Perini Matteo, Yamana Teresa K, Galanti Marta, Suh Jiyeon, Kaondera-Shava Roselyn, Shaman Jeffrey
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United States.
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 W 168th St, New York, NY 10032, United States.
Epidemics. 2025 Mar;50:100818. doi: 10.1016/j.epidem.2025.100818. Epub 2025 Jan 26.
Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions.
We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis.
This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.
The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.
了解传染病传播动态并预测临床结果对于管理大规模流行病和大流行（如2019冠状病毒病）至关重要。对相互关联人群中的疾病传播进行有效建模有助于为跨地区的公共卫生应对措施和干预提供信息。
我们开发了一种新颖的集合种群模型，用于模拟北美地区的呼吸道病毒传播，特别是针对加拿大、墨西哥和美国的96个州、省和地区。该模型以2019冠状病毒病病例数据为依据，这些数据使用贝叶斯推理算法集合调整卡尔曼滤波器（EAKF）进行同化。此外，通勤和流动数据用于每天构建和调整网络以及各地之间的移动情况。
该模型推理系统提供了2020年1月至2021年3月期间96个地点中每个地点的传播动态、感染率和确诊率估计值。结果突出了三国之间疾病动态和确诊情况的差异。
集合种群结构能够进行大规模快速模拟，数据同化方法使系统能够对系统动态变化做出响应。该模型可作为在北美地区对其他传染病进行建模的通用平台。
