  The emergence of the novel coronavirus (COVID-19) has generated a need to
quickly and accurately assemble up-to-date information related to its spread.
While it is possible to use deaths to provide a reliable information feed, the
latency of data derived from deaths is significant. Confirmed cases derived
from positive test results potentially provide a lower latency data feed.
However, the sampling of those tested varies with time and the reason for
testing is often not recorded. Hospital admissions typically occur around 1-2
weeks after infection and can be considered out of date in relation to the time
of initial infection. The extent to which these issues are problematic is
likely to vary over time and between countries.
  We use a machine learning algorithm for natural language processing, trained
in multiple languages, to identify symptomatic individuals derived from social
media and, in particular Twitter, in real-time. We then use an extended SEIRD
epidemiological model to fuse combinations of low-latency feeds, including the
symptomatic counts from Twitter, with death data to estimate parameters of the
model and nowcast the number of people in each compartment. The model is
implemented in the probabilistic programming language Stan and uses a bespoke
numerical integrator. We present results showing that using specific
low-latency data feeds along with death data provides more consistent and
accurate forecasts of COVID-19 related deaths than using death data alone.
