论文标题
使用sikj $α$模型对共vid-19死亡的快速准确预测
Fast and Accurate Forecasting of COVID-19 Deaths Using the SIkJ$α$ Model
论文作者
论文摘要
预测Covid-19的效果对于设计可能使我们处理大流行的政策至关重要。已经提出了许多方法，特别是为了预测国家级别和州级别的案件和死亡。这些方法中的许多方法基于传统的流行病学模型，这些模型依靠模拟或贝叶斯推论一次同时学习许多参数。这使他们容易过度合身和缓慢的执行。我们提出了模型sikj $α$的扩展，以预测死亡，并表明它可以考虑流行过程的许多复杂性的效果，但可以简化为使用快速线性回归学到的一些参数。我们还根据疾病预防控制中心目前使用的七种方法对我们的方法进行了评估，这是根据大流行期间的不同时间的两周预测。我们证明，与在评估期的大多数情况下，与这七种方法相比，我们的方法可以实现更好的根平方误差。此外，在2个核心台式机上，我们的方法仅需3.18秒即可调整超参数，学习参数并生成100天的预测报告案件和美国所有州的死亡。 184个国家的总执行时间为11.83，美国所有县（$> $ 3000）的执行时间为101.03s。
Forecasting the effect of COVID-19 is essential to design policies that may prepare us to handle the pandemic. Many methods have already been proposed, particularly, to forecast reported cases and deaths at country-level and state-level. Many of these methods are based on traditional epidemiological model which rely on simulations or Bayesian inference to simultaneously learn many parameters at a time. This makes them prone to over-fitting and slow execution. We propose an extension to our model SIkJ$α$ to forecast deaths and show that it can consider the effect of many complexities of the epidemic process and yet be simplified to a few parameters that are learned using fast linear regressions. We also present an evaluation of our method against seven approaches currently being used by the CDC, based on their two weeks forecast at various times during the pandemic. We demonstrate that our method achieves better root mean squared error compared to these seven approaches during majority of the evaluation period. Further, on a 2 core desktop machine, our approach takes only 3.18s to tune hyper-parameters, learn parameters and generate 100 days of forecasts of reported cases and deaths for all the states in the US. The total execution time for 184 countries is 11.83s and for all the US counties ($>$ 3000) is 101.03s.
