Spatial-temporal analysis and visualization of scarlet fever in mainland China from 2004 to 2017

Submitted: 10 November 2019
Accepted: 25 March 2020
Published: 2 April 2020
Abstract Views: 2300
PDF: 989
HTML: 43
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.


This study retrospectively analyzed the spatio-temporal distribution and spatial clustering of scarlet fever in mainland China from 2004 to 2017. In recent years, the incidence of scarlet fever is increasing. Previous studies on the spatial distribution of scarlet fever in China are mainly focused at the provincial and municipal levels, and there is few systematic report on the spatial and temporal distribution characteristics of scarlet fever on the national level. Based on the incidence information of scarlet fever in mainland China between 2004 and 2017 collected from the China Center for Disease Control, this paper systematically explored the Spatio-temporal distribution of scarlet fever by three methods, contains spatial autocorrelation analysis, Spatio-temporal scanning analysis, and trend surface analysis. The results demonstrate that the incidence of scarlet fever varies by seasons, which is in line with double-peak distribution.The first peak generally occurs from May to June and the second one from November to December, while February and August is the lowest period of incidence. Trend surface analysis indicates that the incidence of scarlet fever in northern China is higher than the south, slightly higher in western compared to the east, and lower in the central part. Additionally, the results show that the clustering regions of scarlet fever centrally distributed in the northeast, northwest, north china and some provinces in the east, such as Zhejiang, Shanghai, Shandong, and Jiangsu.       



PlumX Metrics


Download data is not yet available.


