Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic
Learning to Forecast and Forecasting to Learn from the COVID-19 Pandemic
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学习预测并通过预测从 COVID-19 大流行中吸取教训
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
V. Prasanna
中科院分区：
文献类型：
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作者：
Ajitesh Srivastava;V. Prasanna
对新冠肺炎的准确预测对于资源管理和制定应对疫情的战略至关重要。我们提出了一个具有人类流动性的异质感染率模型用于流行病建模，该模型的初步版本已在2014年DARPA大挑战中成功使用。通过将模型线性化并使用加权最小二乘法，我们的模型能够快速适应不断变化的趋势，并提供对美国国家和州层面确诊病例的极其准确的预测。我们表明，在疫情的早期阶段，使用旅行数据会增加预测。训练模型进行预测也使人们能够了解疫情的特征。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一个国家或国家对疫情的反应有多好。参数的变化也让我们能够预测不同的情景，比如如果我们无视社交疏远的建议会发生什么。
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.