Prevention and control of influenza epidemics are major challenges for public health care services. Early identification of flu outbreak is an important step towards implementing effective disease interventions for reducing mortality and morbidity in human populations. Indeed, health officials need a real geo-making tool for monitoring and prediction. The aim of the current study is to discuss a novel spatiotemporal tool for monitoring and predicting the phenomenon of the seasonal influenza epidemic spread in the human population using multiple regression analysis. The suggested tool is mainly based on three sub-systems. It allows generating simulation data by the use of a simulation system, integrating data sources in a data warehouse (DW) system and performing a specific online analysis Spatial On-Line Analytical Processing (SOLAP). Our proposal enables also to illustrate evolution of disease through visualizations in time and space. It can examine social network effects to better understand the topological structure of social contact and the impact of its properties. A regression analysis is performed on the influenza epidemic to examine the main factors influencing flu incidence number and therefore to predict and track influenza epidemic.

中文翻译：

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多元回归模型在流行病学决策支持系统中的集成

流感疫情防控是公共卫生服务面临的重大挑战。早期发现流感爆发是实施有效疾病干预以降低人群死亡率和发病率的重要一步。事实上，卫生官员需要一个真正的地理制作工具来进行监测和预测。本研究的目的是讨论一种新的时空工具，用于使用多元回归分析监测和预测季节性流感流行在人群中传播的现象。建议的工具主要基于三个子系统。它允许通过使用模拟系统生成模拟数据，在数据仓库 (DW) 系统中集成数据源并执行特定的在线分析空间在线分析处理 (SOLAP)。我们的提议还可以通过时间和空间的可视化来说明疾病的演变。它可以检查社交网络效应，以更好地了解社交联系的拓扑结构及其属性的影响。对流感流行进行回归分析，以检查影响流感发病人数的主要因素，从而预测和跟踪流感流行。