The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm. (November 2020)
- Record Type:
- Journal Article
- Title:
- The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm. (November 2020)
- Main Title:
- The influence of immune individuals in disease spread evaluated by cellular automaton and genetic algorithm
- Authors:
- Monteiro, L.H.A.
Gandini, D.M.
Schimit, P.H.T. - Abstract:
- Highlights: It is conjectured that immune individuals affect the spread of contagious diseases. An epidemic model formulated in terms of cellular automaton is proposed. Genetic algorithm is employed to identify three parameters of this model. Data of varicella prevalence in Belgium and Italy around the year 2000 are used. The actual role of immune individuals in infection spread is discussed. Abstract: Background and objective: One of the main goals of epidemiological studies is to build models capable of forecasting the prevalence of a contagious disease, in order to propose public health policies for combating its propagation. Here, the aim is to evaluate the influence of immune individuals in the processes of contagion and recovery from varicella. This influence is usually neglected. Methods: An epidemic model based on probabilistic cellular automaton is introduced. By using a genetic algorithm, the values of three parameters of this model are determined from data of prevalence of varicella in Belgium and Italy, in a pre-vaccination period. Results: This methodology can predict the varicella prevalence (with average relative error of 2 % − 4 % ) in these two European countries. Belgium data can be explained by ignoring the role of immune individuals in the infection propagation; however, Italy data can be explained by considering contagion exclusively mediated by immune individuals. Conclusions: The role of immune individuals should be accurately delineated inHighlights: It is conjectured that immune individuals affect the spread of contagious diseases. An epidemic model formulated in terms of cellular automaton is proposed. Genetic algorithm is employed to identify three parameters of this model. Data of varicella prevalence in Belgium and Italy around the year 2000 are used. The actual role of immune individuals in infection spread is discussed. Abstract: Background and objective: One of the main goals of epidemiological studies is to build models capable of forecasting the prevalence of a contagious disease, in order to propose public health policies for combating its propagation. Here, the aim is to evaluate the influence of immune individuals in the processes of contagion and recovery from varicella. This influence is usually neglected. Methods: An epidemic model based on probabilistic cellular automaton is introduced. By using a genetic algorithm, the values of three parameters of this model are determined from data of prevalence of varicella in Belgium and Italy, in a pre-vaccination period. Results: This methodology can predict the varicella prevalence (with average relative error of 2 % − 4 % ) in these two European countries. Belgium data can be explained by ignoring the role of immune individuals in the infection propagation; however, Italy data can be explained by considering contagion exclusively mediated by immune individuals. Conclusions: The role of immune individuals should be accurately delineated in investigations on the dynamics of disease propagation. In addition, the proposed methodology can be adapted for evaluating, for instance, the role of asymptomatic carriers in the novel coronavirus spread. … (more)
- Is Part Of:
- Computer methods and programs in biomedicine. Volume 196(2020)
- Journal:
- Computer methods and programs in biomedicine
- Issue:
- Volume 196(2020)
- Issue Display:
- Volume 196, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 196
- Issue:
- 2020
- Issue Sort Value:
- 2020-0196-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-11
- Subjects:
- Cellular automaton -- Contagious disease -- Genetic algorithm -- SIR model
Medicine -- Computer programs -- Periodicals
Biology -- Computer programs -- Periodicals
Computers -- Periodicals
Medicine -- Periodicals
Médecine -- Logiciels -- Périodiques
Biologie -- Logiciels -- Périodiques
Biology -- Computer programs
Medicine -- Computer programs
Periodicals
Electronic journals
610.28 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01692607 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.cmpb.2020.105707 ↗
- Languages:
- English
- ISSNs:
- 0169-2607
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.095000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14758.xml