Integration of Multiple Regression Model in an Epidemiological Decision Support System

Fatima-Zohra Younsi (), Ahmed Bounnekar (), Djamila Hamdadou () and Omar Boussaid ()
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Fatima-Zohra Younsi: LIO Laboratory, University of Oran 1, Ahmed Ben Bella, BP 1524, El-M’Naouer 31000, Oran, Algeria
Ahmed Bounnekar: #x2020;Polytechnic School, University of Claude Bernard Lyon 1, Lyon 69622, France
Djamila Hamdadou: LIO Laboratory, University of Oran 1, Ahmed Ben Bella, BP 1524, El-M’Naouer 31000, Oran, Algeria
Omar Boussaid: #x2021;ERIC Laboratory, University of Lumière Lyon 2, 5 Avenue Pierre Mendès-France, 9676 Bron Cedex, France

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 06, 1755-1783

Abstract: 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.

Keywords: Decision support system for epidemic monitoring and predicting (DSS-EMP+); simulation system; multiple regression model; geo-visualization; multiple regression analysis; risk mapping; social network analyzes (SNA) (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219622019500408

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