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

Area of Science:

• Epidemiology
• Biostatistics
• Public Health

Background:

• COVID-19 mortality data often lack completeness and accuracy, underestimating the pandemic's impact.
• Excess mortality, a comparison of observed versus expected deaths, provides a more robust metric for pandemic burden.
• Accurate, high-resolution data are crucial for understanding within-country trends and evaluating public health interventions.

Approach:

• Propose a flexible R framework for estimating and visualizing excess mortality at high geographical resolution.
• Demonstrate the framework with a case study of excess deaths in Italy during 2020.
• The framework integrates various models and allows flexible aggregation of results by age, sex, space, and time.

Key Points:

• Excess mortality estimation requires accounting for population trends, temperature, and spatio-temporal factors.
• The R framework enables rapid implementation and adaptable analysis of excess mortality data.
• High geographical resolution is essential for granular analysis of pandemic impact and policy effectiveness.

Conclusions:

• The proposed R framework offers a powerful tool for online monitoring of the pandemic's mortality burden.
• Timely and accurate excess mortality data facilitate informed policy-making and public health responses.
• This approach enhances the ability to assess the true impact of the pandemic across diverse populations and regions.