Global influenza surveillancewith Laplacianmultidimensional scaling

Abstract

The Global Influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the resource-limited countries, making these surveillance gaps a more imminent challenge. We present a spatial-transmission model to estimate epidemic risks in the countries where only partial or even no surveillance data are available. Motivated by the observation that countries in the same influenza transmission zone divided by the World Health Organization had similar transmission patterns, we propose to estimate the influenza epidemic risk of an unmonitored country by incorporating the surveillance data reported by countries of the same transmission zone. Experiments show that the risk estimates are highly correlated with the actual influenza morbidity trends for African and Asian countries. The proposed method may provide the much-needed capability to detect, assess, and notify potential influenza epidemics to the developing world.

Keywords

Surveillance gap / Influenza / Spatial-transmission model

References

[1] Best, N., Richardson, S., Thomson, A., 2005. A comparison of Bayesian spatial models for disease mapping. Stat. Methods Med. Res., 14(1):35–59. http://dx.doi.org/10.1191/0962280205sm388oa

[2] Briand, S., Mounts, A., Chamberland, M., 2014. Challenges of Global Surveillance during an Influenza Pandemic. World Health Organization, Geneva. Available from http://www.who.int/influenza/surveillance_monitoring/ Challenges_global_surveillance.pdf [<Date>Accessed on June 10, 2014</Date>].

[3] Cooper, B.S., Pitman, R.J.,Edmunds, W.J., , 2006. Delaying the international spread of pandemic influenza. PLoS Med., 3(6):e212. http://dx.doi.org/10.1371/journal.pmed.0030212

[4] ECDC, 2009. Pandemic (H1N1) 2009. European Centers for Disease Control, Stockholm. Available fromhttp://ec.europa.eu/health/communicable_diseases/diseases/influenza/h1n1/index_en.htm [<Date>Accessed on June 10, 2014</Date>].

[5] Eubank, S., Guclu, H., Kumar, V., , 2004. Modelling disease outbreaks in realistic urban social networks. Nature, 429:180–184. http://dx.doi.org/10.1038/nature02541

[6] Ferguson, N., Donnelly, C., Anderson, R., 2001. The footand-mouth epidemic in Great Britain: pattern of spread and impact of interventions. Science, 292(5519):1155–1160. http://dx.doi.org/10.1126/science.1061020

[7] Ferguson, N., Cummings, D., Cauchemez, S., , 2005. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature, 437:209–214. http://dx.doi.org/10.1038/nature04017

[8] Hay, S.I., Battle, K.E., Pigott, D.M., , 2013. Global mapping of infectious disease. Phil. Trans. R. Soc. B, 368(1614):20120250. http://dx.doi.org/10.1098/rstb.2012.0250

[9] He, D., Dushoff, J., Eftimie, R., , 2013. Patterns of spread of influenza A in Canada. Proc. R. Soc. B, 280(1770):20131174. http://dx.doi.org/10.1098/rspb.2013.1174

[10] He, D., Chiu, A., Lin, Q., , 2015a. Differences in the seasonality of Middle East respiratory syndrome coronavirus and influenza in the Middle East. Int. J. Infect. Dis., 40:15–16. http://dx.doi.org/10.1016/j.ijid.2015.09.012

[11] He, D., Lui, R., Wang, L., , 2015b. Global spatiotemporal patterns of influenza in the post-pandemic era. Sci. Reports, 5:11013. http://dx.doi.org/10.1038/srep11013

[12] Hollingsworth, T., Ferguson, N., Anderson, R., 2007. Frequent travelers and rate of spread of epidemics. Emerg. Infect. Dis., 13(9):1288–1294.

[13] Keeling, M., Woolhouse, M., Shaw, D., , 2001. Dynamics of the 2001 UK foot and mouth epidemic: stochastic dispersal in a heterogeneous landscape. Science, 294(5543):813–817. http://dx.doi.org/10.1126/science.1065973

[14] Kenah, E., Chao, D., Matrajt, L., , 2011. The global transmission and control of influenza. PLoS ONE, 6(5):e19515. http://dx.doi.org/10.1371/journal.pone.0019515

[15] Lavanchy, D., 1999. The importance of global surveillance of influenza. Vaccine, 17:S24–S25.

[16] Longini, I., Nizam, A., Xu, S., , 2005. Containing pandemic influenza at the source. Science, 309(5737):1083–1087. http://dx.doi.org/10.1126/science.1115717

[17] Nelson, M.I., Viboud, C., Vincent, A.L., , 2015. Global migration of influenza A viruses in swine. Nat. Commun., 6:6696. http://dx.doi.org/10.1038/ncomms7696

[18] Oshitani, H., Kamigaki, T., Suzuki, A., 2008. Major issues and challenges of influenza pandemic preparedness in developing countries. Emerg. Infect. Dis., 14(6):875–880. http://dx.doi.org/10.3201/eid1406.070839

[19] Riley, S., 2007. Large-scale spatial-transmission models of infectious disease. Science, 316(5829):1298–1301. http://dx.doi.org/10.1126/science.1134695

[20] Tamerius, J., Shaman, J., Alonso, W.J., , 2013. Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Path., 9(3):e1003194. http://dx.doi.org/10.1371/journal.ppat.1003194

[21] Wang, L., Li, X., 2014. Spatial epidemiology of networked metapopulation: an overview. Chin. Sci. Bull., 59(28): 3511–3522. http://dx.doi.org/10.1007/s11434-014-0499-8

[22] WHO, 2014. Introduction of the Influenza Transmission Zones. World Health Organization, Geneva. Available from http://www.who.int/csr/disease/swineflu/transmission_zones/en/ [<Date>Accessed on June 10, 2014</Date>].

[23] WHO Regional Office for Africa, 2009. Pandemic (H1N1) 2009 in the African Region: Update 63. World Health Organization, Brazzaville. Available from http://www.afro.who.int/index.php?option=com_docman&task=doc_download&gid=3954 [<Date>Accessed on June 10, 2014</Date>].

[24] Williams, C., 2002. On a connection between kernel PCA and metric multidimensional scaling. . Mach Learn., 46(1): 11–19. http://dx.doi.org/10.1023/A:1012485807823

[25] Zhou, X., Shen, H., 2010. Notifiable infectious disease surveillance with data collected by search engine. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 11(4):241–248. http://dx.doi.org/10.1631/jzus.C0910371

[26] Zhou, X., Ye, J., Feng, Y., 2011. Tuberculosis surveillance by analyzing Google trends. IEEE Trans. Biomed. Eng., 58(8):2247–2254. http://dx.doi.org/10.1109/TBME.2011.2132132

[27] Zhou, X., Li, Q., Zhu, Z., , 2013. Monitoring epidemic alert levels by analyzing Internet search volume. IEEE Trans. Biomed. Eng., 60(2):446–452. http://dx.doi.org/10.1109/TBME.2012.2228264

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