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.

中文翻译：

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政策意识的移动模型解释了城市中COVID-19的增长

随着冠状病毒的持续传播，预测不同城市中独特的COVID-19增长曲线的任务（对于标准流行病学模型仍无法充分解释）对于医疗供应和治疗至关重要。预测必须考虑到非药物干预措施以减缓冠状病毒的传播，包括在家中用餐，社交疏散，隔离和强制戴口罩，从而导致城市内流动性和病毒传播减少。此外，最近将冠状病毒与人类流动性和详细运动数据相关联的工作表明，在疾病预测中需要考虑城市流动性。在这里，我们表明，通过将城市内部的流动性和政策采用纳入新型的人口SEIR模型中，我们可以准确预测美国复杂的COVID-19增长方式 城市（$ R ^ 2 $ = 0.990）。由于政策干预而导致的预计流动性变化与Apple Mobility Trends Reports（Pearson R = 0.872）的经验观察一致，这表明在数据有限的情况下基于模型的预测的实用性。我们的模型还再现了城市的“超级传播”，其中少数社区占整个城市空间中的大多数继发感染，这是由邻里人口不均和在流行社区中城市内部用户流失加剧引起的。因此，我们的模型可以促进以位置感知的移动性减少策略，从而以相似的社会成本更有效地缓解疾病传播。最后，我们证明了我们的模型可以作为细化的分析和模拟框架，为合理的非药物干预政策的设计提供依据。由于政策干预而导致的预计流动性变化与Apple Mobility Trends Reports（Pearson R = 0.872）的经验观察一致，这表明在数据有限的情况下基于模型的预测的实用性。我们的模型还再现了城市的“超级传播”，其中少数社区占整个城市空间中的大多数继发感染，这是由邻里人口不均和在流行社区中城市内部用户流失加剧引起的。因此，我们的模型可以促进以位置感知的移动性减少策略，从而以相似的社会成本更有效地缓解疾病传播。最后，我们证明了我们的模型可以用作细粒度的分析和模拟框架，为合理的非药物干预政策的设计提供依据。由于政策干预而导致的预计流动性变化与Apple Mobility Trends Reports（Pearson R = 0.872）的经验观察一致，这表明在数据有限的情况下基于模型的预测的实用性。我们的模型还再现了城市的“超级传播”，其中少数社区占整个城市空间中的大多数继发感染，这是由邻里人口不均和在流行社区中城市内部流失加剧引起的。因此，我们的模型可以促进以位置感知的移动性减少策略，从而以相似的社会成本更有效地缓解疾病传播。最后，我们证明了我们的模型可以用作细粒度的分析和模拟框架，为合理的非药物干预政策的设计提供依据。