  Timely responses from policy makers to mitigate the impact of the COVID-19
pandemic rely on a comprehensive grasp of events, their causes, and their
impacts. These events are reported at such a speed and scale as to be
overwhelming. In this paper, we present ExcavatorCovid, a machine reading
system that ingests open-source text documents (e.g., news and scientific
publications), extracts COVID19 related events and relations between them, and
builds a Temporal and Causal Analysis Graph (TCAG). Excavator will help
government agencies alleviate the information overload, understand likely
downstream effects of political and economic decisions and events related to
the pandemic, and respond in a timely manner to mitigate the impact of
COVID-19. We expect the utility of Excavator to outlive the COVID-19 pandemic:
analysts and decision makers will be empowered by Excavator to better
understand and solve complex problems in the future. An interactive TCAG
visualization is available at http://afrl402.bbn.com:5050/index.html. We also
released a demonstration video at https://vimeo.com/528619007.
