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Nat Commun. 2023 Dec 11;14(1):8179. doi: 10.1038/s41467-023-43954-0.
2
Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data.利用基因组数据推断传染病暴发的地理来源的方法和挑战。
Lancet Microbe. 2024 Jan;5(1):e81-e92. doi: 10.1016/S2666-5247(23)00296-3. Epub 2023 Nov 30.
3
Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review.马尔堡病毒病疫情、数学模型和疾病参数:系统评价。
Lancet Infect Dis. 2024 May;24(5):e307-e317. doi: 10.1016/S1473-3099(23)00515-7. Epub 2023 Nov 28.
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A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review.全球范围内对生态位模型进行的新兴传染病预测研究:系统综述。
Front Public Health. 2023 Nov 2;11:1244084. doi: 10.3389/fpubh.2023.1244084. eCollection 2023.
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Comparing the Performance of Three Models Incorporating Weather Data to Forecast Dengue Epidemics in Reunion Island, 2018-2019.比较三种模型在预测 2018-2019 年留尼汪岛登革热疫情中的表现,这些模型都纳入了天气数据。
J Infect Dis. 2024 Jan 12;229(1):10-18. doi: 10.1093/infdis/jiad468.
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Mathematical methods for scaling from within-host to population-scale in infectious disease systems.从传染病系统的个体内尺度到群体尺度的数学方法。
Epidemics. 2023 Dec;45:100724. doi: 10.1016/j.epidem.2023.100724. Epub 2023 Oct 30.
7
A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk.一项系统回顾数据、方法和环境协变量用于绘制伊蚊传播虫媒病毒风险的地图。
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8
Spatial and temporal dynamics of West Nile virus between Africa and Europe.西尼罗病毒在非洲和欧洲之间的时空动态。
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A synergistic future for AI and ecology.人工智能与生态学的协同未来。
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10
Projecting the future incidence and burden of dengue in Southeast Asia.预测东南亚登革热的未来发病率和负担。
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建模人畜共患病和媒介传播病毒。

Modeling zoonotic and vector-borne viruses.

机构信息

Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Division of Infectious Disease Epidemiology, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

出版信息

Curr Opin Virol. 2024 Aug;67:101428. doi: 10.1016/j.coviro.2024.101428. Epub 2024 Jul 22.

DOI:10.1016/j.coviro.2024.101428
PMID:39047313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11292992/
Abstract

The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.

摘要

2013-2016 年埃博拉病毒病疫情和 2019 年冠状病毒病大流行极大地推动了新兴人畜共患病和媒介传播病毒模型的发展。因此,我们回顾了模型的主要目标和方法,以指导科学家和决策者。新兴病毒模型的要素在各个方面都有所不同:从了解过去到预测未来,跨越时空使用数据,以及使用统计方法与机械方法。混合/集成模型和人工智能为建模提供了新的机会。尽管取得了这些进展,但在将模型转化为可操作的决策方面仍存在挑战,特别是在病毒病暴发风险最高的领域。为了解决这个问题,我们必须确定特定病毒模型的差距,加强验证,并让决策者参与模型的开发。