  Modern epidemiological analyses to understand and combat the spread of
disease depend critically on access to, and use of, data. Rapidly evolving
data, such as data streams changing during a disease outbreak, are particularly
challenging. Data management is further complicated by data being imprecisely
identified when used. Public trust in policy decisions resulting from such
analyses is easily damaged and is often low, with cynicism arising where claims
of "following the science" are made without accompanying evidence. Tracing the
provenance of such decisions back through open software to primary data would
clarify this evidence, enhancing the transparency of the decision-making
process. Here, we demonstrate a Findable, Accessible, Interoperable and
Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that
allows easy annotation of data as they are consumed by analyses, while tracing
the provenance of scientific outputs back through the analytical source code to
data sources. Such a tool provides a mechanism for the public, and fellow
scientists, to better assess the trust that should be placed in scientific
evidence, while allowing scientists to support policy-makers in openly
justifying their decisions. We believe that tools such as this should be
promoted for use across all areas of policy-facing research.
