Application of spatial epidemiology in research on the transmission risk of parasitic diseases

doi:10.12140/j.issn.1000-7423.2021.01.015

Abstract

Abstract:

Spatial epidemiology is a discipline that studies the spatial distribution of diseases in human population and the influencing factors, including disease mapping, clustering analysis and related factor analysis. With the development of modern spatial technology, spatial epidemiology has been widely used in the visual analysis, hotspot analysis, identification of risk factors and prediction of transmission risk of parasitic diseases. This paper mainly introduces the spatial distribution pattern, spatial interpolation and spatial modeling algorithms used in the research of transmission risk of parasitic diseases.

Key words: Spatial epidemiology, Parasitic diseases, Spatial analysis, Spatial model

CLC Number:

GONG Yan-feng, ZHENG Jin-xin, HU Xiao-kang, XIA Shang, LI Shi-zhu. Application of spatial epidemiology in research on the transmission risk of parasitic diseases[J]. CHINESE JOURNAL OF PARASITOLOGY AND PARASITIC DISEASES, 2021, 39(1): 101-106.

References 59

[1]	Qian MB, Chen J, Bergquist R, et al. Neglected tropical diseases in the People’s Republic of China: progress towards elimination[J]. Infect Dis Poverty, 2019,8(1):86. doi: 10.1186/s40249-019-0599-4 pmid: 31578147
[2]	Feng J, Zhou SS. From control to elimination: the historical retrospect of malaria control and prevention in China[J]. Chin J Parasitol Parasit Dis, 2019,37(5):505-513. (in Chinese)
	( 丰俊, 周水森. 从控制走向消除: 我国疟疾防控的历史回顾[J]. 中国寄生虫学与寄生虫病杂志, 2019,37(5):505-513.)
[3]	Li SZ, Xu J, Lü S, et al. Interpretation of the three-year tough action plan for endemic diseases control(2018—2020): schistosomiasis[J]. Chin J Schisto Control, 2018,30(6):601-604. (in Chinese)
	( 李石柱, 许静, 吕山, 等. 《地方病防治专项三年攻坚行动方案(2018—2020年)》解读: 血吸虫病[J]. 中国血吸虫病防治杂志, 2018,30(6):601-604.)
[4]	Chen ZY, Xie HG, Lin YY, et al. First imported case of Rhodesian human African trypanosomiasis in China[J]. Chin J Zoonoses, 2018,34(4):388-390. (in Chinese)
	( 陈朱云, 谢汉国, 林耀莹, 等. 首例输入性布氏罗得西亚锥虫病病例分析[J]. 中国人兽共患病学报, 2018,34(4):388-390.)
[5]	Liu K, Li XL, Shao ZJ. Spatial epidemiology plays an important role in control and prevention of diseases[J]. Chin J Epidemiol, 2018,39(9):1143-1145. (in Chinese)
	( 刘昆, 李新楼, 邵中军. 空间流行病学在疾病预防控制工作中发挥着重要作用[J]. 中华流行病学杂志, 2018,39(9):1143-1145.)
[6]	Zhou YB. Strengthening the research and application of spatial epidemiology to promote precise prevention and control of parasitic diseases in China[J]. Chin J Schisto Control, 2019,31(4):356-357. (in Chinese)
	( 周艺彪. 加强空间流行病学研究与应用助推我国寄生虫病精准防控[J]. 中国血吸虫病防治杂志, 2019,31(4):356-357.)
[7]	Simoonga C, Utzinger J, Brooker S, et al. Remote sensing, geographical information system and spatial analysis for schistosomiasis epidemiology and ecology in Africa[J]. Parasitology, 2009,136(13):1683-1693. doi: 10.1017/S0031182009006222 pmid: 19627627
[8]	Wang ER, Zhao ZY, Miao CH, et al. A spatiotemporal analysis of schistosomiasis in Hunan Province, China[J]. Asia- Pac J Public Heal, 2018,30(6):521-531.
