Details

Autor(en) / Beteiligte

• Dankwa, Emmanuelle A.
• Brouwer, Andrew F.
• Donnelly, Christl A.

Titel

Structural identifiability of compartmental models for infectious disease transmission is influenced by data type

Ist Teil von

• Epidemics, 2022-12, Vol.41, p.100643-100643, Article 100643

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• If model identifiability is not confirmed, inferences from infectious disease transmission models may not be reliable, so they might result in misleading recommendations. Structural identifiability analysis characterises whether it is possible to obtain unique solutions for all unknown model parameters, given the model structure. In this work, we studied the structural identifiability of some typical deterministic compartmental models for infectious disease transmission, focusing on the influence of the data type considered as model output on the identifiability of unknown model parameters, including initial conditions. We defined 26 model versions, each having a unique combination of underlying compartmental structure and data type(s) considered as model output(s). Four compartmental model structures and three common data types in disease surveillance (incidence, prevalence and detected vector counts) were studied. The structural identifiability of some parameters varied depending on the type of model output. In general, models with multiple data types as outputs had more structurally identifiable parameters, than did models with a single data type as output. This study highlights the importance of a careful consideration of data types as an integral part of the inference process with compartmental infectious disease transmission models. •A non-identifiable model may result in misleading inferences.•We tested the identifiability of 26 combinations of 4 transmission models and 3 data types.•Model identifiability is influenced by both model structure and the type of data considered as model output.•Transmission modellers must assess how parameter identifiability depends on both their model structure and data type(s).