This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in-sample and out-of-sample.

中文翻译：

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模拟 COVID-19 在纽约市的传播

本文针对纽约市邮政编码级别的确诊 COVID-19 病例数量每周增加提出了一个整体预测器。在贝叶斯模型平均框架内，基线是计数数据的泊松回归。协变量集包括自回归项、空间效应以及人口和社会经济变量。我们对第二波冠状病毒大流行的结果表明，随着大流行的蔓延，这些回归量对于预测新确诊病例的数量更为重要。逐点预测和区间预测都表现出很强的样本内和样本外预测能力。