[9]	Fletcher-Lartey SM, Caprarelli G. Application of GIS technology in public health: successes and challenges[J]. Parasitology, 2016,143(4):401-415. doi: 10.1017/S0031182015001869 pmid: 26831619
[10]	Tewara MA, Mbah-Fongkimeh PN, Dayimu A, et al. Small-area spatial statistical analysis of malaria clusters and hotspots in Cameroon; 2000—2015[J]. BMC Infect Dis, 2018,18(1):1-15. doi: 10.1186/s12879-017-2892-9 pmid: 29291713
[11]	Wang Y, Zhuang D. A rapid monitoring and evaluation method of schistosomiasis based on spatial information technology[J]. Int J Environ Res Public Health, 2015,12(12):15843-15859. doi: 10.3390/ijerph121215025 pmid: 26703635
[12]	Nihei N. Analysis of distribution of vector-borne diseases using geographic information systems[J]. Nihon Eiseigaku Zasshi Jpn J Hyg, 2017,72(2):123-127.
[13]	Ferrao JL, Niquisse S, Mendes JM, et al. Mapping and modelling malaria risk areas using climate, socio-demographic and clinical variables in chimoio, Mozambique[J]. Int J Environ Res Public Health, 2018,15(4). DOI: 10.3390/ijerph15040795. doi: 10.3390/ijerph15040814 pmid: 29690498
[14]	Zhang SL, Zhang K. Contrast study on moran and getis-ord indexes of local spatial autocorrelation indices[J]. J Geod Geodyn, 2007,27(3):31-34. (in Chinese)
	( 张松林, 张昆. 空间自相关局部指标Moran指数和G系数研究[J]. 大地测量与地球动力学, 2007,27(3):31-34.)
[15]	Zhang ZJ, Peng WX, Zhou YB, et al. Spatial autocorrelation analysis of the small-scale distribution of Oncomelania hupensis in marshland and lake regions[J]. Chin J Schisto Control, 2007,19(6):418-423. (in Chinese)
	( 张志杰, 彭文祥, 周艺彪, 等. 湖沼地区湖北钉螺小尺度分布的空间自相关分析[J]. 中国血吸虫病防治杂志, 2007,19(6):418-423.)
[16]	Ord JK, Getis A. Local spatial autocorrelation statistics: distributional issues and an application[J]. Geogr Anal, 1995,27(4):286-306. doi: 10.1111/gean.1995.27.issue-4
[17]	Liu WL, Kou ZQ, Chen BL, et al. Spatial distribution characteristics and spatial autocorrelation analysis of human brucellosis in Shandong Province from 2014 to 2016[J]. Chin J Dis Control Prev, 2018,22(9):897-901. (in Chinese)
	( 刘维量, 寇增强, 陈保立, 等. 山东省2014—2016年布鲁氏菌病空间分布特征和空间自相关分析[J]. 中华疾病控制杂志, 2018,22(9):897-901.)
[18]	Xia J, Cai S, Zhang H, et al. Spatial, temporal, and spatiotemporal analysis of malaria in Hubei Province, China from 2004—2011[J]. Malar J, 2015,14:145. doi: 10.1186/s12936-015-0650-2 pmid: 25879447
[19]	Wang YJ, Li SZ, Jiang QW, et al. Progress of spatial epidemiology applied to prevention and control of schistosomiasis[J]. Chin J Schisto Control, 2019,31(1):53-57. (in Chinese)
	( 王英鉴, 李石柱, 姜庆五, 等. 空间流行病空间流行病学在血吸虫病防控实践中的应用进展[J]. 中国血吸虫病防治杂志, 2019,31(1):53-57.)
[20]	Block R. Software review: scanning for clusters in space and time[J]. Soc Sci Comput Rev, 2007,25(2):272-278. doi: 10.1177/0894439307298562
[21]	Cao ZG, Li S, Zhao YE, et al. Spatio-temporal pattern of schistosomiasis in Anhui Province, East China: potential effect of the Yangtze River-Huaihe river water transfer project[J]. Parasitol Int, 2018,67(5):538-546. doi: 10.1016/j.parint.2018.05.007 pmid: 29753097
