Modelling the Spatial Distribution of Epidemic by Search Engine Data

doi:10.13474/j.cnki.11-2246.2018.0051

Abstract

Abstract:

The Internet records people's daily life,and the analyzing and mining of search engine query data with location can discover valuable geographic information hidden in it.In this paper,the correlation between the monthly influenza case data in each Chinese province and the Baidu search index of related keywords were calculated,the most related keyword's index was chosen as the explanatory variable while the influenza case data was chosen as the dependent variable.The principal component analysis was used to eliminate the effect of multicollinearity among variables before the spatial distribution model of influenza was constructed by ordinary least squares regression (OLS),geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR).The GTWR model demonstrated a better goodness-of-fit (0.959) than the OLS (0.737) and GWR model (0.915).The Akaike information criterion (AIC) test also supported that the improvement made by GTWR over OLS and GWR models were statistically significant.Validation results showed that the GTWR model can accurately identify the high prevalence area of influenza.It demonstrates that combining the GTWR model with search engine query data can model the spatial distribution of influenza accurately,and provide a prediction model and statistical explanation for the study of epidemiology.

Key words: geographically and temporally weighted regression, search engine data, influenza, spatial distribution model

CLC Number:

P208

XIAO Yi, HE Zongyi, MIAO Jing, PAN Feng, YANG Hao. Modelling the Spatial Distribution of Epidemic by Search Engine Data[J]. 测绘通报, 2018, 0(2): 94-98.

References

[1] 胡雪芸,何宗宜,苗静.疾病数据的时空聚集分析及可视化[J].测绘通报,2015(11):106-111.
[2] 单杰,秦昆,黄长青,等.众源地理数据处理与分析方法探讨[J].武汉大学学报(信息科学版),2014,39(4):390-396.
[3] 艾廷华.大数据驱动下的地图学发展[J].测绘地理信息,2016,41(2):1-7.
[4] 鲁力,邹远强,彭友松,等.百度指数和微指数在中国流感监测中的比较分析[J].计算机应用研究,2016(2):392-395.
[5] 李秀婷,刘凡,董纪昌,等.基于互联网搜索数据的中国流感监测[J].系统工程理论与实践,2013,33(12):3028-3034.
[6] GINSBERG J,MOHEBBI M H,PATEL R S,et al.Detecting Influenza Epidemics Using Search Engine Query Data[J].Nature,2008,457(7232):1012-1014.
[7] 王若佳,李培.基于互联网搜索数据的流感监测模型比较与优化[J].图书情报工作,2016(18):122-132.
[8] 焦利民,许刚,赵素丽,等.基于LUR的武汉市PM2.5浓度空间分布模拟[J].武汉大学学报(信息科学版),2015,40(8):1088-1094.
[9] 赵阳阳,刘纪平,徐胜华,等.一种基于半监督学习的地理加权回归方法[J].测绘学报,2017,46(1):123-129.
[10] 张金牡,刘彪,吴波,等.应用改进的时空地理加权模型分析城市住宅价格变化[J].东华理工大学学报(自然科学版),2010,33(1):53-59.
[11] HUANG B,WU B,BARRY M.Geographically and Temporally Weighted Regression for Modeling Spatio-temporal Variation in House Prices[J].International Journal of Geographical Information Science,2010,24(3):383-401.
[12] 董晓春,李琳,徐文体,等.特定关键词及百度指数与流感病毒活动相关性分析[J].中国公共卫生,2016(11):1543-1546.
[13] YUAN Q,NSOESUE E O,LV B,et al.Monitoring Influenza Epidemics in China with Search Query from Baidu[J].Plos One,2013,8(5):e64323.
[14] 王旭,林征,张志,等.基于GWR模型的北极滨海平原融冻湖表面温度空间分布模拟[J].武汉大学学报(信息科学版),2016,41(7):918-924.
[15] 黄达沧.基于搜索引擎数据的手足口病监测[D].长春:东北师范大学,2015:27-32.
[16] 覃文忠,王建梅,刘妙龙.混合地理加权回归模型算法研究[J].武汉大学学报(信息科学版),2007,32(2):115-119.
[17] ZHANG H,GUO L,CHEN J,et al.Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model[J].Chinese Geographical Science,24(2):191-204.
[18] LAMPOS V,CRISTIANINI N.Tracking the Flu Pandemic by Monitoring the Social Web[C]//2010 Second International Workshop on Cognitive Information Processing(CIP).Elba:IEEE,2010:411-416.