  We comparatively analyzed the spatiotemporal fluctuations of the 2019-novel
coronavirus (COVID-19) and SARS outbreaks to understand their epidemiological
characteristics. Methodologically, we introduced TPL (Taylor power law) to
characterize their spatiotemporal heterogeneity/stability and Hubbell (2001)
unified neutral theory of biodiversity (UNTB) [specifically Harris et al.
(2015) HDP-MSN model (hierarchical Dirichlet process multi-site neutral model)]
to approximate the metapopulation of coronavirus infections. First, TPL
analysis suggested that the coronaviruses appear to have a specific
heterogeneity/stability scaling parameter (TPL-b) slightly exceeding 2 for
cumulative infections or exceeding 1 for daily incremental infections,
suggesting their potentially chaotic, unstable outbreaks. Another TPL parameter
(M0) (i.e., infection critical threshold) depends on virus kinds
(COVID-19/SARS), time (disease-stages), space (regions) and public-health
interventions (e.g., quarantines and mobility control). M0 measures the
infection level, at which infections are random (Poisson distribution) and
below which infections follow uniform distribution and may die off if M0
coincides or below the level of Allee effects. It was found that COVID-19
outbreak seems nearly twice more risky than SARS, and the lower infection
threshold may be due to its lower lethality than SARS since lower fatality
rates can facilitate the survival and spread of pathogen. Second, metacommunity
UNTB neutrality testing seems appropriate for approximating metapopulation of
coronavirus infections. Specifically, two parameters {\theta} and M, borrowed
from neutral theory, may be used to assess the relative significance of
infection through local contagion vs. infection through migration, both of
which may depend on time, space, virus kinds, and particularly public-health
interventions.
