SARS panic effect-played a more significant role and pushed the external flow to the peak. (3) External flow and its three typical kinds showed obvious spatial heterogeneity, such as self-spreading flow (spatial displacement of SARS cases only within the province or municipality of onset and medical locations); hospitalized flow (spatial displacement of SARS cases that had been seen by a hospital doctor); and migrant flow (spatial displacement of SARS cases among migrant workers). (4) Internal and external flow tended to occur in younger groups, and occupational differentiation was particularly evident. Low-income groups of male migrants aged 19-5 years were the main routes of external flow. Conclusions During 2002-003, SARS in-out flow played an important role in countrywide transmission of the disease in Mainland China. The flow had obvious spatial heterogeneity, which was influenced by migrants-behavior characteristics. In addition, the Chinese holiday effect led to irregular spread of SARS, but the panic effect was more apparent in the middle and late stages of the epidemic. These findings constitute valuable input to prevent and control future serious infectious diseases like SARS." />
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Spatial pattern of severe acute respiratory syndrome in-out flow in 2003 in Mainland China
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  • 作者:Chengdong Xu ; Jinfeng Wang ; Li Wang ; Chunxiang Cao
  • 关键词:In ; out flow ; Mainland China ; SARS
  • 刊名:BMC Infectious Diseases
  • 出版年:2014
  • 出版时间:December 2014
  • 年:2014
  • 卷:14
  • 期:1
  • 全文大小:2,747 KB
  • 参考文献:1. Mills, CE, Robins, JM, Lipsitch, M (2004) Transmissibility of 1918 pandemic influenza. Nature 432: pp. 904-906 CrossRef
    2. McCloskey, B, Zumla, A, Stephens, G, Heymann, DL, Memish, ZA (2013) Applying lessons from SARS to a newly identified coronavirus. Lancet Infect Dis 13: pp. 384-385 CrossRef
    3. Alcorn, T (2013) As H7N9 spreads in China, experts watch and wait. Lancet 381: pp. 1347 CrossRef
    4. Summary of probable SARS cases with onset of illness from 1 November 2002 to 31 July 2003 [http://www.who.int/csr/sars/country/table2004_04_21/en/index.html]
    5. Hu A, Li C: Analysis of SARS impact on China’s economy. / 204th Xiangshan Science Conference workshop “SARS Prevention and Control-/em> 2003.
    6. Li, Z, Cui, H, Yang, H, Li, X (2003) SI Models and Piecewise SI Model on SARS Forecasting. J Remote Sensing 7: pp. 345-349
    7. Wang, JF, Meng, B, Zheng, X, Liu, J, Han, W, Wu, J, Liu, X, Li, X, Song, X (2005) Analysis on the multi-distribution and the major influencing factors on severe acute respiratory syndrome in Beijing. Chinese J Epidemiol 26: pp. 164
    8. Massad, E, Burattini, MN, Lopez, LF, Coutinho, FAB (2005) Forecasting versus projection models in epidemiology: the case of the SARS epidemics. Med Hypotheses 65: pp. 17-22 CrossRef
    9. Lipsitch, M, Cohen, T, Cooper, B, Robins, JM, Ma, S, James, L, Gopalakrishna, G, Chew, SK, Tan, CC, Samore, MH (2003) Transmission dynamics and control of severe acute respiratory syndrome. Science 300: pp. 1966-1970 CrossRef
    10. Riley, S, Fraser, C, Donnelly, CA, Ghani, AC, Abu-Raddad, LJ, Hedley, AJ, Leung, GM, Ho, LM, Lam, TH, Thach, TQ, Chau, P, Chan, KP, Leung, PY, Tsang, T, Ho, W, Lee, KH, Lau, EMC, Ferguson, NM, Anderson, RM (2003) Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science 300: pp. 1961-1966 CrossRef
    11. Wang, JF, Christakos, G, Han, WG, Meng, B (2008) Data-driven exploration of ‘spatial pattern-time process-driving forces-associations of SARS epidemic in Beijing. China J Public Health 30: pp. 234-244 CrossRef
    12. Cao, ZD, Zeng, DJ, Zheng, XL, Wang, QY, Wang, FY, Wang, JF, Wang, XL (2010) Spatio-temporal evolution of Beijing 2003 SARS epidemic. Sci China Earth Sci 53: pp. 1017-1028 CrossRef
