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. The expected deaths are based on historic data and we develop a
model for producing expected estimates for all countries and we allow for
uncertainty in the modeled expected numbers when calculating the excess. We
describe the methods that were developed to produce the 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 sub-regions remain approximately constant over time. Based on our
modeling, the point estimate for global excess mortality, over 2020-2021, is
14.9 million, with a 95% credible interval of (13.3, 16.6) million. This leads
to a point estimate of the ratio of excess deaths to reported COVID-19 deaths
of 2.75, which is a huge discrepancy