Wardle Jack, Bhatia Sangeeta, Kraemer Moritz U G, Nouvellet Pierre, Cori Anne
MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, UK.
Department of Biology, University of Oxford, Oxford, UK.
Epidemics. 2023 Mar;42:100666. doi: 10.1016/j.epidem.2023.100666. Epub 2023 Jan 12.
Reliable estimates of human mobility are important for understanding the spatial spread of infectious diseases and the effective targeting of control measures. However, when modelling infectious disease dynamics, data on human mobility at an appropriate temporal or spatial resolution are not always available, leading to the common use of model-derived mobility proxies. In this study we reviewed the different data sources and mobility models that have been used to characterise human movement in Africa. We then conducted a simulation study to better understand the implications of using human mobility proxies when predicting the spatial spread and dynamics of infectious diseases. We found major gaps in the availability of empirical measures of human mobility in Africa, leading to mobility proxies being used in place of data. Empirical data on subnational mobility were only available for 17/54 countries, and in most instances, these data characterised long-term movement patterns, which were unsuitable for modelling the spread of pathogens with short generation times (time between infection of a case and their infector). Results from our simulation study demonstrated that using mobility proxies can have a substantial impact on the predicted epidemic dynamics, with complex and non-intuitive biases. In particular, the predicted times and order of epidemic invasion, and the time of epidemic peak in different locations can be underestimated or overestimated, depending on the types of proxies used and the country of interest. Our work underscores the need for regularly updated empirical measures of population movement within and between countries to aid the prevention and control of infectious disease outbreaks. At the same time, there is a need to establish an evidence base to help understand which types of mobility data are most appropriate for describing the spread of emerging infectious diseases in different settings.
可靠的人类流动估计对于理解传染病的空间传播以及控制措施的有效靶向至关重要。然而，在对传染病动态进行建模时，并非总能获得具有适当时间或空间分辨率的人类流动数据，这导致模型衍生的流动代理被普遍使用。在本研究中，我们回顾了用于描述非洲人类流动的不同数据来源和流动模型。然后，我们进行了一项模拟研究，以更好地理解在预测传染病的空间传播和动态时使用人类流动代理的影响。我们发现非洲人类流动实证测量数据的可用性存在重大差距，导致使用流动代理来替代数据。关于次国家层面流动的实证数据仅在17/54个国家可用，而且在大多数情况下，这些数据描述的是长期流动模式，不适用于对代际时间短（病例感染与其感染源之间的时间）的病原体传播进行建模。我们模拟研究的结果表明，使用流动代理可能会对预测的疫情动态产生重大影响，产生复杂且非直观的偏差。特别是，根据所使用的代理类型和相关国家的情况，疫情入侵的预测时间和顺序以及不同地点的疫情高峰时间可能会被低估或高估。我们的工作强调需要定期更新国家内部和国家之间人口流动的实证测量数据，以协助预防和控制传染病爆发。与此同时，有必要建立一个证据基础，以帮助了解哪种类型的流动数据最适合描述不同环境中新兴传染病的传播。
