Suppr超能文献

建模安哥拉罗安达大规模黄热病疫情爆发及疫苗接种的影响

Modelling the large-scale yellow fever outbreak in Luanda, Angola, and the impact of vaccination.

机构信息

Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.

School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia.

出版信息

PLoS Negl Trop Dis. 2018 Jan 16;12(1):e0006158. doi: 10.1371/journal.pntd.0006158. eCollection 2018 Jan.

Abstract

BACKGROUND

Yellow fever (YF), transmitted via bites of infected mosquitoes, is a life-threatening viral disease endemic to tropical and subtropical regions of Africa and South America. YF has largely been controlled by widespread national vaccination campaigns. Nevertheless, between December 2015 and August 2016, YF resurged in Angola, quickly spread and became the largest YF outbreak for the last 30 years. Recently, YF resurged again in Brazil (December 2016). Thus, there is an urgent need to gain better understanding of the transmission pattern of YF.

MODEL

The present study provides a refined mathematical model, combined with modern likelihood-based statistical inference techniques, to assess and reconstruct important epidemiological processes underlying Angola's YF outbreak. This includes the outbreak's attack rate, the reproduction number ([Formula: see text]), the role of the mosquito vector, the influence of climatic factors, and the unusual but noticeable appearance of two-waves in the YF outbreak. The model explores actual and hypothetical vaccination strategies, and the impacts of possible human reactive behaviors (e.g., response to media precautions).

FINDINGS

While there were 73 deaths reported over the study period, the model indicates that the vaccination campaign saved 5.1-fold more people from death and saved from illness 5.6-fold of the observed 941 cases. Delaying the availability of the vaccines further would have greatly worsened the epidemic in terms of increased cases and deaths. The analysis estimated a mean [Formula: see text] and an attack rate of 0.09-0.15% (proportion of population infected) over the whole period from December 2015 to August 2016. Our estimated lower and upper bounds of [Formula: see text] are in line with previous studies. Unusually, [Formula: see text] oscillated in a manner that was "delayed" with the reported deaths. High recent number of deaths were associated (followed) with periods of relatively low disease transmission and low [Formula: see text], and vice-versa. The time-series of Luanda's YF cases suggest the outbreak occurred in two waves, a feature that would have become far more prominent had there been no mass vaccination. The waves could possibly be due to protective reactive behavioral changes of the population affecting the mosquito population. The second wave could well be an outcome of the March-April rainfall patterns in the 2016 El Niño year by creating ideal conditions for the breeding of the mosquito vectors. The modelling framework is a powerful tool for studying future YF epidemic outbreaks, and provides a basis for future vaccination campaign evaluations.

摘要

背景

黄热病(YF)通过受感染蚊子的叮咬传播,是一种危及生命的病毒性疾病,流行于非洲和南美洲的热带和亚热带地区。黄热病已通过广泛的全国疫苗接种运动得到了很大程度的控制。尽管如此,2015 年 12 月至 2016 年 8 月,安哥拉的黄热病疫情再次爆发,迅速蔓延,并成为过去 30 年来最大的黄热病疫情爆发。最近,巴西的黄热病疫情再次爆发(2016 年 12 月)。因此,迫切需要更好地了解黄热病的传播模式。

模型

本研究提供了一个经过改进的数学模型,结合了现代基于似然的统计推断技术,以评估和重建安哥拉黄热病疫情爆发的重要流行病学过程。这包括疫情的发病率、繁殖数([Formula: see text])、蚊子媒介的作用、气候因素的影响以及黄热病疫情中出现的异常但明显的两波现象。该模型探讨了实际和假设的疫苗接种策略,以及可能的人类反应性行为(例如对媒体预防措施的反应)的影响。

结果

虽然在研究期间报告了 73 例死亡,但模型表明,疫苗接种运动使死亡人数减少了 5.1 倍,使观察到的 941 例病例中的 5.6 倍免于患病。如果进一步推迟疫苗的供应,病例和死亡人数将会大大增加,疫情将会恶化。分析估计,2015 年 12 月至 2016 年 8 月期间,整个时期的平均[Formula: see text]和发病率为 0.09-0.15%(受感染人口的比例)。我们估计的繁殖数[Formula: see text]的下限和上限与之前的研究一致。不寻常的是,[Formula: see text]以一种与报告的死亡人数“延迟”的方式波动。最近高死亡人数与疾病传播率相对较低和繁殖数[Formula: see text]较低的时期相关,反之亦然。罗安达的黄热病病例时间序列表明,疫情发生了两波,这一特征在没有大规模疫苗接种的情况下将更加明显。这两波可能是由于人口的保护性反应性行为变化影响了蚊子种群。第二波可能是 2016 年厄尔尼诺现象 3 月至 4 月的降雨模式造成的,为蚊子媒介的繁殖创造了理想的条件。该模型框架是研究未来黄热病疫情爆发的有力工具,并为未来的疫苗接种运动评估提供了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a1/5798855/d309a5488e64/pntd.0006158.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验