当前位置: X-MOL 学术Math. Geosci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Stochastic Model of an Early Warning System for Detecting Anomalous Incidence Values of COVID-19
Mathematical Geosciences ( IF 2.8 ) Pub Date : 2023-09-01 , DOI: 10.1007/s11004-023-10096-4
Ana Filipa Duarte , Amílcar Soares , Maria João Pereira , André Peralta-Santos , Pedro Pinto Leite , Leonardo Azevedo

The ability to identify and predict outbreaks during epidemic and pandemic events is critical to the development and implementation of effective mitigation measures by the relevant health and political authorities. However, the spatiotemporal prediction of such diseases is not straightforward due to the highly non-linear behaviour of its evolution in both space and time. The methodology proposed herein is the basis of an early warning system to predict short-term anomalous values (i.e., high and low values) of the incidence of COVID-19 at the municipality level for mainland Portugal. The proposed modelling tool combines stochastic sequential simulation and machine learning, namely symbolic regression, to model the spatiotemporal evolution of the disease. The machine learning component is used to model the 14-day incidence rate curves of COVID-19, as provided by the Portuguese Directorate-General for Health, while the geostatistical simulation component models the spatial distribution of these predictions, for a simulation grid comprising the metropolitan area of Lisbon, following a pre-defined spatial continuity pattern. The method is illustrated for a period of 5 months during 2021, and considering the entire set of 19 municipalities belonging to the metropolitan area of Lisbon, Portugal. The results show the ability of the early warning system to predict and detect anomalous high and low incidence rate values for different periods of the pandemic event during this period.



中文翻译:

用于检测 COVID-19 异常发生率值的早期预警系统的随机模型

在流行病和大流行事件期间识别和预测疫情爆发的能力对于相关卫生和政治当局制定和实施有效的缓解措施至关重要。然而,由于此类疾病在空间和时间上的演变都具有高度非线性行为,因此对此类疾病的时空预测并不简单。本文提出的方法是早期预警系统的基础,用于预测葡萄牙大陆各市级 COVID-19 发病率的短期异常值(即高值和低值)。所提出的建模工具结合了随机序列模拟和机器学习(即符号回归)来模拟疾病的时空演变。机器学习组件用于对葡萄牙卫生总局提供的 COVID-19 14 天发病率曲线进行建模,而地统计模拟组件则对这些预测的空间分布进行建模,模拟网格包括里斯本大都市区,遵循预先定义的空间连续性模式。该方法以 2021 年 5 个月为时间段进行说明,并考虑了属于葡萄牙里斯本大都市区的全部 19 个城市。结果表明,预警系统有能力预测和检测该时期大流行事件不同时期的异常高低发病率值。

更新日期:2023-09-01
down
wechat
bug