{"title":"用元胞自动机模型预测登革热患者人数","authors":"Trirat Soemsap, S. Wongthanavasu, W. Satimai","doi":"10.1109/IEECON.2014.6925876","DOIUrl":null,"url":null,"abstract":"This paper presents a novel forecasting model for dengue patient number using cellular automata (CA). The proposed model takes a number of people in each status of an epidemic model called SIER into consideration. In this respect, CA take a Genetic Algorithm (GA) to generate the factor weight chromosomes and Artificial Neural Network (ANN) to determine the probability of state transition `S' to `E' at time step t (Pt(s,e)). In addition, other related probabilities are obtained by expert knowledge; P(e,i) = 0.15 and P(i,s)=0.001. P(r,s) is determined by GA. These probabilities were used to calculate the cell number of each state at the next time step of CA. CA compute the fitness for one time step and repeat every time step finally to compute RMSE. For performance evaluation, 32 factors of dengue causes are used in the model. The dataset collected during 2005 to 2011 consisting of 359 weeks in which 287 and 72 are used to train and test the model, respectively. The results showed that the proposed model outperforms the compared ANN.","PeriodicalId":306512,"journal":{"name":"2014 International Electrical Engineering Congress (iEECON)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Forecasting number of dengue patients using cellular automata model\",\"authors\":\"Trirat Soemsap, S. Wongthanavasu, W. Satimai\",\"doi\":\"10.1109/IEECON.2014.6925876\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel forecasting model for dengue patient number using cellular automata (CA). The proposed model takes a number of people in each status of an epidemic model called SIER into consideration. In this respect, CA take a Genetic Algorithm (GA) to generate the factor weight chromosomes and Artificial Neural Network (ANN) to determine the probability of state transition `S' to `E' at time step t (Pt(s,e)). In addition, other related probabilities are obtained by expert knowledge; P(e,i) = 0.15 and P(i,s)=0.001. P(r,s) is determined by GA. These probabilities were used to calculate the cell number of each state at the next time step of CA. CA compute the fitness for one time step and repeat every time step finally to compute RMSE. For performance evaluation, 32 factors of dengue causes are used in the model. The dataset collected during 2005 to 2011 consisting of 359 weeks in which 287 and 72 are used to train and test the model, respectively. The results showed that the proposed model outperforms the compared ANN.\",\"PeriodicalId\":306512,\"journal\":{\"name\":\"2014 International Electrical Engineering Congress (iEECON)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-03-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Electrical Engineering Congress (iEECON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEECON.2014.6925876\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Electrical Engineering Congress (iEECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEECON.2014.6925876","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Forecasting number of dengue patients using cellular automata model
This paper presents a novel forecasting model for dengue patient number using cellular automata (CA). The proposed model takes a number of people in each status of an epidemic model called SIER into consideration. In this respect, CA take a Genetic Algorithm (GA) to generate the factor weight chromosomes and Artificial Neural Network (ANN) to determine the probability of state transition `S' to `E' at time step t (Pt(s,e)). In addition, other related probabilities are obtained by expert knowledge; P(e,i) = 0.15 and P(i,s)=0.001. P(r,s) is determined by GA. These probabilities were used to calculate the cell number of each state at the next time step of CA. CA compute the fitness for one time step and repeat every time step finally to compute RMSE. For performance evaluation, 32 factors of dengue causes are used in the model. The dataset collected during 2005 to 2011 consisting of 359 weeks in which 287 and 72 are used to train and test the model, respectively. The results showed that the proposed model outperforms the compared ANN.