2023/08/19 by María Luz Gámiz, Enno Mammen, Gámiz, María Luz +7
Mathematics · #62G05 #COVID-19 epidemiological studies #FOS: Computer and information sciences #G.3 #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2308.09919
openalex publication_date 2023/08/19 · openalex created_date 2023/08/23 · openalex updated_date 2026/07/28
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.