论文标题
预测在Covid-19-19大流行爆发期间的医院需求
Forecasting hospital demand during COVID-19 pandemic outbreaks
论文作者
论文摘要
我们提出了一个隔间SEIRD模型，旨在在当前的Covid-19爆发期间预测大都市地区的医院入住率。该模型具有无症状和有症状的感染，并具有详细的医院动力学。我们在每个潜在和受感染的隔间中对分支概率和非指数停留时间进行建模。使用同时确认的病例和死亡，我们推断了动态系统的接触率和初始条件，考虑到模型锁定干预措施。我们的贝叶斯方法使我们能够及时对医院需求进行概率预测。该模式已被墨西哥联邦政府使用来协助公共政策，并已应用于对70多个大都市地区和该国32个州的分析。
We present a compartmental SEIRD model aimed at forecasting hospital occupancy in metropolitan areas during the current COVID-19 outbreak. The model features asymptomatic and symptomatic infections with detailed hospital dynamics. We model explicitly branching probabilities and non exponential residence times in each latent and infected compartments. Using both hospital admittance confirmed cases and deaths we infer the contact rate and the initial conditions of the dynamical system, considering break points to model lockdown interventions. Our Bayesian approach allows us to produce timely probabilistic forecasts of hospital demand. The model has been used by the federal government of Mexico to assist public policy, and has been applied for the analysis of more than 70 metropolitan areas and the 32 states in the country.
