Application of spatial filtering model based on different spatial weight settings in hand-foot-mouth disease incidence data

CHEN Xi; LI Ke; YIN Yun; LIU Yuanhua; HONG Jie; SHI Jin; HUANG Jiaqi; ZHAO Zheng; XU Jiayao; YUAN Rui; ZHANG Zhijie

doi:10.16462/j.cnki.zhjbkz.2023.11.004

Volume 27 Issue 11

Nov. 2023

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2023 > 27(11): 1262-1267

CHEN Xi, LI Ke, YIN Yun, LIU Yuanhua, HONG Jie, SHI Jin, HUANG Jiaqi, ZHAO Zheng, XU Jiayao, YUAN Rui, ZHANG Zhijie. Application of spatial filtering model based on different spatial weight settings in hand-foot-mouth disease incidence data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1262-1267. doi: 10.16462/j.cnki.zhjbkz.2023.11.004

Citation:

CHEN Xi, LI Ke, YIN Yun, LIU Yuanhua, HONG Jie, SHI Jin, HUANG Jiaqi, ZHAO Zheng, XU Jiayao, YUAN Rui, ZHANG Zhijie. Application of spatial filtering model based on different spatial weight settings in hand-foot-mouth disease incidence data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1262-1267. doi: 10.16462/j.cnki.zhjbkz.2023.11.004

Citation:

CHEN Xi, LI Ke, YIN Yun, LIU Yuanhua, HONG Jie, SHI Jin, HUANG Jiaqi, ZHAO Zheng, XU Jiayao, YUAN Rui, ZHANG Zhijie. Application of spatial filtering model based on different spatial weight settings in hand-foot-mouth disease incidence data[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(11): 1262-1267. doi: 10.16462/j.cnki.zhjbkz.2023.11.004

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Application of spatial filtering model based on different spatial weight settings in hand-foot-mouth disease incidence data

doi: 10.16462/j.cnki.zhjbkz.2023.11.004

Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China

Funds:

National Natural Science Foundation of China 81973102

More Information

Corresponding author: ZHANG Zhijie, E-mail: epistat@gmail.com
Received Date: 2022-09-14
Rev Recd Date: 2023-01-27

Available Online: 2023-11-20

Publish Date: 2023-11-10

Abstract

Abstract

Objective To study the application of spatial filtering model in the incidence data of hand-foot-mouth disease (HFMD) in East China given different spatial weight, and to determine its applicability by comparing the effects of different spatial models. Methods The incidence data of hand, foot and mouth disease in East China in 2009 were collected and the related influencing factors were identified. Four different spatial weight matrices were decomposed using the eigenvector spatial filtering method (ESF), and the eigenvectors were determined according to Moran′s I(MI) value and stepwise regression, which was introduced as the spatial filter into the model. The effects of different weight matrices were compared by Akaike information criterion (AIC), deviance information criterion (DIC) and Root Mean Square Error (RMSE). Finally, the spatial filtering model based on the optimal weight matrix was compared with the Bayesian spatial model in terms of the fitting value, standard deviation and confidence interval of the model coefficients. Results There were a total of 403 607 HFMD cases reported in East China in 2009, most of which concentrated in the west of Shandong Province and the southeast of Zhejiang Province. According to MI test, HFMD exhibited spatial correlation in East China. After the spatial filter was introduced into the normal negative binomial distribution model, the residual of the spatial filter model ceased to show spatial autocorrelation (MI were -0.11, -0.15, -0.08 and -0.09, respectively, all P>0.05), and the spatial autocorrelation was effectively removed. The Rook weight matrix was considered the optimal weight matrix. Although, the regression coefficient of the spatial filtering model under the optimal weight matrix were comparable to that of the Bayesian spatial model, the spatial filtering model was still significantly outweighed by the Bayesian spatial model in terms of standard deviation and confidence interval. Conclusions The spatial filtering model demonstrates the advantages of simple calculation and accurate results. Therefore, it can be applied to visualize the map patterns at different geographic scales from whole to local, and to reveal the underlying spatial structure of disease onset. It is also applicable as an effective alternative to traditional complex spatial models.
- Hand-foot-mouth disease,
- Spatial weight matrix,
- Spatial filtering model,
- Spatial epidemiology

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References(13)

