Abstract
Similar to species immigration or exotic species invasion, infectious disease transmission is strengthened due to the globalization of human activities. Using schistosomiasis as an example, we propose a conceptual model simulating the spatio-temporal dynamics of infectious diseases. We base the model on the knowledge of the interrelationship among the source, media, and the hosts of the disease. With the endemics data of schistosomiasis in Xichang, China, we demonstrate that the conceptual model is feasible; we introduce how remote sensing and geographic information systems techniques can be used in support of spatio-temporal modeling; we compare the different effects caused to the entire population when selecting different groups of people for schistosomiasis control. Our work illustrates the importance of such a modeling tool in supporting spatial decisions. Our modeling method can be directly applied to such infectious diseases as the plague, lyme disease, and hemorrhagic fever with renal syndrome. The application of remote sensing and geographic information systems can shed light on the modeling of other infectious disease and invasive species studies.
Keywords: spatio-temporal modeling, spatial connectivity, prevention and control of infectious diseases, biological invasion
References
- 1.Rogers D. J., Randolph S. E. Studying the global distribution of infectious diseases using GIS and RS. Nat Rev: Microbiol. 2003;1:231–237. doi: 10.1038/nrmicro776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Zhou Y., Maszle D., Gong P., et al. GIS based spatial network models of schistosomiasis infection. Geogr Info Sci. 1996;2:51–57. [Google Scholar]
- 3.Spear R., Gong P., Seto E., et al. Remote sensing and GIS for schistosomiasis control in mountainous areas in Sichuan. China Geogr Info Sci. 1998;4:14–22. [Google Scholar]
- 4.Anderson R. M., May R. M., Anderson B. Infectious Diseases of Humans: Dynamics and Control. London: Oxford University Press; 1991. [Google Scholar]
- 5.Liang S., Mazsle D., Spear R. A quantitative framework for a multigroup model of Schistosomiasis japonicum transmission dynamics and control in Sichuan. China Acta Trop. 2002;82:263–277. doi: 10.1016/s0001-706x(02)00018-9. [DOI] [PubMed] [Google Scholar]
- 6.Hubbard A., Liang S., Maszle D., et al. Estimating the distribution of worm burden and egg excretion of Schistosoma japonicum by risk group in Sichuan Province. China Parasitol. 2002;125:221–231. doi: 10.1017/S003118200200207X. [DOI] [PubMed] [Google Scholar]
- 7.Zhao W. X., Gu X. G., Xu F. S., et al. An ecological observation of Oncomelania hupensis robertsoni in Xichang, Daliang, Mountains, Sichuan. Sichuan J Zool (in Chinese) 1995;14(3):119–121. [Google Scholar]
- 8.Xie F. X., Yin G. L., Wu J. Z., et al. Life span and cercaria shedding of schistosome-infected snails in mountainous region of Yunnan. Chin J Parasitol Parasit Diseases (in Chinese) 1990;8(1):4–7. [PubMed] [Google Scholar]
- 9.Stelma F. F., Talia I., Sow S., et al. Efficacy of and side effects of praziquantel in an epidemic focus of Schistosoma mansoni. Am J Trop Med Hyg. 1995;53:167–170. doi: 10.4269/ajtmh.1995.53.167. [DOI] [PubMed] [Google Scholar]
- 10.Liang Y. S., Coles G. C., Doenhoff M. J. Short communication: Detection of praziquantel resistance in schistosomes. Trop Med Int Health. 2000;5(1):72–72. doi: 10.1046/j.1365-3156.2000.00514.x. [DOI] [PubMed] [Google Scholar]
- 11.Pesigan T. P., Hairston N. G., Jauregui J. J., et al. Studies on Schistosoma japonicum infection in the Philippines. 2. The molluscan host. Bull WHO. 1958;18(4):481–578. [PMC free article] [PubMed] [Google Scholar]
- 12.Qian B. Z., Qian J., Xu D. M., et al. The population dy-namics of cercariae of Schistosoma japonicum in Oncomelania hupensis. Southeast Asian J Trop Med Public Health. 1997;28(2):296–302. [PubMed] [Google Scholar]
- 13.Xu B., Gong P., Seto E., et al. A spatial-temporal model for assessing the effects of inter-village connectivity in schistosomiasis transmission. Ann AAG. 2006;96(1):31–46. [Google Scholar]
- 14.Xu B., Gong P., Biging G., et al. Snail density prediction for schistosomiasis control using IKONOS and ASTER images. Photo Eng Remote Sensing. 2004;70(11):1285–1294. [Google Scholar]