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