PDF(Pubmed)
Abstract：
Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.
摘要：
印度先前的研究已经确定了城市化，人口流动和人口统计是与较高地区水平COVID-19发病率相关的关键变量。然而,印度农村和城市地区流动模式的时空动态，与COVID-19传输的其他驱动器一起，没有得到充分的调查。我们使用从Google获得的汇总和匿名的每周人类运动数据集，在两次大流行浪潮中探索了印度境内的旅行网络。与2020年初8周时间段的平均基线流动性相比，大流行之前和期间流动性的量化变化。我们在R中的集成嵌套拉普拉斯近似（INLA）软件包中拟合贝叶斯时空分层模型和分布式滞后非线性模型（DLNM），以检查城市中COVID-19传播驱动因素的滞后响应关联，郊区,郊区和印度农村地区在2020-2021年的两次大流行浪潮中。模型结果表明，在Delta传播波期间，流动性恢复到大流行前水平的99%与COVID-19传播的相对风险增加有关。这增加了流动性，再加上公共干预政策的严格性降低和Delta变体的出现，是2021年4月印度COVID-19传播高峰的主要贡献者。在印度的两次大流行浪潮中，减少人类的流动性，更严格的干预措施，和气候因素(温度和降水)对COVID-19传播的Rt有2周的滞后响应影响，随着城市中观察到的COVID-19传播驱动因素的变化，农村和郊区。随着全球气候的变化，新发感染和疾病爆发的可能性增加，提供一个框架来理解感染传播的时空驱动因素的滞后影响对于告知干预措施至关重要。
