title: IASM: A System for the Intelligent Active Surveillance of Malaria.
abstract: Malaria, a life-threatening infectious disease, spreads rapidly via parasites. Malaria prevention is more effective and efficient than treatment. However, the existing surveillance systems used to prevent malaria are inadequate, especially in areas with limited or no access to medical resources. In this paper, in order to monitor the spreading of malaria, we develop an intelligent surveillance system based on our existing algorithms. First, a visualization function and active surveillance were implemented in order to predict and categorize areas at high risk of infection. Next, socioeconomic and climatological characteristics were applied to the proposed prediction model. Then, the redundancy of the socioeconomic attribute values was reduced using the stepwise regression method to improve the accuracy of the proposed prediction model. The experimental results indicated that the proposed IASM predicted malaria outbreaks more close to the real data and with fewer variables than other models. Furthermore, the proposed model effectively identified areas at high risk of infection.
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Xinlei
authors: 0000-0003-2765-6547
authors: X
authors: Wang
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Bo
authors: B
authors: Yang
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Jing
authors: 0000-0003-2077-556X
authors: J
authors: Huang
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Hechang
authors: H
authors: Chen
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Xiao
authors: X
authors: Gu
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Yuan
authors: Y
authors: Bai
authors: School of Computer Science and Technology, Jilin University, Changchun 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun 130012, China.
authors: Zhanwei
authors: 0000-0002-2020-767X
authors: Z
authors: Du
mesh_terms: D000465:Algorithms; D000818:Animals; D000962:Antimalarials / Q000008:administration & dosage; D002681:China; D002980:Climate; D003140:Communicable Disease Control; D003198:Computer Simulation; D009033:Culicidae; D040362:Geographic Information Systems; D006801:Humans; D008288:Malaria / Q000175:diagnosis* / Q000453:epidemiology*; D008490:Medical Informatics / Q000379:methods; D008516:Medicine, Chinese Traditional; D002052:Myanmar; D016011:Normal Distribution; D015588:Observer Variation; D011159:Population Surveillance / Q000379:methods; D011336:Probability; D040442:Public Health Informatics; D012044:Regression Analysis; D012306:Risk; D012923:Social Class; D013696:Temperature
chemical_list: D000962:Antimalarials