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A Cellular Automata Modeling for Visualizing and Predicting Spreading Patterns of Dengue Fever

Journal from gdlhub / 2016-11-03 12:14:37
Oleh : Puspa Eosina Hosen, Taufik Djatna, Helda Khusun, Telkomnika
Dibuat : 2016-03-01, dengan 1 file

Keyword : Cellular Automata, Dengue Fever, HMM, Neighborhood, SIR
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/2404

A Cellular Automata (CA) model is used for visualizing and predicting spreading pattern of the disease. The main problem of this model is how to find a function that represents an update rule that changes the state of a cell in time steps affected by neighborhood. This research aims to develop visualization and prediction model of the spreading patterns of Dengue Hemorrhagic Fever. The contribution of our study is to introduce a new approach in defining a probabilistic function that represents CA transmission rule by employing Von Neumann neighborhood and the Hidden Markov Model (HMM). This study only considered an infective state which dedicated particular attention to the spatial distribution of infected areas. The infected data were devided into four categories and change the definition of a cell as an area. The evaluation was conducted by comparing the results of the proposed model to that of one yielded by a Susceptible-Infected-Recovered (SIR) model. The evaluation result showed that the CA model was capable of generating patterns that similar to the patterns generated by SIR models with a similarities value of 0.95

Deskripsi Alternatif :

A Cellular Automata (CA) model is used for visualizing and predicting spreading pattern of the disease. The main problem of this model is how to find a function that represents an update rule that changes the state of a cell in time steps affected by neighborhood. This research aims to develop visualization and prediction model of the spreading patterns of Dengue Hemorrhagic Fever. The contribution of our study is to introduce a new approach in defining a probabilistic function that represents CA transmission rule by employing Von Neumann neighborhood and the Hidden Markov Model (HMM). This study only considered an infective state which dedicated particular attention to the spatial distribution of infected areas. The infected data were devided into four categories and change the definition of a cell as an area. The evaluation was conducted by comparing the results of the proposed model to that of one yielded by a Susceptible-Infected-Recovered (SIR) model. The evaluation result showed that the CA model was capable of generating patterns that similar to the patterns generated by SIR models with a similarities value of 0.95

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