title: Prediction of population with Alzheimer's disease in the European Union using a system dynamics model.
abstract: BACKGROUND
  Alzheimer's disease (AD) is a slowly progressing neurodegenerative brain disease with irreversible brain effects; it is the most common cause of dementia. With increasing age, the probability of suffering from AD increases. In this research, population growth of the European Union (EU) until the year 2080 and the number of patients with AD are modeled.
  
  
  OBJECTIVE
  The aim of this research is to predict the spread of AD in the EU population until year 2080 using a computer simulation.
  
  
  METHODS
  For the simulation of the EU population and the occurrence of AD in this population, a system dynamics modeling approach has been used. System dynamics is a useful and effective method for the investigation of complex social systems. Over the past decades, its applicability has been demonstrated in a wide variety of applications. In this research, this method has been used to investigate the growth of the EU population and predict the number of patients with AD. The model has been calibrated on the population prediction data created by Eurostat.
  
  
  RESULTS
  Based on data from Eurostat, the EU population until year 2080 has been modeled. In 2013, the population of the EU was 508 million and the number of patients with AD was 7.5 million. Based on the prediction, in 2040, the population of the EU will be 524 million and the number of patients with AD will be 13.1 million. By the year 2080, the EU population will be 520 million and the number of patients with AD will be 13.7 million.
  
  
  CONCLUSIONS
  System dynamics modeling approach has been used for the prediction of the number of patients with AD in the EU population till the year 2080. These results can be used to determine the economic burden of the treatment of these patients. With different input data, the simulation can be used also for the different regions as well as for different noncontagious disease predictions.
authors.affiliation: Faculty of Informatics and Management.
authors.forename: Hana
authors.initials: H
authors.lastname: Tomaskova
authors.affiliation: Faculty of Science.
authors.forename: Jitka
authors.initials: J
authors.lastname: Kuhnova
authors.affiliation: Faculty of Informatics and Management; Center for Basic and Applied Research (CZAV), University of Hradec Králové, Hradec Králové, Czech Republic.
authors.forename: Richard
authors.initials: R
authors.lastname: Cimler
authors.affiliation: Faculty of Informatics and Management.
authors.forename: Ondrej
authors.initials: O
authors.lastname: Dolezal
authors.affiliation: Center for Basic and Applied Research (CZAV), University of Hradec Králové, Hradec Králové, Czech Republic.
authors.forename: Kamil
authors.initials: K
authors.lastname: Kuca
keywords: Alzheimer’s disease; population modeling; prediction model; system dynamics