the expected deaths are based on historic data and we
develop a model for producing expected estimates for all countries.We allow
for uncertainty in the modeled expected numbers when calculating the excess.
We describe the methods that were developed to produce World Health
Organization (WHO) excess death estimates. To achieve both interpretability
and transparency we developed a relatively simple overdispersed Poisson
count framework, within which the various data types can be modeled. We
use data from countries with national monthly data to build a predictive log-linear
regression model with time-varying coefficients for countries without
data. For a number of countries, subnational data only are available, and we
construct a multinomial model for such data, based on the assumption that
the fractions of deaths in specific sub-regions remain approximately constant
over time. Our inferential approach is Bayesian, with the covariate predictive
model being implemented in the fast and accurate INLA software. The
subnational modeling was carried out using MCMC in Stan or in some nonstandard
data situations, using our own MCMC code. Based on our modeling,
the 95% interval estimate for global excess mortality, over 2020–2021,
is 13.3–16.6 million.