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Spatiotemporal Analysis of Hand, Foot and Mouth Disease Data Using Time-Lag Geographically-Weighted Regression

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Spatiotemporal Analysis of Hand, Foot and Mouth Disease Data Using Time-Lag Geographically-Weighted Regression gh-2020_2 DEF.qxp_Hrev_master 14/01/21 23:37 Pagina 337 Geospatial Health 2020; volume 15:849 Spatiotemporal analysis of hand, foot and mouth disease data using time-lag geographically-weighted regression Zhi-Min Hong,1,2 Hu-Hu Wang,1,3 Yan-Juan Wang,1 Wen-Rui Wang3 1School of Sciences, Inner Mongolia University of Technology, Hohhot; 2Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling, Inner Mongolia, Hohhot; 3Institute for infectious disease and endemic disease control, Inner Mongolia Autonomous Region Center for Disease Control and Prevention, Hohhot, China effect of climate factors on HFMD occurrence, but also provide Abstract helpful evidence for making measures of HFMD prevention and Hand, Foot, and Mouth Disease (HFMD) is a common and control and implementing appropriate public health interventions widespread infectious disease. Previous studies have presented at the county level in different seasons. 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 transmis- Introduction sion of HFMD. In this paper, the monthly time-lag geographical- Hand, Foot and Mouthonly Disease (HFMD) is a common, acute ly-weighted regression model was constructed to investigate the infectious disease resulting in millions of cases and even death spatiotemporal variations of effect of climate factors on HFMD among children under five years old. It is caused by different occurrence in Inner Mongolia Autonomous Region, China. From species of enteroviruses, most commonly Coxsackievirus A16 and the spatial and temporal perspectives, the spatial and temporal Enterovirususe 71 (Yang et al., 2011; Zeng et al., 2015) that may variations of effect of climate factors on HFMD incidence are change with the variation of climate. In addition, human activity described respectively. The results indicate that the effect of cli- and contact frequency may greatly determine the eventual disease occurrence (Cheng et al., 2018). China is one of the most affected mate factors on HFMD incidence shows very different spatial pat- countries where HFMD has been made statutorily notifiable in terns and time trends. The findings may provide not only an in- May 2008 (Xing et al., 2014). The intensity and frequency of the depth understanding of spatiotemporal variation patterns of the HFMD outbreaks are also different from place to place due to the extensive area where the disease occurs in China, as there may be very different climatic and environmental conditions. Correspondence: Hu-Hu Wang, School of Sciences, Inner Mongolia Various research approaches have been used to investigate the University of Technology, Hohhot, 010051, China. spatial and temporal patterns of HFMD outbreaks and the risk fac- Tel.: +86.471.6575863 tors, such as climate factors and socio-economic environment. In E-mail: [email protected] the state of Sarawak in Malaysia, spatial and temporal distribu- tions of HFMD were studied based on Geographical Information Key words: Hand, foot, and mouth disease; geographically weighted Systems (GIS), (Sham et al., 2014). In China, time series analysis regression; time lag geographically weighted regression; spatiotempo- methods were adopted to achieve a short-term epidemic prediction ral non-stationarity; China. for HFMD occurrence (Lai et al., 2016; Wang et al., 2018), and space–time scanning was applied for the detection of spatiotempo- Acknowledgements: This work wasNon-commercial supported by the National Natural Science Foundation of China (No. 81860605); the Natural Science ral clusters and the identification of high-risk locations (Hong et Foundation of Inner Mongolia (No. 2020MS01005). Special thanks to al., 2017; Qian et al., 2016) revealing that HFMD occurrence the data set provided by the Inner Mongolia Autonomous Region shows autocorrelation characteristics. Using linear regression Center for Disease Control and Prevention. model, Ma et al. (2010) demonstrated that climate factors play important role in predicting HFMD activity, which could assist in Conflict of interests: The authors declare no potential conflict of interests. explaining the winter peak detected in recent years. The geograph- ical detector method was implemented to test the association Received for publication: 10 December 2020. between HFMD with a single climate factor and their interaction Accepted for publication: 19 August 2020. (Wang et al., 2013), while a distributed lag non-linear model was used to quantify the effect of the Average Temperature (AT) and ©Copyright: the Author(s), 2020 Licensee PAGEPress, Italy relative Humidity (AH) on HFMD incidence in Huainan and Geospatial Health 2020; 15:849 Hefei, China, respectively (Xu et al., 2016; Yang et al., 2017). The doi:10.4081/gh.2020.849 boosted regression tree model, combining the advances of the tra- ditional regression models and the machine-learning methods, This article is distributed under the terms