References

[1]	李鹏, 葛淼, 王聪霞, 等. 2008—2014年中国大陆手足口病的时空变化规律[J]. 南京医科大学学报(自然科学版), 2018, 38(3): 380-385. DOI: 10.7655/NYDXBNS20180321. Li P, Ge M, Wang CX, et al. Temporal-spatial variation of hand-foot-mouth disease in 2008 to 2014, China[J]. Acta Universitatis Medicinalis Nanjing (Natural Science), 2018, 38(3): 380-385. DOI: 10.7655/NYDXBNS20180321.
[2]	李颉, 郑步云, 王劲峰. 2008—2018年中国手足口病时空分异特征[J]. 地球信息科学学报, 2021, 23(3): 419-430. DOI: 10.12082/dqxxkx.2021.190778. Li J, Zheng BY, Wang JF. Spatial-temporal heterogeneity of hand, foot and mouth disease in China from 2008 to 2018[J]. J Geo Inf Sci, 2021, 23(3): 419-430. DOI: 10.12082/dqxxkx.2021.190778.
[3]	Du ZC, Lawrence WR, Zhang WJ, et al. Bayesian spatiotemporal analysis for association of environmental factors with hand, foot, and mouth disease in Guangdong, China[J]. J Geo-Inf Sci, 2018, 8(1): 15147. DOI: 10.1038/s41598-018-33109-3.
[4]	He XY, Dong SJ, Li LP, et al. Using a Bayesian spatiotemporal model to identify the influencing factors and high-risk areas of hand, foot and mouth disease(HFMD) in Shenzhen[J]. PLoS Negl Trop Dis, 2020, 14(3): e0008085. DOI: 10.1371/journal.pntd.0008085.
[5]	王薇, 刘韫宁, 殷鹏, 等. 不同空间权重矩阵对我国心血管疾病死亡空间自相关分析的影响[J]. 中华流行病学杂志, 2021, 42(8): 1437-1444. DOI: 10.3760/cma.j.cn112338-20201102-01293. Wang W, Liu YN, Yin P, et al. Influences of using different spatial weight matrices in analyzing spatial autocorrelation of cardiovascular diseases mortality in China[J]. Chin J Epidemiol, 2021, 42(8): 1437-1444. DOI: 10.3760/cma.j.cn112338-20201102-01293.
[6]	Goodchild MF. What problem? Spatial autocorrelation and geographic information science[J]. Geogr Anal, 2009, 41(4): 411-417. DOI: 10.1111/j.1538-4632.2009.00769.x.
[7]	Tiefelsdorf M, Griffith DA. Semiparametric filtering of spatial autocorrelation: the eigenvector approach[J]. Environ Plan A, 2007, 39(5): 1193-1221. DOI: 10.1068/a37378.
[8]	Hong SZ, Liu F, Bauer C, et al. Intra-area factors dominate the spatio-temporal transmission heterogeneity of hand, foot, and mouth disease in China: a modelling study[J]. Sci Total Environ, 2021, 775: 145859. DOI: 10.1016/j.scitotenv.2021.145859.
[9]	Song C, Shi X, Bo Y, et al. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian spatiotemporally varying coefficients (STVC) model in Sichuan, China[J]. Sci Total Environ, 2019, 648: 550-560. DOI: 10.1016/j.scitotenv.2018.08.114.
[10]	张湘雪, 王丽, 尹礼唱, 等. 京津唐地区HFMD时空变异分析与影响因子探测[J]. 地球信息科学学报, 2019, 21(3): 398-406. DOI: 10.12082/dqxxkx.2019.180517. Zhang XX, Wang L, Yin LC, et al. Spatiotemporal variation analysis and risk determinants of hand, foot and mouth disease in Beijing-Tianjin-Tangshan, China[J]. J Geo-Inf Sci, 2019, 21(3): 398-406. DOI: 10.12082/dqxxkx.2019.180517.
[11]	Patuelli R, Griffith DA, Tiefelsdorf M, et al. The use of spatial filtering techniques: the spatial and space-time structure of German unemployment data[J]. SSRN Electron J, 2006. DOI: 10.2139/ssrn.893540.
[12]	赵文铀, 郑良芳, 张辉国, 等. 基于Bayesian-INLA的宏观因素对手足口病疫情的时空响应分析[J]. 中国卫生统计, 2020, 37(1): 6-9. DOI: 10.3969/j.issn.1002-3674.2020.01.002. Zhao WY, Zheng LF, Zhang HG, et al. Spatio-temporal analysis of macro factors against hand, foot and mouth disease: a Bayesian-INLA approach[J]. Chinese Journal of Health Statistics, 2020, 37(1): 6-9. DOI: 10.3969/j.issn.1002-3674.2020.01.002.
[13]	Tian L, Liang FC, Xu MM, et al. Spatio-temporal analysis of the relationship between meteorological factors and hand-foot-mouth disease in Beijing, China[J]. BMC Infect Dis, 2018, 18(1): 1-10. DOI: 10.1186/s12879-018-3071-3.