    13. Fan, XS, Ying, LG (2005) An exploratory spatial data analysis of SARS epidemic in China. Adv Earth Sci 20: pp. 282-288
    14. Cao, ZD, Wang, JF, Gao, YG, Han, WG, Feng, XL, Zeng, G (2008) Risk factors and autocorrelation characteristics on SARS in Guangzhou. Acta Geograph Sin 63: pp. 981-993
    15. Yang, H, Li, X, Shi, H, Zhao, K, Han, L (2003) -Fly dots-spreading model of SARS along transportation. J Remote Sensing-Beijing 7: pp. 251-255
    16. Cao, C, Li, X, Yan, J, Jin, S (2003) Geo-spatial information and analysis of SARS spread trend. J Remote Sensing 7: pp. 241-244
    17. Peiris, JSM, Chu, CM, Cheng, VCC, Chan, KS, Hung, IFN, Poon, LLM, Law, KI, Tang, BSF, Hon, TYW, Chan, CS, Chan, KH, Ng, JSC, Zheng, BJ, Ng, WL, Lai, RWM, Guan, Y, Yuen, KY (2003) Clinical progression and viral load in a community outbreak of coronavirus-associated SARS pneumonia: a prospective study. Lancet 361: pp. 1767-1772 CrossRef
    18. Harris, SR, Feil, EJ, Holden, MTG, Quail, MA, Nickerson, EK, Chantratita, N, Gardete, S, Tavares, A, Day, N, Lindsay, JA (2010) Evolution of MRSA during hospital transmission and intercontinental spread. Science 327: pp. 469 CrossRef
    19. Eubank, S, Guclu, H, Kumar, VSA, Marathe, MV, Srinivasan, A, Toroczkai, Z, Wang, N (2004) Modelling disease outbreaks i
  • 刊物主题:Infectious Diseases; Parasitology; Medical Microbiology; Tropical Medicine; Internal Medicine;
  • 出版者:BioMed Central
  • ISSN:1471-2334
文摘
Background Severe acute respiratory syndrome (SARS) spread to 32 countries and regions within a few months in 2003. There were 5327 SARS cases from November 2002 to May 2003 in Mainland China, which involved 29 provinces, resulted in 349 deaths, and directly caused economic losses of $18.3 billion. Methods This study used an in-out flow model and flow mapping to visualize and explore the spatial pattern of SARS transmission in different regions. In-out flow is measured by the in-out degree and clustering coefficient of SARS. Flow mapping is an exploratory method of spatial visualization for interaction data. Results The findings were as follows. (1) SARS in-out flow had a clear hierarchy. It formed two main centers, Guangdong in South China and Beijing in North China, and two secondary centers, Shanxi and Inner Mongolia, both connected to Beijing. (2) “Spring Festival travel-strengthened external flow, but -a href='/search?dc.title=SARS&facet-content-type=ReferenceWorkEntry&sortOrder=relevance' class='reference-link webtrekk-track' gaCategory="Internal link" gaLabel="SARS" gaAction="reference keyword">SARS panic effect-played a more significant role and pushed the external flow to the peak. (3) External flow and its three typical kinds showed obvious spatial heterogeneity, such as self-spreading flow (spatial displacement of SARS cases only within the province or municipality of onset and medical locations); hospitalized flow (spatial displacement of SARS cases that had been seen by a hospital doctor); and migrant flow (spatial displacement of SARS cases among migrant workers). (4) Internal and external flow tended to occur in younger groups, and occupational differentiation was particularly evident. Low-income groups of male migrants aged 19-5 years were the main routes of external flow. Conclusions During 2002-003, SARS in-out flow played an important role in countrywide transmission of the disease in Mainland China. The flow had obvious spatial heterogeneity, which was influenced by migrants-behavior characteristics. In addition, the Chinese holiday effect led to irregular spread of SARS, but the panic effect was more apparent in the middle and late stages of the epidemic. These findings constitute valuable input to prevent and control future serious infectious diseases like SARS.

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