Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression

  • Zhi-Min Hong School of Sciences, Inner Mongolia University of Technology, Hohhot; Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Inner Mongolia, Hohhot, China. https://orcid.org/0000-0001-8189-527X
  • Hu-Hu Wang | zhiminhong@163.com School of Sciences, Inner Mongolia University of Technology, Hohhot; Institute for infectious disease and endemic disease control, Inner Mongolia Autonomous Region Center for Disease Control and Prevention, Hohhot, China. https://orcid.org/0000-0002-1716-674X
  • Yan-Juan Wang School of Sciences, Inner Mongolia University of Technology, Hohhot, China.
  • Wen-Rui Wang Institute for infectious disease and endemic disease control, Inner Mongolia Autonomous Region Center for Disease Control and Prevention, Hohhot, China.

Abstract

Hand, Foot, and Mouth Disease (HFMD) is a common and widespread infectious disease. Previous studies have presented evidence that climate factors, including the monthly averages of temperature, relative humidity, air pressure, wind speed and Cumulative Risk (CR) all have a strong influence on the transmission of HFMD. In this paper, the monthly time-lag geographically- weighted regression model was constructed to investigate the spatiotemporal variations of effect of climate factors on HFMD occurrence in Inner Mongolia Autonomous Region, China. From the spatial and temporal perspectives, the spatial and temporal variations of effect of climate factors on HFMD incidence are described respectively. The results indicate that the effect of climate factors on HFMD incidence shows very different spatial patterns and time trends. The findings may provide not only an indepth understanding of spatiotemporal variation patterns of the effect of climate factors on HFMD occurrence, but also provide helpful evidence for making measures of HFMD prevention and control and implementing appropriate public health interventions at the county level in different seasons.

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References

Brunsdon C, Fotheringham AS, Charlton ME, 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28: 281-298. DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Cheng Q, Bai L, Zhang Y, Zhang H, Wang S, Xie M, Zhao D, Su H, 2018. Ambient temperature, humidity and hand, foot, and mouth disease: a systematic review and meta-analysis. Sci Total Environ 625: 828-836. DOI: https://doi.org/10.1016/j.scitotenv.2018.01.006

Dong WH, Li XE, Yang P, Liao H, Wang XL, Wang QY, 2016. The effects of weather factors on hand, foot and mouth disease in Beijing. Sci Rep 6: 19247. DOI: https://doi.org/10.1038/srep19247

Duan SB and Li ZL, 2016. Spatial downscaling of MODIS land surface temperatures using geographically weighted regression: case study in northern China. IEEE T Geosci Remote 54: 6458-6469. DOI: https://doi.org/10.1109/TGRS.2016.2585198

Fotheringham AS, Brunsdon C, Charlton, ME, 2002. Geographically Weighted regression: the analysis of spatially varying relationships. Chichester, U.K.: Wiley.

Fotheringham AS, Crespo R, Yao J, 2015. Geographical and temporal weighted regression (GTWR). Geogr Anal 47: 431-452. DOI: https://doi.org/10.1111/gean.12071

Haining R, 2003. Spatial data analysis: theory and practice. Cambridge, U.K.: Cambridge University Press. DOI: https://doi.org/10.1017/CBO9780511754944

Hong ZM, Hao H, Wang XL, Wang WR, Wei LD, 2017. Analysis of spatio-temporal epidemiology of severe hand, foot and mouth disease in Inner Mongolia from 2009 to 2016. Chinese J Dis Control Prev 21: 1048-1051.

Hong ZM, Hao H, Li CY, Du W, Wei LD, Wang HH, 2018. Exploration of potential risks of hand, foot, and mouth disease in Inner Mongolia Autonomous Region, China using geographically weighted regression model. Sci Rep 8: 17707. DOI: https://doi.org/10.1038/s41598-018-35721-9

Huang B, Wu B, Barry M, 2010. Geographically and temporally weighted regression for spatio-temporal modeling of house prices. Int J Geogr Inf Sci 24: 383-401. DOI: https://doi.org/10.1080/13658810802672469

Huang JX, Wang JF, Bo YC, Xu CD, Hu MG, Huang DC, 2014. Identification of health risks of hand, foot and mouth disease in China using the geographical detector technique. Int J Environ Res Public Health 11: 3407-3423. DOI: https://doi.org/10.3390/ijerph110303407

Hu MG, Li ZJ, Wang JF, Jia L, Liao YL, Lai SJ, Guo YS, Zhao D, Yang WZ, 2012. Determinants of the incidence of hand, foot and mouth disease in China using geographically weighted regression models. Plos One 7: e38978. DOI: https://doi.org/10.1371/journal.pone.0038978

Lai CC, Jiang DS, Wu HM, Chen HH, 2016. A dynamic model for the outbreaks of hand, foot, and mouth disease in Taiwan. Epidemiol Infect 144: 1500-1511. DOI: https://doi.org/10.1017/S0950268815002630

Lu B, Charlton M, Harris P, Fotheringham AS, 2014. Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data. Int J Geogr Inf Sci 28: 660-681. DOI: https://doi.org/10.1080/13658816.2013.865739

Ma E, Lam T, Wong C, Chuang SK, 2010. Is hand, foot and mouth disease associated with meteorological parameters? Epidemiol Infect 138, 1779-1788. DOI: https://doi.org/10.1017/S0950268810002256

Onozuka D, Hashizume M, 2011. The influence of temperature and humidity on the incidence of hand, foot, and mouth disease in Japan. Sci Total Environ 410-411: 119-125 DOI: https://doi.org/10.1016/j.scitotenv.2011.09.055

Qian WY, Dang YG, 2011. Weakening buffer operator with variable weights based on the average growth rate and its properties. Syst Eng 1: 105-110.

