  The COVID-19 pandemic and the implementation of social distancing policies
have rapidly changed people's visiting patterns, as reflected in mobility data
that tracks mobility traffic using location trackers on cell phones. However,
the frequency and duration of concurrent occupancy at specific locations govern
the transmission rather than the number of customers visiting. Therefore,
understanding how people interact in different locations is crucial to target
policies, inform contact tracing, and prevention strategies. This study
proposes an efficient way to reduce the spread of the virus among on-campus
university students by developing a self-developed Google History Location
Extractor and Indicator software based on real-world human mobility data. The
platform enables policymakers and researchers to explore the possibility of
future developments in the epidemic's spread and simulate the outcomes of human
mobility and epidemic state under different epidemic control policies. It
offers functions for determining potential contacts, assessing individual
infection risks, and evaluating the effectiveness of on-campus policies. The
proposed multi-functional platform facilitates the screening process by more
accurately targeting potential virus carriers and aids in making informed
decisions on epidemic control policies, ultimately contributing to preventing
and managing future outbreaks.
