Inapparent infections and cholera dynamics

无症状的 爆发 疾病 流行病学 霍乱 传输(电信) 病菌 免疫学 生物 医学 人口学 病毒学 内科学 计算机科学 电信 社会学
作者
Aaron A. King,Edward L. Ionides,Mercedes Pascual,Menno J. Bouma
出处
期刊:Nature [Springer Nature]
卷期号:454 (7206): 877-880 被引量:395
标识
DOI:10.1038/nature07084
摘要

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 reservoir (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, 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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
英吉利25发布了新的文献求助10
2秒前
2秒前
Jasper应助头老师采纳,获得10
2秒前
CipherSage应助马哈哈采纳,获得20
3秒前
忧郁的毛巾完成签到,获得积分10
3秒前
思源应助禾唔昂黄采纳,获得10
3秒前
Polar_bear发布了新的文献求助10
4秒前
4秒前
要有锋芒的善良完成签到,获得积分10
4秒前
rr完成签到,获得积分10
4秒前
满天星完成签到,获得积分20
4秒前
李健应助stt1011采纳,获得10
4秒前
黄袜子发布了新的文献求助10
5秒前
5秒前
5秒前
yqc完成签到,获得积分20
5秒前
5秒前
小琴子发布了新的文献求助10
5秒前
大气夜南发布了新的文献求助10
5秒前
爆米花应助hhh采纳,获得10
6秒前
周周发布了新的文献求助10
6秒前
xiao完成签到,获得积分10
7秒前
7秒前
满天星发布了新的文献求助10
7秒前
黎敏发布了新的文献求助10
7秒前
余才锐完成签到,获得积分20
7秒前
Ayanami完成签到,获得积分10
8秒前
南歌子完成签到 ,获得积分10
8秒前
不语完成签到,获得积分10
8秒前
科研通AI6.2应助慢慢采纳,获得10
8秒前
666完成签到,获得积分10
9秒前
CipherSage应助zhang采纳,获得10
9秒前
9秒前
9秒前
量子星尘发布了新的文献求助10
10秒前
YAO发布了新的文献求助10
11秒前
11秒前
知性的骁完成签到,获得积分20
12秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
Social Work and Social Welfare: An Invitation(7th Edition) 410
Medical Management of Pregnancy Complicated by Diabetes 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6056656
求助须知:如何正确求助?哪些是违规求助? 7889514
关于积分的说明 16291597
捐赠科研通 5201985
什么是DOI,文献DOI怎么找? 2783387
邀请新用户注册赠送积分活动 1766115
关于科研通互助平台的介绍 1646904