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Promising of spatial-temporal model in public health

JIA, Zhong-wei; LI, Xiao-wen; WANG, Wei; CHENG, Shi-ming

Section Editor(s): SUN, Jing

doi: 10.3760/cma.j.issn.0366-6999.2009.03.021
Medical progress
Free
SDC

Edited by

School of Public Health and Family Medicine, Capital Medical University, Beijing 100069, China (Jia ZW and Wang W)

Institute of Remote Sensing Applications Chinese Academy of Sciences, P.O.Box9718, Beijing 100101, China (Li XW)

National Center for Tuberculosis Control and Prevention, Chinese Center for Disease control and Prevention, Beijing 100050, China (Cheng SM)

Correspondence to: Prof. CHENG Shi-ming, National Center for Tuberculosis Control and Prevention, Chinese Center for Disease control and Prevention, Beijing 100500, China (Tel: 86–10–83136107. Fax: 86–10–83136022. Email: smcheng@chinatb.org); Prof. WANG Wei, School of Public Health and Family Medicine, Capital Medical University, Beijing 100069, China (Tel: 86–10–83911504. Fax: 86–10–83911501. Email: wei6014@yahoo.com)

This work was supported by the grants from the “Eleventh Five-year Plan” “AIDS and viral hepatitis prevention and treatment of infectious diseases and other major” special major science and technology—ˆEarly-Warning System for Tuberculosis (No. 2008ZX100(03–008) and Investigation on Methodology for Estimation and Prediction of HIV/AIDS Epidemic (No. 2008ZX10102).

(Received June 23, 2008)

Investigating the transmission of infectious diseases, predicting the epidemic trend of an infectious disease and evaluating the effects of control measures are the continuing subject of public health participators. In recent years the spatial-temporal model has been proposed as a powerful tool to get this information from large incomplete and insufficiently general monitoring data.

One class of models has been successfully used to examine the temporal evolution of a variety of infectious diseases including small pox,1 malaria, 2 tuberculosis,3 measles4 and human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS).5 It originated from a simple deterministic model proposed by Kermack and McKendrick6 in 1927. Such models all assumed that people moved in a single closed group and the number of people in the group was constant. The long-term dynamic behavior of this model was simple and clear: either the disease dies out or a stable equilibrium is reached where the disease becomes endemic. These models are all integral equations in which the spatial factors are not explicit.1–6 Recent years have produced a shock for public health officers with new infectious diseases emerging, severe acute respiratory syndrome (SARS), legionnaire’s disease, panic influenza, HIV/AIDS), old diseases re-emerging (tuberculosis, diphtheria, cholera, plague, epidemic cerebrospinal meningitis) and biological terror attacks, all of which indicate that the spread of an infectious disease is not as simple as the results disclosed by the traditional models. Many infectious diseases tend to occur in spatial clusters,7 such as SARS in 2003, which presented a cluster in some big cities and railway with the disease progressed. In fact, an emergency control measure for SARS, spatial isolation of the high risk communities from other areas, has been conducted based on the ideas of the influence of spatial factors on disease. Obviously, the traditional models can not describe the spatial spread and distribution pattern of disease. However, spatial-temporal models can detect the role of spatial factors as well as temporal factors comparing with the traditional models. It makes containment possible if we can predict the spatial spread of SARS, for example, using spatial-temporal models. With the development of spatial techniques, for example geographic information systems (GIS), remote sensing (RS) and global positioning systems (GPS), the temporal-spatial models are extensively used to help characterize the distribution pattern of large-scale spreading disease, such as schistosomiasis,8 malaria, 9 tuberculosis,10 measles,11 influenza,12 and can predict the diffusing features of and evaluate the effects of new control measures in the public health field.

Epidemics of infectious disease actually results from the co-actions of a series of complicated natural and social factors, which not only possess time attribute but also covers spatial attributes, such as productivity, geographic environments, and climate. These spatial attributes play an important role in the persistence and dynamics of infectious diseases because the asynchrony of disease in populations within different regions allows a disease to persist globally, even if the disease dies out locally. The traditional model for a single population rarely considers the diversity and interaction of spatial factors. It is also difficult, or limited, to investigate the impact of these spatial factors on the disease before the full development of the GIS-based spatial techniques.

The most important motivation for the public health participators to extend the traditional model with an explicitly spatial factor is the desire to examine geographical coordinates, origin, extent, diffusion pattern, speed of spreading and so on of an infectious disease. The potential value of these results can be used for different purposes. For epidemiologists and medical official, they can (1) detect the first occurrence of a disease in a location where it has not been previously recorded,13 (2) evaluate the effect of spatial variability associated with the population on the transmission of an infectious disease (for example, with tuberculosis, migrant populations change its transmission and raise more challenges for public health officers, (3) distinguish one infectious disease from another, and then a measure aimed at the disease will be conducted in a timely manner. Against biological terror attacks, which may currently be the first important driver, it is necessary to discriminate the regular outbreaks of an infectious disease from one initiated by terrorist organizations. Rapidly detecting the particular spatial and temporal extent of the biological terror attack is vital for the antiterrorist specialist to carry out an emergent countermeasure.14 The SARS spreading in 2003 was a practical test for application of the temporal-spatial model to the question (3) above.15

In recent years, some people further applied the temporal-spatial model to investigate the risk factors associated with non-infectious diseases, such as cancer16 and infant death,17 and it has a promising application in public health. Looking toward the near future, we anticipate that health planning will be substantially improved by developments in informatics, that is, through the application of information science and technology to public health practice and research. Ideally, each public health practitioner will have the capability to link together health information from a variety of different data sources and to recognize spatial data patterns that suggest where cost-effective public health interventions can be applied. The spatial-temporal model will play an increasingly important role to support this capability although many challenges remain before the full potential of spatial-temporal models can be realized for public health practices, planning, and research.

