  Mathematical models in ecology and epidemiology must be consistent with
observed data in order to generate reliable knowledge and evidence-based
policy. Metapopulation systems, which consist of a network of connected
sub-populations, pose technical challenges in statistical inference due to
nonlinear, stochastic interactions. Numerical difficulties encountered in
conducting inference can obstruct the core scientific questions concerning the
link between the mathematical models and the data. Recently, an algorithm has
been developed which enables effective likelihood-based inference for the
high-dimensional partially observed stochastic dynamic models arising in
metapopulation systems. The COVID-19 pandemic provides a situation where
mathematical models and their policy implications were widely visible, and we
use the new inferential technology to revisit an influential metapopulation
model used to inform basic epidemiological understanding early in the pandemic.
Our methods support self-critical data analysis, enabling us to identify and
address model limitations, and leading to a new model with substantially
improved statistical fit and parameter identifiability. Our results suggest
that the lockdown initiated on January 23, 2020 in China was more effective
than previously thought. We proceed to recommend statistical analysis standards
for future metapopulation system modeling.
