  If model identifiability is not confirmed, inferences from infectious disease
transmission models may not be reliable, so they might lead to misleading
recommendations. Structural identifiability analysis characterizes 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.
