  Humanitarian agencies must be prepared to mobilize quickly in response to
complex emergencies, and their effectiveness depends on their ability to
identify, anticipate, and prepare for future needs. These are typically highly
uncertain situations in which predictive modeling tools can be useful but
challenging to build. To better understand the need for humanitarian support --
including shelter and assistance -- and strengthen contingency planning and
protection efforts for displaced populations, we present a situational analysis
tool to help anticipate the number of migrants and forcibly displaced persons
that will cross a border in a humanitarian crisis. The tool consists of: (i)
indicators of potential intent to move drawn from traditional and big data
sources; (ii) predictive models for forecasting possible future movements; and
(iii) a simulation of border crossings and shelter capacity requirements under
different conditions. This tool has been specifically adapted to contingency
planning in settings of high uncertainty, with an application to the
Brazil-Venezuela border during the COVID-19 pandemic.
