Statistical modelling of COVID-19 data: Putting Generalised Additive Models to work

TL;DR

This paper demonstrates the versatility of Generalised Additive Models (GAMs) in analyzing COVID-19 data, addressing issues like interdependence among age groups, reporting delays, and ICU occupancy with practical modeling solutions.

Contribution

The paper introduces novel GAM-based methods for COVID-19 data analysis, including independence conditions, delay correction via offset, and multinomial modeling of ICU occupancy.

Findings

GAMs can effectively model interdependence among age groups in COVID-19 data.

Incorporating delay correction as an offset improves hospitalisation incidence estimates.

Multinomial GAMs provide detailed insights into ICU occupancy composition.

Abstract

Over the course of the COVID-19 pandemic, Generalised Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this paper we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalisations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and "off-the-shelf" applicability of GAMs to gain new insights from real-world data.