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Inapparent infections and cholera dynamics

dc.contributor.authorKing, Aaron A.en_US
dc.contributor.authorIonides, Edward L.en_US
dc.contributor.authorPascual, Mercedesen_US
dc.contributor.authorBouma, Menno J.en_US
dc.date.accessioned2009-06-01T17:21:41Z
dc.date.available2009-06-01T17:21:41Z
dc.date.issued2008-08-14en_US
dc.identifier.citationKing, Aaron A.; Ionides, Edward L.; Pascual, Mercedes; Bouma, Menno J.. (2008) "Inapparent infections and cholera dynamics." Nature 454(7206): 877-U29. <http://hdl.handle.net/2027.42/62519>en_US
dc.identifier.issn0028-0836en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/62519
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=18704085&dopt=citationen_US
dc.description.abstractIn many infectious diseases, an unknown fraction of infections produce symptoms mild enough to go unrecorded, a fact that can seriously compromise the interpretation of epidemiological records. This is true for cholera, a pandemic bacterial disease, where estimates of the ratio of asymptomatic to symptomatic infections have ranged from 3 to 100 (refs 1-5). In the absence of direct evidence, understanding of fundamental aspects of cholera transmission, immunology and control has been based on assumptions about this ratio and about the immunological consequences of inapparent infections. Here we show that a model incorporating high asymptomatic ratio and rapidly waning immunity, with infection both from human and environmental sources, explains 50 yr of mortality data from 26 districts of Bengal, the pathogen's endemic home. We find that the asymptomatic ratio in cholera is far higher than had been previously supposed and that the immunity derived from mild infections wanes much more rapidly than earlier analyses have indicated. We find, too, that the environmental reservoir(5,6) (free-living pathogen) is directly responsible for relatively few infections but that it may be critical to the disease's endemicity. Our results demonstrate that inapparent infections can hold the key to interpreting the patterns of disease outbreaks. New statistical methods(7), which allow rigorous maximum likelihood inference based on dynamical models incorporating multiple sources and outcomes of infection, seasonality, process noise, hidden variables and measurement error, make it possible to test more precise hypotheses and obtain unexpected results. Our experience suggests that the confrontation of time-series data with mechanistic models is likely to revise our understanding of the ecology of many infectious diseases.en_US
dc.format.extent297242 bytes
dc.format.extent2489 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherNature Publishing Groupen_US
dc.sourceNatureen_US
dc.titleInapparent infections and cholera dynamicsen_US
dc.typeArticleen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumPascual, Mercedes] Univ Michigan, Dept Ecol & Evolutionary Biol, Ann Arbor, MI 48109 USAen_US
dc.contributor.affiliationum[King, Aaron A.] Univ Michigan, Dept Math, Ann Arbor, MI 48109 USAen_US
dc.contributor.affiliationum[Ionides, Edward L.] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USAen_US
dc.contributor.affiliationother[King, Aaron A.en_US
dc.contributor.affiliationother[Pascual, Mercedes] Santa Fe Inst, Santa Fe, NM 87501 USAen_US
dc.contributor.affiliationother[Bouma, Menno J.] Univ London London Sch Hyg & Trop Med, Dept Infect & Trop Dis, London WC1E 7HT, Englanden_US
dc.identifier.pmid18704085en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/62519/1/nature07084.pdf
dc.identifier.doihttp://dx.doi.org/10.1038/nature07084en_US
dc.identifier.sourceNatureen_US
dc.contributor.authoremailkingaa@umich.eduen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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