of the Creative Commons were employed by Zhang et al., 2016 to determine the optimal lag Attribution Noncommercial License (CC BY-NC 4.0) which permits any for the climate factors at which the variance of HFMD occurrence noncommercial use, distribution, and reproduction in any medium, pro- was most explained, and to evaluate the effects of these climate vided the original author(s) and source are credited. factors on HFMD occurrence at the optimal lag. A Bayesian spa- [Geospatial Health 2020; 15:849] [page 337] gh-2020_2 DEF.qxp_Hrev_master 14/01/21 23:37 Pagina 338 Article tiotemporally varying coefficients model was used to detect the These data from each county were provided by the Inner Mongolia effect of climate factors on HFMD incidence in Sichuan, China Autonomous Region Statistics Bureau. The climate data, consist- (Song et al., 2019). The results showed that the epidemic patterns ing of the monthly Average Temperature (AT), relative humidity of HFMD are closely related to climate factors. Meanwhile, the (AH), Air Pressure (AP), wind speed (AW) and monthly cumula- impact of climate factors on HFMD is significantly different across tive precipitation (CR) for each county, came from the Ecological space and time. Therefore, the study on spatiotemporal variation and Agricultural Meteorology Center of Inner Mongolia patterns of the effect of climate factors on HFMD activity is of Autonomous Region. In addition, the geographic information of great importance to understand the transmission mechanism of each county, including name, code, and longitude and latitude, HFMD and take proper protection measures against HFMD at dif- came from the National Catalogue Service for Geographic ferent regions and times. Information. The Geographically Weighted Regression (GWR) model, orig- inally proposed by Brunsdon et al. (1996), is a popular and power- Preliminary hand, foot and mouth disease data processing ful tool in exploring spatial nonstationarity of a regression relation- Consideration that the number of reported HFMD cases in ship. Subsequently, GWR models with their variants have been each county is closely associated with the size of the exposed pop- applied to various fields for geo-referenced data analysis. In addi- ulation in that county, it was felt suitable to use the monthly tion of the recent applications in, for instance, hedonic house price Cumulative Incidence (CI) in each county as the response variable modelling (Lu et al., 2014), geology (Duan and Li, 2016), ecology to model the relationship to the climate factors. The monthly CI is (Song et al., 2016) and public health (Wabiri et al., 2016), GWR defined by the ratio of the number of monthly reported HFMD models with their various variants have also been employed to cases O(i) under 9 years old to the size of the total population P(i) detect the association between HFMD incidence and climate fac- of this age group at risk in that county. However, the crude CI may tors, social and economic factors, air pollutants, etc. (Hu et al., suffer from the problem of zero CI values, mainly caused by the 2012; Dong et al., 2016; Wang et al., 2017; Hong et al., 2018; Yu geographic coded error oronly missing reports. Here, we adopted a et al., 2019). In the GWR model, temporal effects are assumed to hierarchical Bayesian model with both structured and unstructured be constant over space because the weighting matrix is calculated random effects (Haining 2003; Huang et al., 2014) to adjust the only based on spatial information. In fact, however, there is possi- crude CI of HFMD. Subsequently, the adjusted monthly CI was bility that the effect of explanatory variables on the response vari- used as the response variable for analysis. The adjusting process is able is not only spatially non-stationary, but also temporally so, use described as follows illustrating the spatial distribution and tempo- meaning that the underlying process is spatiotemporally non-sta- ral trend of the values of the adjusted CI: tionary. Time is also an essential dimension relating to various th Let r(i) be the risk probability that a child in the i county physical processes that often provides valuable information. To fill infects HFMD. The hierarchical Bayesian model is then formulat- this gap, Huang et al. (2010) proposed a so-called Geographically ed as: and Temporally Weighted Regression model (GTWR) by incorpo- rating the time factor into the GWR model. Fotheringham et al. (2015) also extended GWR to a new version of GTWR (here called (Eq. 1) TL-GWR) by separating the selection of the optimal spatial band- width from that of the temporal bandwidth. Only previous time where m represents the mean effect of disease risk; e(i) indepen- (lag time) neighbours are taken into account and their impact on 2 dently and identically distributed N(0,σ e) and captures the spatial the current state are estimated in the TL-GWR model. This model unstructured nonstationarity; and υ(i) a spatial structured random is useful because a set of spatial bandwidths are selected adaptive- variable describing the spatial dependence of the relative risk and ly at different time points.
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