Estimating Global and Country-Specific Excess Mortality During the COVID-19 Pandemic

Estimating Global and Country-Specific Excess Mortality During the COVID-19 Pandemic
Photo by Fusion Medical Animation on Unsplash

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 develop a model for this endeavor. 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 such countries. The expected deaths are based on historic data and we develop a model for producing estimates of these deaths for all countries. We allow for uncertainty in the modeled expected numbers when calculating the excess. The methods we describe  were used 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.  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 non-standard data situations, using our own MCMC code. Based on our modeling, the point estimate for global excess mortality, over 2020--2021, is 14.8 million, with a 95% credible interval of (13.2, 16.6) million. This is joint work with William Msemburi, Victoria Knutson, Serge Aleshin-Guendel and Ariel Karlinsky.
Dr Wakefield has conducted research on: population pharmacokinetic and pharmacodynamic modeling, ecological inference, disease mapping, epidemiological study design, cluster detection, genetic epidemiology, small area estimation and space-time modeling of infectious disease data. He spent two sabbaticals at the International Agency for Research on Cancer (IARC) in Lyon, France. He is an affiliate member in the Vaccine and Infectious Division at the Fred Hutchinson Cancer Research Center, an Affiliate with the Center for Studies in Demography and Ecology (CSDE) and a Research Affiliate with the Center for Statistics and the Social Sciences (CSSS). For the past 10 years, Dr Wakefield has been working on methods for modeling health and demographic outcomes in a low and medium incomes (LMIC) setting, and in particular on small-area estimation. Since 2016, Dr Wakefield has been collaborating with the United Nations (UN) Inter-agency group for Child Mortality Estimation (IGME) on methods for subnational estimation of child mortality. Since October, 2018, he has been on the Technical Advisory Group (TAG) for IGME. He is also a member of the UN TAGs for maternal mortality and stillbirths estimation. The team he leads has just produced the first UN subnational estimates of under-5 mortality in 22 countries. He led the modeling effort for estimating excess mortality during the pandemic, as part of the WHO TAG on this topic. 

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Date & time

Tue 11 Oct 2022, 11:00am to 12:00pm


Room 2.56, RSSS Building, ANU, 146 Ellery Crescent, Acton and by Zoom


Prof Jon Wakefield


James O'Donnell


Updated:  11 October 2022/Responsible Officer:  Head of School/Page Contact:  CASS Marketing & Communications