Monitoring a developing pandemic with available data

TL;DR

This paper develops a statistical framework for real-time pandemic monitoring using incomplete daily reported data, incorporating calendar effects and expert knowledge to improve inference and forecasting accuracy.

Contribution

It introduces a novel dynamic modeling approach that handles missing data and integrates domain expertise, enhancing pandemic severity assessment.

Findings

Model effectively captures temporal variations in pandemic data

Framework successfully applied to COVID-19 data from France

Establishes a new benchmark for integrating expert knowledge into statistical models

Abstract

This paper addresses statistical modelling and forecasting of key indicators describing the severity of a developing pandemic, using routinely reported daily counts of infections, hospitalizations, deaths (both in and out of hospital), and recoveries. These observed counts constitute what we term ``available data''. Because such data are typically incomplete or inconsistently reported, we address several novel missing data challenges arising in this context and propose statistically rigorous solutions that enable inference based solely on the available information. The model is formulated dynamically, explicitly incorporating calendar effects to capture systematic temporal variations in the progression of the pandemic. The proposed framework is illustrated using data from France collected during the COVID-19 pandemic. Our approach also establishes a new benchmark for integrating prior information from domain experts directly into the modelling process, thereby enabling a potential new division of labour between statistical estimation and epidemiological knowledge from external experts.