Department of Statistics, Colorado State University, Fort Collins, Colorado, USA.
Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA.
Stat Med. 2022 Jul 10;41(15):2745-2767. doi: 10.1002/sim.9382. Epub 2022 Mar 23.
The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission. The foundation of this model is a time-varying coefficient compartment model to recapitulate the dynamics among susceptible, exposed, undetected infectious, detected infectious, undetected removed, hospitalized, detected recovered, and detected deceased individuals. The infectiousness and detection parameters are modeled to vary by time, and the infectiousness component in the model incorporates information on multiple sources of population mobility. Along with this compartment model, a multiplicative process model is introduced to allow for deviation from the deterministic dynamics. We apply this model to observed COVID-19 cases and deaths in several U.S. states and Colorado counties. We find that population mobility measures are highly correlated with transmission rates and can explain complicated temporal variation in infectiousness in these regions. Additionally, the inferred connections between mobility and epidemiological parameters, varying across locations, have revealed the heterogeneous effects of different policies on the dynamics of COVID-19.
COVID-19 的传播受到各国、各州和各县不同的监管政策和行为模式的极大影响。COVID-19 的人群动态通常可以用一组常微分方程来描述，但这些确定性方程不足以对观察到的病例率进行建模，因为病例率会因当地的检测和病例报告政策以及个体之间的非同质行为而发生变化。为了评估人口流动对 COVID-19 传播的影响，我们开发了一种用于传染病传播的新型贝叶斯时变系数状态空间模型。该模型的基础是一个时变系数 compartment 模型，以重现易感者、暴露者、未检测到的传染性、检测到的传染性、未检测到的清除、住院、检测到的恢复和检测到的死亡个体之间的动态。感染性和检测参数被建模为随时间变化，模型中的感染性部分包含了关于多种人口流动源的信息。除了这个 compartment 模型，还引入了一个乘法过程模型，以允许对确定性动态进行偏离。我们将该模型应用于美国几个州和科罗拉多州各县的观察到的 COVID-19 病例和死亡。我们发现，人口流动措施与传播率高度相关，并且可以解释这些地区感染性的复杂时间变化。此外，推断出的流动性和流行病学参数之间的联系因地点而异，揭示了不同政策对 COVID-19 动态的异质影响。
