School of Engineering, Ulster University, United Kingdom.
School of Engineering, Ulster University, United Kingdom.
J Biomed Inform. 2021 Oct;122:103905. doi: 10.1016/j.jbi.2021.103905. Epub 2021 Sep 2.
Compartment-based infectious disease models that consider the transmission rate (or contact rate) as a constant during the course of an epidemic can be limiting regarding effective capture of the dynamics of infectious disease. This study proposed a novel approach based on a dynamic time-varying transmission rate with a control rate governing the speed of disease spread, which may be associated with the information related to infectious disease intervention. Integration of multiple sources of data with disease modelling has the potential to improve modelling performance. Taking the global mobility trend of vehicle driving available via Apple Maps as an example, this study explored different ways of processing the mobility trend data and investigated their relationship with the control rate. The proposed method was evaluated based on COVID-19 data from six European countries. The results suggest that the proposed model with dynamic transmission rate improved the performance of model fitting and forecasting during the early stage of the pandemic. Positive correlation has been found between the average daily change of mobility trend and control rate. The results encourage further development for incorporation of multiple resources into infectious disease modelling in the future.
基于舱室的传染病模型在传染病过程中将传播率（或接触率）视为常数，这可能会限制对传染病动态的有效捕捉。本研究提出了一种新方法，该方法基于时变的传播率，采用控制率来控制疾病传播的速度，这可能与传染病干预的相关信息有关。将多种数据源与疾病建模相结合具有提高建模性能的潜力。本研究以苹果地图提供的全球车辆驾驶移动趋势为例，探索了处理移动趋势数据的不同方法，并研究了它们与控制率的关系。该方法基于来自六个欧洲国家的 COVID-19 数据进行了评估。结果表明，在大流行的早期阶段，具有动态传播率的建议模型提高了模型拟合和预测的性能。已经发现移动趋势的平均每日变化与控制率之间存在正相关关系。结果鼓励未来进一步将多种资源纳入传染病建模中。