Andrey DO, Posfay-Barbe KM, 2016. Re-emergence of scarlet fever: old players return? Expert Rev Anti Infect Ther 14:687-9. DOI:
Cheng J, Xu Z, Zhu R, Wang X, Jin L, Song J, Su H, 2014. Impact of diurnal temperature range on human health: a systematic review. Int J Biometeorol 58:2011-24. doi:10.1007/s00484-014-0797-5. DOI:
Duan Y, Huang XL, Wang YJ, Zhang JQ, Zhang Q, Dang YW, Wang J, 2016. Impact of meteorological changes on the incidence of scarlet fever in Hefei city, China. Int J Biometeorol 60:1543-50. DOI:
Jiang QW, Zhao F, 2011. [Application of spatial autocorrelation method in epidemiology] [Article in Chinese]. Chin J Epidemiol 32:539-46.
Kirby RS, Delmelle E, Eberth JM, 2017. Advances in spatial epidemiology and geographic information systems. Ann Epidemiol 27:1–9. doi:10.1016/j.annepidem.2016.12.001. DOI:
Kong DC, Chen J, Wang Y, Zhu YY, Zheng YX, Pan H, Wu HY, 2017. [Epidemiologic characteristics of scarlet fever in Shanghai, 2005-2015]. [Article in Chinese]. Dis Surveillance 32:394-8.
Lau EH, Nishiura H, Cowling BJ, Ip DK, Wu JT, 2012. Scarlet fever outbreak, Hong Kong, 2011. Emerg Infect Dis 18:1700-2. doi: 10.3201/eid1810.120062. DOI:
Li LL, Jiang XH, Sui X, Ni DX, Jin LM, Feng ZJ, 2012. [Epidemiologic characteristics of scarlet fever in China, 2005-2011]. [Article in Chinese]. Chin J Public Health 28:826-7.
Liu YY, 2018. [Epidemiological Characteristics and Trend Prediction of Scarlet Fever from 2007 to 2016 in Changchun]. [Article in Chinese]. Jilin University.
Liu ZY, Bi ZQ, 2014. [A review on the advancement of aetiology and epidemiology of group A Streptococcus]. [Article in Chinese]. Chin J Epidemiol 35:752-754.
Lowen AC, Mubareka S, Steel J, Palese P, 2007. Influenza virus transmission is dependent on relative humidity and temperature. PLo S Pathog 3:1470-6. Available from: DOI:
Moran PAP, 1950. Notes on continuous stochastic phenomena. Biometrika. 37:17-23. doi: DOI:
National Bureau of Statistics of China. Sixth census data in China. Available from:
Ning SQ, Chen S, Cao L, Zhou TC, Wang WH, Wang S, Zhang Y, 2018. [Epidemiological characteristics and trend of scarlet fever in Shaanxi Province from 2010 to 2016]. [Article in Chinese]. Chin J Dis Con Prev 22:585-9.
Pan HH, Liu XQ, Yang M, Yuan H, 2016. [Epidemiological characteristics of scarlet fever in Jiangxi Province from 2004 to 2014] [Article in Chinese]. Mod Prev Med 43:577-9.
Tami A, Grillet ME, Grobusch MP, 2016. Applying geographical information systems (GIS) to arboviral disease surveillance and control: a powerful tool. Travel Med Infect Dis 14:9-10. doi:10.1016/j.tmaid.2016.01.002. DOI:
Tang JH, 2011. [Application of Geographic Information System technology in disease prevention and control]. [Article in Chinese]. Anhui J Prev Med 17:113-6.
Tsai P J, Lin M L, Chu C M, Perng CH, 2009. Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006. BMC Public Health 9:464. doi: 10.1186/1471-2458-9-464. DOI:
Wang L, Zhang L, Wang T, Yang SX, Kou ZQ, Fang M, Bi ZQ, Bi ZW, 2016. [Incidence trend and epidemiological characteristics of scarlet fever in Zibo City from 1956 to 2014]. [Article in Chinese]. Chin J Dis Con Prev 20:349-52.
Wang Y, Wei ZS, Lai CY, Wu F, Gao JJ, Zhang GQ, Li YL, Zhang WJ, 2019. [Epidemiological characteristics of scarlet fever in Haidian district of Beijing from 2011 to 2017]. [Article in Chinese]. Mod Prev Med 46:2895-8.
Wong SSY, Yuen KY, 2012. Streptococcus pyogenes and re-emergence of scarlet fever as a public health problem. Emerg Microbes Infect 1:1-10. doi: DOI:
Wong SSY, Yuen KY, 2018. The Comeback of Scarlet Fever. EBioMedicine 28:7-8. doi:10.1016/j.ebiom.2018.01.030. DOI:
Wu SS, Ma CN, Peng XM, Zhang DT, Wang QY, Yang P, 2017. [Characteristics on the onset features of scarlet fever in Beijing, 2006-2015]. [Article in Chinese]. Chin J Epidemiol 38:514-7. doi:10.3760/cma.j.issn.0254-6450.2017.04.020
Xue FZ, Wang JZ, Zhang JW, Zhang YJ, 2004. [Order-selecting methods for trend surface model of spatial distribution of disease]. [Article in Chinese]. Journal of Shandong University (Health Sciences) 2:125-30.
Yang SG, Dong HJ, Li FR, Xie SY, Cao HC, Xia SC, Yu Z, Li LJ, 2007. Report and analysis of a scarlet fever outbreak among adults through food-borne transmission in China. J Infect 55:419–24. DOI:
You YH, Song YY, Yan XM, Wang HB, Zhang MH, Tao XX, Li LL, Zhang YX, Jiang XH, Zhang BH, Zhou H, Xiao D, Jin LM, Feng ZJ, Luo FJ, Zhang JZ, 2013. Molecular epidemiological characteristics of streptococcus pyogenes strains involved in an outbreak of scarlet fever in China, 2011. Biomed Environ Sci 26:877–85.
You Y, Davies MR, Protani M, McIntyre L, Walker MJ, Zhang J, 2018. Scarlet fever epidemic in China caused by streptococcus pyogenes serotype M12: epidemiologic and molecular analysis. EBioMedicine 28:128-35. doi:10.1016/j.ebiom.2018.01.010. DOI:
Zeng G, Ding YP, Cheng YH, 1997. [Demonstration On Z-D phenomenon in the occurrence of infectious diseases]. [Article in Chinese]. Chin J Epidemiol 5:270-4. Available from:
Zhang MY, Lv Y, Liu TC, Yi SH, Zha WT, 2019. [Spatial-temporal analysis and short-term prediction of the incidence of dysentery in China]. [Article in Chinese]. Chinese Journal of Disease Control and Prevention 23:904-10.
Zhang Q, 2018. [The research on epidemic characteristics of scarlet fever and impact of meteorological factors on scarlet fever in Jiangsu Province]. [Article in Chinese]. Nanjing Medical University.

How to Cite

Li, W.- tong, Feng, R.- hua, Li, T., Du, Y.- bing, Zhou, N., Hong, X.- qin, Yi, S.- hui, Zha, W.- ting, & Lv, Y. (2020). Spatial-temporal analysis and visualization of scarlet fever in mainland China from 2004 to 2017. Geospatial Health, 15(1).

List of Cited By :

Crossref logo