|
马上注册登录,享用更多感控资源,助你轻松入门。
您需要 登录 才可以下载或查看,没有账号?注册
|
×
专题:Nature报道
霍乱爆发过程中轻度感染的真实程度难以评估,对无症状感染与有症状感染比例的估计最低可以低到3比1、最高可以高达100比1,因此,对流行病学记录的解读一直是比实际情况低估的,而且低估的程度似乎要比我们所认为的更大:对50年间孟加拉国霍乱死亡模式所做的一项新的模拟研究表明,无症状感染与有症状感染的比例实际上超过1000比1。这项工作有可能极大改变我们对霍乱爆发及怎样应对它们的认识,并且说明,从普遍意义上来讲,传染病的不明显感染的重要性一直是被低估的。(生物谷Bioon.com)
生物谷推荐原始出处:
Nature 454, 877-880 (14 August 2008) | doi:10.1038/nature07084
Inapparent infections and cholera dynamics
Aaron A. King1,2, Edward L. Ionides3, Mercedes Pascual1,4 & Menno J. Bouma5
Department of Ecology and Evolutionary Biology,
Department of Mathematics,
Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, USA
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA
Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, University of London, London WC1E 7HT, UK
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. |
|