Tanade Cyrus, Pate Nathanael, Paljug Elianna, Hoffman Ryan A, Wang May D
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.
Proc IEEE Int Symp Bioinformatics Bioeng. 2019 Oct;2019:204-210. doi: 10.1109/bibe.2019.00044. Epub 2019 Dec 26.
The Ebola virus disease (EVD) epidemic that occurred in West Africa between 2014-16 resulted in over 28,000 cases and 11,000 deaths - one of the deadliest to date. A generalized model of the spatiotemporal progression of EVD for Liberia, Guinea, and Sierra Leone in 2014-16 remains elusive. There is also a disconnect in the literature on which interventions are most effective in curbing disease progression. To solve these two key issues, we designed a hybrid agent-based and compartmental model that switches from one paradigm to the other on a stochastic threshold. We modeled disease progression with promising accuracy using WHO datasets.
2014年至2016年间在西非爆发的埃博拉病毒病(EVD)疫情导致超过28000人感染,11000人死亡,是迄今为止最致命的疫情之一。2014年至2016年利比里亚、几内亚和塞拉利昂埃博拉病毒病时空传播的通用模型仍然难以捉摸。关于哪些干预措施在遏制疾病进展方面最有效,文献中也存在脱节。为了解决这两个关键问题,我们设计了一种基于主体和分区的混合模型,该模型在随机阈值上从一种范式切换到另一种范式。我们使用世界卫生组织的数据集对疾病进展进行了具有可观准确性的建模。