Qian HK, Huo D, Wang XL, Jia L, Li XT, Li J, Gao ZY, Liu BW, Tian Y, Wu XN, Wang QY, 2016. Detecting spatial-temporal cluster of hand foot and mouth disease in Beijing, China, 2009-2014. BMC Infect Dis 16: 206. DOI: https://doi.org/10.1186/s12879-016-1547-6

Sham NM, Krishnarajah I, Ibrahim NA, Lye MS, 2014. Temporal and spatial mapping of hand, foot and mouth disease in Sarawak Malaysia. Geospatial Health 8: 503-507. DOI: https://doi.org/10.4081/gh.2014.39

Song XD, Brus DJ, Liu F, Li DC, Zhao YG, Yang JL, Zhang GL, 2016. Mapping soil organic carbon content by geographically weighted regression: a case study in the Heihe River Basin, China. Geoderma, 261: 11-22. DOI: https://doi.org/10.1016/j.geoderma.2015.06.024

Song C, Shi X, Bo YC, Wang JF, Wang Y, Huang DC, 2019. Exploring spatiotemporal nonstationary effects of climate factors on hand, foot, and mouth disease using Bayesian spatiotemporally varying coefficients (STVC) model in Sichuan, China. Sci Total Environ 648: 550-560. DOI: https://doi.org/10.1016/j.scitotenv.2018.08.114

Wabiri N, Shisana O, Zuma K, Freeman J, 2016. Assessing the spatial nonstationarity in relationship between local patterns of HIV infections and the covariates in South Africa: a geographically weighted regression analysis. Spatial and Spatio-Temporal Epidemiology, 16: 88-99. DOI: https://doi.org/10.1016/j.sste.2015.12.003

Wang JF, Xu CD, Tong SL, Chen HY, Yang WZ, 2013. Spatial dynamic patterns of hand-foot-mouth disease in the People's Republic of China. Geospatial Health 7: 381-390. DOI: https://doi.org/10.4081/gh.2013.95

Wang JJ, Cao ZD, Zeng Daniel D, Wang QY, 2017. Assessing local risk factors of Beijing hand-foot-mouth disease in China. Online J Public Health Informatics 9: e9. DOI: https://doi.org/10.5210/ojphi.v9i1.7759

Wang L, Wang CY, Zu WG, Cui LZ, 2018. Predictive analysis of ARIMA model on hand, foot, and mouth disease (HFMD) of Baoding City of Hebei Province. J Med Pest Control 34: 836-839.

Xing, W., Liao, Q., Viboud, C., Zhang, J., Sun, J., Wu, J.T., et al., 2014. Hand, foot, and mouth disease in China, 2008–12: an epidemiological study. Lancet Infect. Dis. 14: 308–318. DOI: https://doi.org/10.1016/S1473-3099(13)70342-6

Xu J, Zhao D, Su H, Xie M, Cheng J, Wang X, Li K, Yang H, Wen L, Wang B, 2016. Impact of temperature variability on childhood hand, foot and mouth disease in Huainan, China. Public Health 134: 86-94. DOI: https://doi.org/10.1016/j.puhe.2015.10.029

Yang F, Zhang T, Hu Y, Wang X, Du J, Li Y, Sun S, Sun X, Li Z, Jin Q, 2011. Survey of enterovirus infections from hand, foot and mouth disease outbreak in china, 2009. Virol J 8: 508. DOI: https://doi.org/10.1186/1743-422X-8-508

Yang H, Wu J, Cheng J, Wang X, Wen L, Li K, Su H, 2017. Is high relative humidity associated with childhood hand, foot, and mouth disease in rural and urban areas? Public Health 142: 201-207. DOI: https://doi.org/10.1016/j.puhe.2015.03.018

Yang YW, You EQ, Wu JJ, Zhang WY, Jin J, Zhou MM, Jiang CX, Huang F, 2018. Effects of relative humidity on childhood hand, foot, and mouth disease reinfection in Hefei, China. Sci Total Environ 630: 820-826. DOI: https://doi.org/10.1016/j.scitotenv.2018.02.262

Yu GQ, Li YH, Cai JS, Yu DM, Tang JX, Zhai WW, Wei Y, Chen SY, Chen QH, Qin J, 2019. Short-term effects of meteorological factors and air pollution on childhood hand-foot-mouth disease in Guilin, China. Sci Total Environ 646: 460-470. DOI: https://doi.org/10.1016/j.scitotenv.2018.07.329

Zhang W, Du Z, Zhang D, Yu S, Hao Y, 2016. Boosted regression tree model-based assessment of the impacts of meteorological drivers of hand, foot and mouth disease in Guangdong, China. Sci Total Environ 553: 366-371. DOI: https://doi.org/10.1016/j.scitotenv.2016.02.023

Zeng H, Lu J, Zheng H, Yi L, Guo X, Liu L, Rutherford S, Sun L, Tan X, Li H, Ke C, Lin J, 2015. The epidemiological study of coxsackievirus A6 revealing hand, foot and mouth disease epidemic patterns in Guangdong, China. Sci Rep 5: 10550. DOI: https://doi.org/10.1038/srep10550

Published
2020-12-29
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Original Articles
Keywords:
Hand, foot, and mouth disease, geographically weighted regression, time lag geographically weighted regression, spatiotemporal non-stationarity, China.
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How to Cite
Hong, Z.-M., Wang, H.-H., Wang, Y.-J., & Wang, W.-R. (2020). Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.849