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Inapparent infections and cholera dynamics


In 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 reservoir5,6 (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 methods7, 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.

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Figure 1: The mechanistic models used.
Figure 2: Profile likelihood of the duration of immunity, 1/ ε , in the SIRS model for the data from Dacca district.
Figure 3: Typical model simulations versus data for the district of Dacca.


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We acknowledge discussions with K. Koelle, C. Bretó and A.P. Dobson. This work was funded by the joint National Science Foundation–National Institutes of Health Ecology of Infectious Diseases Program (NSF grants nos 0545276 and 0430120) and the National Oceanic and Atmospheric Administration (Oceans and Health, grant no. NA040AR460019).

Author Contributions A.A.K. formulated and implemented the models, implemented the fitting algorithm, performed the analyses, and drafted and revised the text. E.L.I. provided input on the models and statistical analyses, and drafted much of the Supplementary Information. M.P. provided input on the models and commented on the text. M.J.B. extracted and assembled the data and provided input on the models and on the historical and clinical aspects of cholera.

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Correspondence to Aaron A. King.

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The file contains Supplementary Equations, Supplementary Methods, Supplementary Discussion, Supplementary Figures S1-S3 with Legends, Supplementary Tables S1-S6 and additional references. (PDF 301 kb)

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King, A., Ionides, E., Pascual, M. et al. Inapparent infections and cholera dynamics. Nature 454, 877–880 (2008).

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