[22]	Zhu H, Cai SX, Liu JB, et al. A spatial analysis of human Schistosoma japonicum infections in Hubei, China, during 2009—2014[J]. Parasit Vectors, 2016,9(1):529. doi: 10.1186/s13071-016-1817-6 pmid: 27716421
[23]	Li T, He JG, Yang CH, et al. A comparative study on SaTScan and FleXScan software for spatial clustering analysis regarding the incidence of pulmonary tuberculosis[J]. Chin J Epidemiol, 2020,41(2):207-212. (in Chinese)
	( 李婷, 何金戈, 杨长虹, 等. SaTScan与FleXScan软件空间扫描统计量法在肺结核疫情空间聚集性研究中的应用比较[J]. 中华流行病学杂志, 2020,41(2):207-212.)
[24]	Xia C, Bergquist R, Lynn H, et al. Village-based spatio-temporal cluster analysis of the schistosomiasis risk in the Poyang Lake Region, China[J]. Parasit Vectors, 2017,10(1):136. doi: 10.1186/s13071-017-2059-y pmid: 28270197
[25]	Zhang Z, Clark AB, Bivand R, et al. Nonparametric spatial analysis to detect high-risk regions for schistosomiasis in Guichi, China[J]. Trans R Soc Trop Med Hyg, 2009,103(10):1045-1052. doi: 10.1016/j.trstmh.2008.11.012 pmid: 19117584
[26]	Davies TM. Jointly optimal bandwidth selection for the planar kernel-smoothed density-ratio[J]. Spatial Spatio-Temporal Epidemiol, 2013,5:51-65. doi: 10.1016/j.sste.2013.04.001
[27]	Naranjo-Lucena A, Munita Corbalán MP, Martínez-Ibeas AM, et al. Spatial patterns of Fasciola hepatica and Calicophoron daubneyi infections in ruminants in Ireland and modelling of C. daubneyi infection[J]. Parasites Vectors, 2018,11(1):531. doi: 10.1186/s13071-018-3114-z pmid: 30268155
[28]	Casartelli-Alves L, Amendoeira MR, Boechat VC, et al. Mapping of the environmental contamination of Toxoplasma gondii by georeferencing isolates from chickens in an endemic area in Southeast Rio de Janeiro State, Brazil[J]. Geospat Health, 2015,10(1):311. doi: 10.4081/gh.2015.311 pmid: 26054514
[29]	Lins TNB, Souza IB, Barros GMMDR, et al. Seroprevalence and spatial distribution of canine leishmaniasis in an endemic region in Brazil: how has the situation changed after 10 years?[J]. Rev Soc Bras Med Trop, 2018,51(5):680-682. doi: 10.1590/0037-8682-0087-2018 pmid: 30304278
[30]	Yan SP. Epidemic situation and spatial analysis of leptospirosis in Hunan Province[D]. Changsha: Central South University, 2010. ( in Chinese)
	( 颜仕鹏. 湖南省钩体病流行态势及空间分析[D]. 长沙: 中南大学, 2010.)
[31]	Ozturk D, Kilic F. Geostatistical approach for spatial interpolation of meteorological data[J]. An Acad Bras Cienc, 2016,88(4):2121-2136. doi: 10.1590/0001-3765201620150103 pmid: 27991954
[32]	Gomes ECS, Mesquitta MCS, Wanderley L, et al. Spatial risk analysis on occurrences and dispersal of Biomphalaria straminea in and endemic area for schistosomiasis[J]. J Vector Borne Dis, 2018,55(3):208. doi: 10.4103/0972-9062.249142 pmid: 30618447
[33]	Salimi M, Jesri N, Javanbakht M, et al. Spatio-temporal distribution analysis of zoonotic cutaneous leishmaniasis in Qom Province, Iran[J]. J Parasit Dis, 2018,42(4):570-576. doi: 10.1007/s12639-018-1036-5 pmid: 30538355
