The impact of ambiguously reported epidemiological parameters for infectious disease modelling and recommended best practices.

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.