  The dynamics of infectious diseases propagating in populations depends both
on human interaction patterns, the contagion process and the pathogenesis
within hosts. The immune system follows a circadian rhythm and, consequently,
the chance of getting infected varies with the time of day an individual is
exposed to the pathogen. The movement and interaction of people also follow
24-hour cycles, which couples these two phenomena. We use a stochastic
metapopulation model informed by hourly mobility data for two medium-sized
Chinese cities. By this setup, we investigate how the epidemic risk depends on
the difference of the clocks governing the population movement and the immune
systems. In most of the scenarios we test, we observe circadian rhythms would
constrain the pace and extent of disease emergence. The three measures
(strength, outward transmission risk and introduction risk) are highly
correlated with each other. For example of the Yushu City, outward transmission
risk and introduction risk are correlated with a Pearson's correlation
coefficient of 0.83, and the risks correlate to strength with coefficients of
$-0.85$ and $-0.75$, respectively (all have $p<0.05$), in simulations with no
circadian effect and $R_0=1.5$. The relation between the circadian rhythms of
the immune system and daily routines in human mobility can affect the pace and
extent of infectious disease spreading. Shifting commuting times could mitigate
the emergence of outbreaks.
