College of Geography and Environment, Shandong Normal University, Jinan 250014, China.
Int J Environ Res Public Health. 2021 Dec 16;18(24):13294. doi: 10.3390/ijerph182413294.
Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various "densities" were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the "densities" were actually an abstract reflection of the "contact" frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect "contact" frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional "densities". Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.
新冠疫情风险评估是制定防控策略的基础，特别是在微观层面。在之前的风险评估模型中，各种“密度”被视为新冠疫情在空间维度（人口密度、设施密度、轨迹密度等）的决定性驱动因素。然而，这一结论忽略了一个事实，即“密度”实际上是“接触”频率的抽象反映，而“接触”频率才是疫情传播更本质的决定因素，并且缺乏任何相应的定量修正手段。在这项研究中，基于在传统研究中经常使用的设施密度（FD），结合区域人口密度和各年龄段人口流动性水平的差异，提出了一种新的微观尺度新冠疫情风险预测指标——设施吸引力（FA），用于改进基于重力模型的预测方法。利用地理加权回归（GTWR）方法，对疫情初期青岛市进行了时空建模的实证分析。模型结果揭示了设施密度（吸引力）与新冠疫情微观风险之间的时空非平稳关系。新的预测指标表明，居住区域和医疗保健设施比传统的“密度”指标具有更合理的影响。与基于 FD 构建的模型（0.5159）相比，FA 模型的全局预测能力（调整后的 R²）提高了 10.4%（0.5694）。局部预测能力的提高更为显著，尤其是在疫情初期的高风险地区（增长率：107.2%）和人口稠密地区（市南区增长率：64.4%；市北区增长率：57.8%）。研究结果证明，在区域疫情的初始阶段，优化后的预测指标比传统预测指标更适合用于时空感染风险建模。这些发现可为未来微观尺度时空感染建模提供方法学参考和模型优化思路。
