Abstract
The changing spatiotemporal patterns of the individual susceptible-infected-symptomatic-treated-recovered epidemic process and the interactions of information/material flows between regions, along with the 2002–2003 Severe Acute Respiratory Syndrome (SARS) epidemiological investigation data in mainland China, including three typical locations of individuals (working unit/home address, onset location and reporting unit), are used to define the in-out flow of the SARS epidemic spread. Moreover, the input/output transmission networks of the SARS epidemic are built according to the definition of in-out flow. The spatiotemporal distribution of the SARS in-out flow, spatial distribution and temporal change of node characteristic parameters, and the structural characteristics of the SARS transmission networks are comprehensively and systematically explored. The results show that (1) Beijing and Guangdong had the highest risk of self-spread and output cases, and prevention/control measures directed toward self-spread cases in Beijing should have focused on the later period of the SARS epidemic; (2) the SARS transmission networks in mainland China had significant clustering characteristics, with two clustering areas of output cases centered in Beijing and Guangdong; (3) Guangdong was the original source of the SARS epidemic, and while the infected cases of most other provinces occurred mainly during the early period, there was no significant spread to the surrounding provinces; in contrast, although the input/output interactions between Beijing and the other provinces countrywide began during the mid-late epidemic period, SARS in Beijing showed a significant capacity for spatial spreading; (4) Guangdong had a significant range of spatial spreading throughout the entire epidemic period, while Beijing and its surrounding provinces formed a separate, significant range of high-risk spreading during the mid-late period; especially in late period, the influence range of Beijing’s neighboring provinces, such as Hebei, was even slightly larger than that of Beijing; and (5) the input network had a low-intensity spread capacity and middle-level influence range, while the output network had an extensive high-intensity spread capacity and influence range that covered almost the entire country, and this spread and influence indicated that significant clustering characteristics increased gradually. This analysis of the epidemic in-out flow and its corresponding transmission network helps reveal the potential spatiotemporal characteristics and evolvement mechanism of the SARS epidemic and provides more effective theoretical support for prevention and control measures.
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References
Jia N, Tsui L. Epidemic modelling using SARS as a case study. North Amer Act J, 2005, 9: 28–42
Dye C, Gay N. Epidemiology modeling the SARS epidemic. Science, 2003, 300: 1884–1885
Shi Y L. Stochastic dynamic model of SARS spreading. Chin Sci Bull, 2003, 48: 1287–1292
Anderson R M, Fraser C, Ghani A C, et al. Epidemiology, transmission dynamics and control of SARS: The 2002–2003 epidemic. Philos Trans R Soc Lond B Biol Sci, 2004, 359: 1091–1105
Lipsitch M, Cohen T, Cooper B, et al. Transmission dynamics and control of severe acute respiratory syndrome. Science, 2003, 300: 1966–1970
Pang X, Zhu Z, Xu F, et al. Evaluation of control measures implemented in the severe acute respiratory syndrome outbreak in Beijing, 2003. JAMA, 2003, 290: 3215–3221
Cao Z D, Zeng D J, Zheng X L, et al. Spatiotemporal evolution of Beijing 2003 SARS epidemic. Sci China Earth Sci, 2010, 53: 1017–1028
Fang L Q, de Vlas S J, Feng D, et al. Geographical spread of SARS in mainland china. Trop Med Int Health, 2009, 14(Suppl 1): 14–20
Wang J F, Christakos G, Han W G, et al. Data-driven exploration of “spatial pattern-time process-driving forces” associations of SARS epidemic in Beijing, China. J Public Health (Oxf), 2008, 30: 234–244
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
Meng B, Wang J F, Liu J, et al. Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health, 2005, 119: 1080–1087
Gong J H, Sun Z L, Li X W, et al. Simulation and analysis of control of Severe Acute Respiratory Syndrome (in Chinese). J Remote Sens, 2003, 7: 260–265
Gong J H, Zhou J P, Xu S, et al. Dynamics model and multi-agent based simulation of SARS transmission (in Chinese). J Remote Sens, 2006, 10: 829–835
Lin G J, Jia X, Ouyang Q. Predict SARS infection with the small world network model (in Chinese). J Peking Univ (Health Sci), 2003, 35(Suppl): 66–69
Small M, Tse C K. Small world and scale free model of transmission of SARS. Int J Bifurcation Chaos, 2005, 15: 1745–1755
Erdős P, Rényi A. On the evolution of random graphs. Pub Math Inst Hungarian Acad Sci, 1960, 5: 17–61
Erdős P, Rényi A. On the strength of connectedness of a random graph. Acta Math Sci Hungary, 1961, 12: 261–267
Watts D J, Strogatz S H. Collective dynamics of /‘small-world/’net-works. Nature, 1998, 393: 440–442
Watts D J. Networks, dynamics, and the small world phenomenon model. Phys Lett A, 1999, 263: 341–346
Barabasi A L, Albert R. Emergence of scaling in random networks. Science, 1999, 286: 509–512
Amaral L A, Scala A, Barthelemy M, et al. Classes of small-world networks. Proc Natl Acad Sci USA, 2000, 97: 11149–11152
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Hu, B., Gong, J., Sun, J. et al. Exploring the epidemic transmission network of SARS in-out flow in mainland China. Chin. Sci. Bull. 58, 1818–1831 (2013). https://doi.org/10.1007/s11434-012-5501-8
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DOI: https://doi.org/10.1007/s11434-012-5501-8