School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK.
School of Global and Area Studies, University of Oxford, 12 Bevington Road, Oxford, OX2 6LH, UK.
Trans R Soc Trop Med Hyg. 2021 Sep 3;115(9):956-964. doi: 10.1093/trstmh/trab009.
In recent years, Zika virus (ZIKV) has expanded its geographic range and in 2015-2016 caused a substantial epidemic linked to a surge in developmental and neurological complications in newborns. Mathematical models are powerful tools for assessing ZIKV spread and can reveal important information for preventing future outbreaks. We reviewed the literature and retrieved modelling studies that were developed to understand the spatial epidemiology of ZIKV spread and risk. We classified studies by type, scale, aim and applications and discussed their characteristics, strengths and limitations. We examined the main objectives of these models and evaluated the effectiveness of integrating epidemiological and phylogeographic data, along with socioenvironmental risk factors that are known to contribute to vector-human transmission. We also assessed the promising application of human mobility data as a real-time indicator of ZIKV spread. Lastly, we summarised model validation methods used in studies to ensure accuracy in models and modelled outcomes. Models are helpful for understanding ZIKV spread and their characteristics should be carefully considered when developing future modelling studies to improve arbovirus surveillance.
近年来，寨卡病毒（ZIKV）的地理范围不断扩大，2015-2016 年导致了一次大规模的流行，与新生儿发育和神经并发症的激增有关。数学模型是评估 ZIKV 传播的有力工具，可以为预防未来的爆发提供重要信息。我们回顾了文献，检索了旨在了解 ZIKV 传播和风险的空间流行病学的建模研究。我们根据类型、规模、目的和应用对研究进行了分类，并讨论了它们的特点、优势和局限性。我们研究了这些模型的主要目标，并评估了整合流行病学和系统地理学数据以及已知有助于媒介-人类传播的社会环境风险因素的有效性。我们还评估了人类流动性数据作为 ZIKV 传播实时指标的有前景的应用。最后，我们总结了用于确保模型和模型结果准确性的研究中使用的模型验证方法。模型有助于了解 ZIKV 的传播，在开发未来的建模研究以改善虫媒病毒监测时，应仔细考虑其特点。
