  Digital twins are virtual representations of physical objects or systems used
for the purpose of analysis, most often via computer simulations, in many
engineering and scientific disciplines. Recently, this approach has been
introduced to computational medicine, within the concept of Digital Twin in
Healthcare (DTH). Such research requires verification and validation of its
models, as well as the corresponding sensitivity analysis and uncertainty
quantification (VVUQ).
  From the computing perspective, VVUQ is a computationally intensive process,
as it requires numerous runs with variations of input parameters. Researchers
often use high-performance computing (HPC) solutions to run VVUQ studies where
the number of parameter combinations can easily reach tens of thousands.
However, there is a viable alternative to HPC for a substantial subset of
computational models - serverless computing.
  In this paper we hypothesize that using the serverless computing model can be
a practical and efficient approach to selected cases of running VVUQ
calculations. We show this on the example of the EasyVVUQ library, which we
extend by providing support for many serverless services. The resulting library
- CloudVVUQ - is evaluated using two real-world applications from the
computational medicine domain adapted for serverless execution. Our experiments
demonstrate the scalability of the proposed approach.
