On the role of data, statistics and decisions in a pandemic

TL;DR

This paper reviews the role of data, statistical models, and decision-making processes during a pandemic, emphasizing the importance of data quality, modeling techniques, and communication for effective responses.

Contribution

It provides a comprehensive overview of modeling approaches and data requirements in pandemic decision-making, with recommendations from a statistical perspective.

Findings

Highlights the importance of data quality and statistical literacy.

Discusses various modeling techniques used in pandemic analysis.

Emphasizes effective communication of results for better decision-making.

Abstract

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy, and of effective dissemination and communication of findings.