论文标题
在意大利首次爆发期间，NOTCAST COVID-19发病率指标
Nowcasting COVID-19 incidence indicators during the Italian first outbreak
论文作者
论文摘要
提出了一种新型的参数回归模型，以拟合通常在流行期间收集的发病率数据。该提案是由意大利Covid-19的第一次爆发中的主要流行病学指标进行实时监测和短期预测的动机。提供准确的短期预测，包括提供外源或外部变量的潜在影响；这样可以确保准确预测流行病的重要特征（例如，高峰时间和身高），从而可以随着时间的推移更好地分配健康资源。参数估计是在最大似然框架中进行的。提供了复制方法并复制结果所需的所有计算细节。
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided; this ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameters estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.
