Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible–infectious–recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

COVID-19 的标准流行病学模型采用局部尺度的隔室（SIR 或易感-感染-康复）模型变体，隐含地假设空间均匀的局部混合。在这里，我们研究了基于人际网络的已知空间特征，采用更详细的地理扩散模型对疾病扩散的影响，特别是与距离相互作用的概率存在长尾但单调下降的情况。根据对美国 19 个城市不受限制的 COVID-19 扩散的模拟，我们得出结论，人口分布的异质性可能对当地大流行的时间和严重程度产生重大影响，即使更大规模的总体行为反映了经典的类似 SIR 的模式。观察到的影响包括严重的局部疫情，相对于总感染曲线而言具有较长的滞后时间，以及许多地区的疾病轨迹与邻近地区的相关性较差。医院需求的简单流域模型说明了对医疗保健利用的潜在影响，即使没有疏远干预措施，影响的时间和程度也存在巨大差异。同样，对其他患病或死亡者的社会暴露分析表明，当地个人对这种流行病的看法存在很大差异，可能会影响风险评估和缓解措施的遵守。这些结果表明，即使在城市规模上，空间网络结构也有可能产生高度不均匀的扩散行为，并表明在设计模型以告知医疗保健规划、预测社区结果或识别潜在差异时纳入此类结构的重要性。