Han, Zhenyu; Fengli Xu; Yong Li; Tao Jiang; Depeng Jin; Jianhua Lu and James A. Evans
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 treatment1,2. 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 transmission3. Moreover, recent work associating coronavirus with human mobility4,5 and detailed movement data6,7 suggest the need to consider urban mobility in disease forecasts8,9. 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 (R2 =0.990). Estimated mobility change due to policy interventions is consistent with empirical observation from Apple Mobility Trends Reports10 (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.