模拟2017年印度尼西亚雅加达白喉疫情期间的传播动态和控制策略

IF 2.5 3区 医学 Q1 Medicine
Bimandra A. Djaafara , Verry Adrian , Etrina Eriawati , Iqbal R.F. Elyazar , Raph L. Hamers , J. Kevin Baird , Guy E. Thwaites , Hannah E. Clapham
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引用次数: 0

摘要

尽管自20世纪70年代以来进行了广泛的疫苗接种,但白喉已在全球范围内复苏,包括在印度尼西亚。在了解当代传播驱动因素和有效控制疫情方面,特别是在雅加达等人口稠密地区,知识差距仍然存在。我们分析了2017年雅加达疫情数据,并建立了一个纳入人群易感性和无症状携带者估计的分区模型。估计了主要流行病学参数,并模拟了各种控制措施。我们的研究发现,在不同的建模情景下,5岁以下儿童的白喉总体易感性为12.9% (95% CrI: 8.6% - 19.0%)和28.0% (95% CrI: 20.5% - 36.0%),低于“群体免疫阈值”。我们估计无症状携带者非常普遍,对繁殖数量有很大贡献。该模型表明,追踪接触者并治疗疑似病例及其接触者在预防新病例方面比单独接种追赶疫苗更有效。这些发现为未来类似情况下的疫情管理策略提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the transmission dynamics and control strategies during the 2017 diphtheria outbreak in Jakarta, Indonesia
Diphtheria has resurged globally, including in Indonesia, despite widespread vaccination since the 1970s. Knowledge gaps persist in understanding contemporary transmission drivers and effective outbreak control, especially in densely populated areas like Jakarta. We analyzed the 2017 Jakarta outbreak data and developed a compartmental model incorporating estimates of population susceptibility and asymptomatic carriers. Key epidemiological parameters were estimated, and various control measures were simulated. Our study found overall diphtheria susceptibility at 12.9 % (95 % CrI: 8.6 %–19.0 %) and 28.0 % (95 % CrI: 20.5 %–36.0 %) in children under 5 under different modeling scenarios, which were below the 'herd immunity threshold'. We estimated asymptomatic carriers to be highly prevalent, substantially contributing to the reproduction number. The model indicated that contact tracing and treating suspected cases and their contacts were more effective in preventing new cases than catch-up vaccination alone. These findings provide valuable insights for future outbreak management strategies in similar settings.
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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