The impact of ambiguously reported epidemiological parameters for infectious disease modelling and recommended best practices
File(s)
Published version (pre-proof)
Author(s)
Type
Journal Article
Abstract
Epidemiological parameters characterise the natural history, transmission and severity of a pathogen and are necessary to understand the spread of infectious diseases. These parameters underpin our ability to quantify and respond to disease outbreaks. Parameters can be estimated from observations using a range of methods and are often reported in varied ways throughout the literature. These parameter estimates constitute essential inputs to infectious disease models used to quantify and project disease spread and burden, and assess intervention impact. Hence, any incompleteness or ambiguity in reported parameter estimates can have downstream consequences on the inferences drawn from the models that use these estimates. We summarise common issues with incomplete or ambiguous reporting of epidemiological parameter estimates and illustrate the impact through five case studies. Specifically, we show that in many instances, misinterpreting parameters reported in the literature can lead to biased conclusions that mislead subsequent public health responses. Additionally, we provide recommendations on how to clearly communicate common epidemiological parameter estimates consistently and reproducibly, to maximise their secondary use and comparison, in turn minimising erroneous extraction from the literature and application in epidemiological analysis.
Date Issued
2026-08-08
Date Acceptance
2026-08-04
Citation
Epidemics, 2026
ISSN
1755-4365
Publisher
Elsevier BV
Start Page
100942
End Page
100942
Journal / Book Title
Epidemics
Copyright Statement
© 2026 Published by Elsevier.
License URL
Publication Status
Published online
Article Number
100942
Date Publish Online
2026-08-08