Human mobility triggers how fast and where infectious diseases spread and modelling community flows helps assess the impact of social distancing policies and advance our understanding of community behaviour in such circumstances. This study investigated the relationship between human mobility and the surging incidence of COVID-19 in India. We performed a generalised estimating equation with a Poisson log-linear model to analyse the daily mobility rate and new cases of COVID-19 between 14 March and 11 September 2020. We found that mobility to grocery and retail locations was significantly associated (p < 0.01) with the incidence of COVID-19, these being crowded and unorganised in most parts of India. In contrast, visits to parks, workplaces, and transit stations did not considerably affect the changing COVID-19 cases over time. In particular, workplaces equipped with social distancing protocols or low-density open spaces are much less susceptible to the spread of the virus. These findings suggest that human mobility data, geographic information, and health geography modelling have significant potential to inform strategic decision-making during pandemics because they provide actionable knowledge of when and where communities might be exposed to the disease.

中文翻译：

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人员流动对印度 COVID-19 发病率激增的影响

人类流动性触发传染病传播的速度和地点，对社区流动进行建模有助于评估社会疏远政策的影响，并促进我们对这种情况下的社区行为的理解。本研究调查了印度人口流动性与 COVID-19 发病率激增之间的关系。我们使用泊松对数线性模型执行了广义估计方程，以分析 2020 年 3 月 14 日至 9 月 11 日期间的每日流动率和 COVID-19 新病例。我们发现到杂货店和零售店的流动性显着相关（p < 0.01) 随着 COVID-19 的发病率，这些在印度大部分地区都是拥挤和无组织的。相比之下，随着时间的推移，对公园、工作场所和中转站的访问并没有显着影响不断变化的 COVID-19 病例。特别是，配备社交距离协议或低密度开放空间的工作场所更不容易受到病毒传播的影响。这些研究结果表明，人类流动数据、地理信息和健康地理模型具有为大流行期间的战略决策提供信息的巨大潜力，因为它们提供了有关社区何时何地可能接触该疾病的可操作知识。