In a word, public health participators and medical officials can not deliberately create a real scene of infectious disease or a biological terror attack to generate enough information. The spatial-temporal model supported by GIS-based techniques can provide a feasible solution to many intractable questions.18–20

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REFERENCES

1. Ferguson NM, Keeling MJ, Edmunds WJ, Gani R, Grenfell BT, Anderson RM, et al. Planning for smallpox outbreaks. Nature 2003; 425: 681-685
2. Yang HM. A mathematical model for malaria transmission relating global warming and local socioeconomic conditions. Rev Saude Publica 2001; 35: 224-231.
3. Jia ZW, Tang GY, Jin Z, Dye C, Sake JDV, Li XW, et al. Modeling the impact of immigration on the epidemiology of tuberculosis. Theor Popul Biol. 2008; 73: 437-448.
4. Wallinga J, Heijne JC, Kretzschmar M. A measles epidemic threshold in a highly vaccinated population. PLoS Med 2005; 2: 1152-1157.
5. Culshaw RV. Mathematical modeling of AIDS progression: limitations, expectations, and future directions. J Am Physicians Surg 2006; 11: 101-105.
6. Kermack WO, McKendrik AG. A contribution to the mathematical theory of epidemics. Proc Roy Soc 1927; 115: 700-721.
7. Riley S. Large-scale spatial-transmission models of infectious disease. Science 2007; 316: 1298-1301.
8. Liang S, Seto EY, Remais JV, Zhong B, Yang CH, Hubbard A, et al. Environmental effects on parasitic disease transmission exemplified by schistosomiasis in western China. Proc Natl Acad Sci U S A 2007; 104: 7110-7115.
9. Rodriguez DJ, Torres-Sorando L. Models of infectious diseases in spatially heterogeneous environments. Bull Math Biol 2001; 63: 547-571.
10. Jia ZW, Jia XW, Liu YX, Dye C, Chen F, Chen CS, et al. Migrant population in cities: a spatial analysis of tuberculosis in Beijing. Emerg Infect Dis 2008; 14: 1413-1419.
11. Grenfell BT, Bjornstad ON, Kappey J. Traveling waves and spatial hierarchies in measles epidemics. Nature 2001; 414: 716-723.
12. Hyman JM, Laforce T. Modeling the spread of in fluecnza among cities. In: Banks T, Castillo-Chavez C, eds. Biomathematical modeling applications for homeland security. Philadephia: Society for Industrial and Applied Mathematics; 2003: 215-240.
13. Nash D, Mostashari F, Fine A, Miller J, O'Leary D, Murray K et al. The outbreak of West Nile virus infection in the New York City area in 1999. N Engl J Med 2001; 344: 1807-1814.
14. Kretzschmar M, Hof S, Wallinga J, Wijngaarden J. Ring vaccination and smallpox control. Emerg Infect Dis 2004; 10: 832-841.
15. Gong JH, Sun ZL, Li XW, Cao CX, Li XY, Zhou JP. Simulation and analysis of control of severe acute respiratory syndrome. J Remote Sensing 2003, 7: 260-265.
16. Vieira VM, Webster TF, Weinberg JM, Aschengrau A. A temporal-spatial analysis of breast cancer in upper Cape Cod, Massachusetts. Int J Health Geogr 2008; 7: 46.
17. Oreno C, Ardanaz E, Olivera JE, Castilla J, de Pedro-Cuesta J. A temporal-spatial cluster of sudden infant death syndrome in Navarre, Spain. Eur J Epidemiol 1994; 10: 129-134.
18. Wang JF, McMichael AJ, Meng B, Becker NG, Han WG, Glass K, et al. Spatial dynamics of an epidemic of severe acute respiratory syndrome in an urban area. Bull World Health Organ 2006; 84: 965-968.
19. Watkins RE, Eagleson S, Beckett S, Garner G, Veenendaal B, Wright G, et al. Using GIS to create synthetic disease outbreaks. BMC Med Inform Decis Mak 2007; 7: 4-17.
20. Xu B, Gong P, Seto E, Liang S, Yang CH, Wen S, et al. A spatial-temporal model for assessing the effects of intervillage connectivity in schistosomiasis transmission. Ann Assoc Am Geogr 2006; 96: 31-46.
Keywords:

spatial-temporal model; public health; geographic information systems

© 2009 Chinese Medical Association