为 COVID-19 制定严格的年龄针对性缓解策略的建模。
Modeling strict age-targeted mitigation strategies for COVID-19.
机构信息
Department of Computation and Systems Biology, University of Pittsburgh, Pittsburgh, PA, United Status of America.
Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA, United Status of America.
出版信息
PLoS One. 2020 Jul 24;15(7):e0236237. doi: 10.1371/journal.pone.0236237. eCollection 2020.
We use a simple SIR-like epidemic model integrating known age-contact patterns for the United States to model the effect of age-targeted mitigation strategies for a COVID-19-like epidemic. We find that, among strategies which end with population immunity, strict age-targeted mitigation strategies have the potential to greatly reduce mortalities and ICU utilization for natural parameter choices.
我们使用一个简单的 SIR 样传染病模型，整合了美国已知的年龄接触模式，来模拟针对 COVID-19 样传染病的针对年龄的缓解策略的效果。我们发现，在以人群免疫为终点的策略中，严格的针对年龄的缓解策略有可能大大降低自然参数选择下的死亡率和 ICU 利用率。
Zotero 插件