Angelou Anastasia, Kioutsioukis Ioannis, Stilianakis Nikolaos I
Department of Physics, University of Patras, Greece.
European Commission, Joint Research Centre (JRC), Ispra, Italy.
One Health. 2021 Sep 20;13:100330. doi: 10.1016/j.onehlt.2021.100330. eCollection 2021 Dec.
In this study, initial elements of a modelling framework aimed to become a spatial forecasting model for the transmission risk of West Nile virus (WNV) are presented. The model describes the dynamics of a WNV epidemic in population health states of mosquitoes, birds and humans and was applied to the case of Greece for the period 2010-2019. Calibration was performed with the available epidemiological data from the Hellenic Centre for Disease Control and Prevention and the environmental data from the European Union's earth observation program, Copernicus. Numerical results of the model for each municipality were evaluated against observations. Specifically, the occurrence of WNV, the number of infected humans and the week of incidence predicted from the model were compared to the corresponding numbers from observations. The results suggest that dynamic downscaling of a climate-dependent epidemiological model is feasible down-to roughly 80km. This below nomenclature of territorial units for statistics (NUTS) 3 level represents the municipalities being the lowest level of administrative units, able to cope with WNV and take actions. The average detection probability in hindcast mode was 72%, improving strongly as the number of infected humans increased. Using the developed model, we were also able to show the fundamental importance of the May temperatures in shaping the WNV dynamics. The modeling framework couples epidemiological and environmental dynamical variables with surveillance data producing risk maps downscaled at a local level. The approach can be expanded to provide targeted early warning probabilistic forecasts that can be used to inform public health policy decision making.
在本研究中，提出了一个建模框架的初步要素，该框架旨在成为西尼罗河病毒（WNV）传播风险的空间预测模型。该模型描述了WNV在蚊子、鸟类和人类群体健康状态下的流行动态，并应用于2010 - 2019年希腊的情况。利用希腊疾病控制和预防中心的现有流行病学数据以及欧盟地球观测计划哥白尼计划的环境数据进行了校准。针对各观测值评估了该模型对每个市政当局的数值结果。具体而言，将模型预测的WNV发生情况、感染人类数量和发病周数与观测得到的相应数字进行了比较。结果表明，将依赖气候的流行病学模型动态降尺度至约80公里是可行的。低于统计领土单位命名法（NUTS）3级代表市政当局，它们是能够应对WNV并采取行动的最低行政单位级别。后报模式下的平均检测概率为72%，随着感染人类数量的增加显著提高。利用所开发的模型，我们还能够展示五月气温在塑造WNV动态方面的根本重要性。该建模框架将流行病学和环境动态变量与监测数据相结合，生成在地方层面降尺度的风险地图。该方法可以扩展以提供有针对性 的早期预警概率预测，可用于为公共卫生政策决策提供信息。