[34]	Cuadros DF, Sartorius B, Hall C, et al. Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data[J]. Int J Health Geogr, 2018,17(1):27. doi: 10.1186/s12942-018-0146-8 pmid: 29996876
[35]	Ngoen-Klan R, Moophayak K, Klong-Klaew T, et al. Do climatic and physical factors affect populations of the blow fly Chrysomya megacephala and house fly Musca domestica?[J]. Parasitol Res, 2011,109(5):1279-1292. doi: 10.1007/s00436-011-2372-x
[36]	Zheng JX, Liu L, Feng Y, et al. A review of spatial epidemiology with malaria surveillance and control in China[J]. Chin J Schisto Control, 2017,29(5):651-655. (in Chinese)
	( 郑金鑫, 刘璐, 冯云, 等. 空间流行病学在我国疟疾监测预警中的研究进展[J]. 中国血吸虫病防治杂志, 2017,29(5):651-655.)
[37]	Wei QB, Zhang LJ, Duan WB, et al. Global and geographically and temporally weighted regression models for modeling PM2.5 in Heilongjiang, China from 2015 to 2018[J]. Int J Environ Res Public Heal, 2019,16(24):5107.
[38]	Duarte-Cunha M, Almeida AS, Cunha GM, et al. Geographic weighted regression: applicability to epidemiological studies of leprosy[J]. Rev Soc Bras Med Trop, 2016,49(1):74-82. doi: 10.1590/0037-8682-0307-2015 pmid: 27163567
[39]	Yang D, Xu C, Wang J, et al. Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China[J]. BMC Public Health, 2017,17(1):66. doi: 10.1186/s12889-016-3994-9 pmid: 28077125
[40]	Zhang Y, Liu M, Wu SS, et al. Spatial distribution of tuberculosis and its association with meteorological factors in mainland China[J]. BMC Infect Dis, 2019,19(1):379. doi: 10.1186/s12879-019-4008-1 pmid: 31053104
[41]	Atique S, Chan TC, Chen CC, et al. Investigating spatio-temporal distribution and diffusion patterns of the dengue outbreak in Swat, Pakistan[J]. J Infect Public Health, 2018,11(4):550-557. doi: 10.1016/j.jiph.2017.12.003 pmid: 29287804
[42]	Hu QA, Zhang Y, Guo YH, et al. Small-scale spatial analysis of intermediate and definitive hosts of Angiostrongylus cantonensis[J]. Infect Dis Poverty, 2018,7:100. doi: 10.1186/s40249-018-0482-8 pmid: 30318019
[43]	Cheng G, Li D, Zhuang D, et al. The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis[J]. Acta Trop, 2016,164:194-207. doi: 10.1016/j.actatropica.2016.09.017 pmid: 27659095
[44]	Deng Y, Liu JP, Liu Y, et al. Spatial distribution estimation of PM_2.5 concentration in Beijing by applying Bayesian geographic weighted regression model[J]. Sci Surv Mapp, 2018,43(10):39-45, 59. (in Chinese)
	( 邓悦, 刘纪平, 刘洋, 等. 北京PM_2.5浓度空间分布的贝叶斯地理加权回归模拟[J]. 测绘科学, 2018,43(10):39-45, 59.)
[45]	Fan WJ. Spatio-temporal epidemiology analysis of the relationship between typhoid/paratyphoid fever and urbanization in Zhejiang Province from 2006 to 2014[D]. Hangzhou: Zhejiang University, 2016. ( in Chinese)
	( 樊文洁. 浙江省2006—2014年伤寒/副伤寒与城市化之间关系时空流行病学分析[D]. 杭州: 浙江大学, 2016.)
[46]	Flórez-Lozano K, Navarro-Lechuga E, Llinás-Solano H, et al. Spatial distribution of the relative risk of Zika virus disease in Colombia during the 2015—2016 epidemic from a Bayesian approach[J]. Int J Gynaecol Obstet, 2020,148(Suppl 2):55-60. doi: 10.1002/ijgo.v148.s2
[47]	Zhang Y, Wang X, Li Y, et al. Spatiotemporal analysis of influenza in China, 2005—2018[J]. Sci Rep, 2019,9(1):19650. doi: 10.1038/s41598-019-56104-8 pmid: 31873144
