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. 2010 May 12;53(7):1017–1028. doi: 10.1007/s11430-010-0043-x

Spatio-temporal evolution of Beijing 2003 SARS epidemic

ZhiDong Cao 1, DaJun Zeng 1,✉, XiaoLong Zheng 1, QuanYi Wang 2, FeiYue Wang 1, JinFeng Wang 3, XiaoLi Wang 2
PMCID: PMC7104599  PMID: 32288761

Abstract

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran’s I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS’s spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

Keyword: severe acute respiratory syndrome (SARS), Beijing, morbidity rate, spatial analysis, spatio-temporal evolution, control measures

References

  • [1].Oxford J. S., Bossuyt S., Lambkin R. A new infectious disease challenge: Urbani severe acute respiratory syndrome (SARS) associated coronavirus. Immunology. 2003;109:326–328. doi: 10.1046/j.1365-2567.2003.01684.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [2].Marra M. A., Jones S. J., Astell C. R., et al. The genome sequence of the SARS-associated coronavirus. Science. 2003;300:1399–1404. doi: 10.1126/science.1085953. [DOI] [PubMed] [Google Scholar]
  • [3].Tan Y. M., Chow P. K. H., Soo K. C. Severe acute respiratory syndrome—Clinical outcome after inpatient outbreak of SARS in Singapore. Br Med J. 2003;326:1394–1394. doi: 10.1136/bmj.326.7403.1394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Lipsitch M., Cohen T., Cooper B., et al. Transmission dynamics and control of severe acute respiratory syndrome. Science. 2003;300:1966–1970. doi: 10.1126/science.1086616. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Keogh-Brown M. R., Smith R. D. The economic impact of SARS: How does the reality match the predictions? Health Policy. 2008;88:110–120. doi: 10.1016/j.healthpol.2008.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Chen W. K., Wu H. W., Lin C. C., et al. Emergency department response to SARS, Taiwan. Emerging Infect Dis. 2005;11:1067–1073. doi: 10.3201/eid1107.040917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Pang X. H., Zhu Z. H., Xu F. J., et al. Evaluation of control measures implemented in the severe acute respiratory syndrome outbreak in Beijing, 2003. JAMA. 2003;290:3215–3221. doi: 10.1001/jama.290.24.3215. [DOI] [PubMed] [Google Scholar]
  • [8].Liang W. N., Liu M., Chen Q., et al. Assessment of impacts of public health interventions on the SARS epidemic in Beijing in terms of the intervals between its symptom onset, hospital admission, and notification. Biomed Environ Sci. 2005;18:153–158. [PubMed] [Google Scholar]
  • [9].Liang W. N., Liu M., Chen Q., et al. Description about onset date,visit date,report date of severe acute respiratory syndromes(SARS) to observe impact of public health interventions (in Chinese) China Public Health. 2004;20:129–130. [Google Scholar]
  • [10].Zhang Z. B. The outbreak pattern of SARS cases in China as revealed by a mathematical model. Ecol Modell. 2007;204:420–426. doi: 10.1016/j.ecolmodel.2007.01.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Wang H. Y., Rong F., Ke F. J., et al. Control dynamics of severe acute respiratory syndrome transmission. Chin Sci Bull. 2003;48:2684–2687. doi: 10.1007/BF02901756. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Cai Q. C., Jiang Q. W., Xu Q. F., et al. To develop a model on severe acute respiratory syndrome epidemics to quantitatively evaluate the effectiveness of intervention measures (in Chinese) Chin J Epidemiol. 2005;26:153–158. [PubMed] [Google Scholar]
  • [13].Han W. G., Wang J. F., Liu X. H. Back Analyzing Parameters and Predicting Trend of SAR transmission (in Chinese) Adv Earth Sci. 2004;19:925–930. [Google Scholar]
  • [14].Zong Y. G., Wang L., Qu X. L. Monte-Carlo simulation based approach for the study of SARS spatial-temporal scenarios in Beijing (in Chinese) Geogr Res. 2004;23:815–824. [Google Scholar]
  • [15].Shi Y. L. Stochastic dynamic model of SARS spreading. Chin Sci Bull. 2003;13:1373–1377. doi: 10.1007/BF03184164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Chen Q. Z. Application of SIR Model in forecasting and analyzing for SARS (in Chinese) J Beijing Med Univ. 2003;35:75–80. [PubMed] [Google Scholar]
  • [17].Wu J., Xu F. J., Zhou W. G., et al. Risk factors for SARS among persons without known contact with SARS patients, Beijing, China. Emerging Infect Dis. 2004;10:210–216. doi: 10.3201/eid1002.030730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Wang Z., Cai D., Li S., et al. On season risk of the prevalence of SARS in China (in Chinese) Geogr Res. 2003;22:541–550. [Google Scholar]
  • [19].Li R. W. K., Leung K. W. C., Sun F. C. S., et al. Severe Acute Respiratory Syndrome (SARS) and the GDP. Part I: Epidemiology, virology, pathology and general health issues. Br Dent J. 2004;197:77–80. doi: 10.1038/sj.bdj.4811469. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Zhu Y. B., Ge Q. S., Wei X. A., et al. A study on the effect of the SARS epidemic on the international tourist arrivals in China (in Chinese) Geogr Res. 2003;22:551–559. [Google Scholar]
  • [21].Meng B., Wang J. F., Liu J. Y., et al. Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health. 2005;119:1080–1087. doi: 10.1016/j.puhe.2005.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Wang J. F., Mcmichael A. J., Meng B., et al. Spatial dynamics of an epidemic of severe acute respiratory syndrome in an urban area. Bull World Health Organ. 2006;84:965–968. doi: 10.2471/BLT.06.030247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Wang J. F., Meng B., Zheng X. Y., et al. Analysis on the multi-distribution and the major influencing factors on severe acute respiratory syndrome in Beijing (in Chinese) Chin J Epidemiol. 2005;26:164–168. [PubMed] [Google Scholar]
  • [24].Wu J. L., Wang J. F., Meng B., et al. Spatial association analysis on epidemic of SARS in Beijing. J Zhejiang Univ (Agric Life Sci) 2003;200531:97–101. [Google Scholar]
  • [25].Liu M., Liang W. N., Du H., et al. Epidemiological features of severe acute respiratory syndrome in Beijing urban and suburb areas in. Biomed Environ Sci. 2003;200518:227–232. [PubMed] [Google Scholar]
  • [26].Anselin L., Kim Y. W., Syabri I. Web-based analytical tools for the exploration of spatial data. J Geogr Syst. 2004;6:197–218. doi: 10.1007/s10109-004-0132-5. [DOI] [Google Scholar]
  • [27].Tobler W. A computer movie simulating urban growth in the Detroit region. Econ Geogr. 1970;46:234–240. doi: 10.2307/143141. [DOI] [Google Scholar]
  • [28].Goodchild M F. The application of advanced tecnology in assessing environmental impacts, In: Corwin D L, Loague K. Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. Soil Sci Soc Am J, 1996, 1–17
  • [29].Haining R. P. Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University; 2003. [Google Scholar]
  • [30].Anselin L. Local indicators of spatial association-lisa. Geogr Anal. 1995;27:93–115. [Google Scholar]
  • [31].Ord J. K., Getis G. Local spatial autocorrelation statistics: Distributional issues and application. Geogr Anal. 1995;27:286–306. [Google Scholar]
  • [32].Cressie N. Statistics for Spatial Data. New York: Wiley; 1993. [Google Scholar]
  • [33].Moran P. The interpretation of statistical maps. J R Stat Soc Series B Stat Methodol. 1948;10:243–251. [Google Scholar]

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