  With the continued spread of coronavirus, the task of forecasting distinctive
COVID-19 growth curves in different cities, which remain inadequately explained
by standard epidemiological models, is critical for medical supply and
treatment. Predictions must take into account non-pharmaceutical interventions
to slow the spread of coronavirus, including stay-at-home orders, social
distancing, quarantine and compulsory mask-wearing, leading to reductions in
intra-city mobility and viral transmission. Moreover, recent work associating
coronavirus with human mobility and detailed movement data suggest the need to
consider urban mobility in disease forecasts. Here we show that by
incorporating intra-city mobility and policy adoption into a novel
metapopulation SEIR model, we can accurately predict complex COVID-19 growth
patterns in U.S. cities ($R^2$ = 0.990). Estimated mobility change due to
policy interventions is consistent with empirical observation from Apple
Mobility Trends Reports (Pearson's R = 0.872), suggesting the utility of
model-based predictions where data are limited. Our model also reproduces urban
"superspreading", where a few neighborhoods account for most secondary
infections across urban space, arising from uneven neighborhood populations and
heightened intra-city churn in popular neighborhoods. Therefore, our model can
facilitate location-aware mobility reduction policy that more effectively
mitigates disease transmission at similar social cost. Finally, we demonstrate
our model can serve as a fine-grained analytic and simulation framework that
informs the design of rational non-pharmaceutical interventions policies.