[48]	Jackson C, Presanis A, Conti S, et al. Value of information: sensitivity analysis and research design in Bayesian evidence synjournal[J]. J Am Stat Assoc, 2019,114(528):1436-1449. doi: 10.1080/01621459.2018.1562932 pmid: 32165869
[49]	Hu Y, Ward MP, Xia C, et al. Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach[J]. Sci Rep, 2016,6:24173. doi: 10.1038/srep24173 pmid: 27053447
[50]	Ouédraogo M, Samadoulougou S, Rouamba T, et al. Spatial distribution and determinants of asymptomatic malaria risk among children under 5 years in 24 districts in Burkina Faso[J]. Malar J, 2018,17(1):460. doi: 10.1186/s12936-018-2606-9 pmid: 30526598
[51]	Huang NB, Yang K, Shi XW, et al. Study on spatio- temporal pattern of mountainous Oncomelania hupensis snails at village scale in Eryuan County, Yunnan Province[J]. Chin J Schisto Control, 2014,26(2):137-140, 147. (in Chinese)
	( 黄宁波, 杨坤, 施学文, 等. 云南省洱源县山区村级尺度的钉螺时空分布格局研究[J]. 中国血吸虫病防治杂志, 2014,26(2):137-140, 147.)
[52]	Wang XH, Zhou XN, Wu XH, et al. Study on the spatio-temporal distribution of seroprevalence of Schistosoma japonicum[J]. Chin J Parasitol Parasit Dis, 2008,26(4):290-294, 298.
	( 王显红, 周晓农, 吴晓华, 等. 日本血吸虫血清学阳性率时空分布格局的初步研究[J]. 中国寄生虫学与寄生虫病杂志, 2008,26(4):290-294, 298.)
[53]	Carlos-Júnior Jr, Barbosa NPU, Moulton TP, et al. Ecological Niche Model used to examine the distribution of an invasive, non-indigenous coral[J]. Mar Environ Res, 2015,103:115-124. doi: 10.1016/j.marenvres.2014.10.004 pmid: 25465286
[54]	Yu HY, Sun CK, Huo X, et al. Predicting the transmission risk of H7N9 using ecological niche modeling[J]. Mod Prev Med, 2019,46(2):206-210, 229. (in Chinese)
	( 余慧燕, 孙长奎, 霍翔, 等. 基于生态位模型的人感染H7N9禽流感病毒潜在风险区预测分析[J]. 现代预防医学, 2019,46(2):206-210, 229.)
[55]	McIntyre S, Rangel EF, Ready PD, et al. Species-specific ecological niche modelling predicts different range contractions for Lutzomyia intermedia and a related vector of Leishmania braziliensis following climate change in South America[J]. Parasit Vectors, 2017,10(1):157. doi: 10.1186/s13071-017-2093-9 pmid: 28340594
[56]	Kulkarni MA, Desrochers RE, Kajeguka DC, et al. 10 years of environmental change on the slopes of mount Kilimanjaro and its associated shift in malaria vector distributions[J]. Front Public Health, 2016,4:281. doi: 10.3389/fpubh.2016.00281 pmid: 28066759
[57]	Niu YN, Li RD, Qiu J, et al. Identifying and predicting the geographical distribution patterns of Oncomelania hupensis[J]. Int J Environ Res Public Heal, 2019,16(12):2206.
[58]	Xia CC, Lu CF, Li S, et al. Maximum entropy model versus remote sensing-based methods for extracting Oncomelania hupensis snail habitats[J]. Chin J Schisto Control, 2017,29(1):12-17, 23. (in Chinese)
	( 夏聪聪, 卢成芳, 李思, 等. 基于最大熵模型和遥感的两种钉螺孳生地提取方法的比较[J]. 中国血吸虫病防治杂志, 2017,29(1):12-17, 23.)
[59]	Huang D, Li RD, Qiu J, et al. Geographical environment factors and risk mapping of human cystic echinococcosis in Western China[J]. Int J Environ Res Public Heal, 2018,15(8):1729.