A framework for estimating and visualising excess mortality during the COVID-19 pandemic

TL;DR

This paper presents a flexible R-based framework for estimating and visualizing excess mortality with high geographical resolution during the COVID-19 pandemic, aiding policy decisions and pandemic monitoring.

Contribution

It introduces a fast, adaptable framework that combines multiple models for high-resolution excess mortality estimation and visualization, enhancing pandemic analysis capabilities.

Findings

Effective estimation of excess deaths during 2020 in Italy.

Framework supports various data aggregations for detailed analysis.

Enables real-time monitoring and policy assessment.

Abstract

COVID-19 related deaths underestimate the pandemic burden on mortality because they suffer from completeness and accuracy issues. Excess mortality is a popular alternative, as it compares observed with expected deaths based on the assumption that the pandemic did not occur. Expected deaths had the pandemic not occurred depend on population trends, temperature, and spatio-temporal patterns. In addition to this, high geographical resolution is required to examine within country trends and the effectiveness of the different public health policies. In this tutorial, we propose a framework using R to estimate and visualise excess mortality at high geographical resolution. We show a case study estimating excess deaths during 2020 in Italy. The proposed framework is fast to implement and allows combining different models and presenting the results in any age, sex, spatial and temporal aggregation desired. This makes it particularly powerful and appealing for online monitoring of the pandemic burden and timely policy making.