Pandemic data quality modelling: a Bayesian approach in the Italian case

Luisa Ferrari (), Giancarlo Manzi (), Alessandra Micheletti (), Federica Nicolussi () and Silvia Salini ()
Additional contact information
Luisa Ferrari: University of Bologna
Giancarlo Manzi: University of Milan
Alessandra Micheletti: University of Milan
Federica Nicolussi: Politecnico di Milano
Silvia Salini: University of Milan

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 1, No 4, 87-109

Abstract: Abstract When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to always have a firm grasp on what is the current state of the pandemic, and envision how the number of infections and possible deaths is going to evolve shortly. However, as in many other situations involving compulsory registration of sensitive data from multiple collectors, cases might be reported with errors, often with delays deferring an up-to-date view of the state of things. Errors in collecting new cases affect the overall mortality, resulting in excess deaths reported by official statistics only months later. In this paper, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a Bayesian approach accounting for the excess mortality pandemics might bring with respect to the normal level of mortality in the population.

Keywords: Pandemics; Bayesian analysis; Variance models; Time-space models (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11135-024-01913-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:qualqt:v:59:y:2025:i:1:d:10.1007_s11135-024-01913-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-024-01913-x

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().