  Daily variation in human mobility modulates the speed and severity of
emerging outbreaks, yet most epidemiological studies assume static contact
patterns. With a highly mobile population exceeding 24 million people,
Shanghai, China is a transportation hub at high risk for the importation and
subsequent global propagation of infectious diseases. Here, we use a dynamic
metapopulation model informed by hourly transit data for Shanghai to estimate
epidemic risks across thousands of outbreak scenarios. We find that the rate of
initial epidemic growth varies by more than twenty-fold, depending on the hour
and neighborhood of disease introduction. The riskiest introductions are those
occurring close to the city center and on Fridays--which bridge weekday and
weekend transit patterns and thereby connect otherwise disconnected portions of
the population. The identification of these spatio-temporal hotspots can inform
more efficient targets for sentinel surveillance and strategies for mitigating
transmission.
