论文标题
学会预测和预测，从19号大流行中学习
Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic
论文作者
论文摘要
Covid-19的准确预测是资源管理和建立处理流行病的策略的核心。我们提出了一个具有人类流动性模型的异质感染率模型，这是我们在2014年DARPA大挑战赛期间成功使用的初步版本。通过线性化并使用加权的最小正方形，我们的模型能够快速适应不断变化的趋势，并在美国和国家国家和国家和州的国家中提供了非常准确的确认案例预测。我们表明，在流行病的早期，使用旅行数据增加了预测。培训模型以预测还可以使流行病的学习特征。特别是，我们表明，随着时间的推移，模型参数的变化可以帮助我们量化州或一个国家对流行病的反应状况。参数的变化也使我们能够预测不同的情况，例如如果我们无视社会疏远的建议将会发生什么。
Accurate forecasts of COVID-19 is central to resource management and building strategies to deal with the epidemic. We propose a heterogeneous infection rate model with human mobility for epidemic modeling, a preliminary version of which we have successfully used during DARPA Grand Challenge 2014. By linearizing the model and using weighted least squares, our model is able to quickly adapt to changing trends and provide extremely accurate predictions of confirmed cases at the level of countries and states of the United States. We show that during the earlier part of the epidemic, using travel data increases the predictions. Training the model to forecast also enables learning characteristics of the epidemic. In particular, we show that changes in model parameters over time can help us quantify how well a state or a country has responded to the epidemic. The variations in parameters also allow us to forecast different scenarios such as what would happen if we were to disregard social distancing suggestions.
