论文标题
关于流行病的实时预测的不确定性：中国和意大利的COVID-19案例研究
On the uncertainty of real-time predictions of epidemic growths: a COVID-19 case study for China and Italy
论文作者
论文摘要
虽然Covid-19在全球范围内迅速传播，但需要提供对流行病的实时预测的需求将动态和统计模型的拟合拟合到其功能之外的可用数据。在这里，我们重点介绍通过将渐近分布拟合到实际数据进行的COVID-19感染的统计预测。通过作为一个案例研究，中国省和意大利地区的共同19感染的流行进化，我们发现预测的特征是流行病生长的早期阶段的不确定性很大。在达到流行峰后，这些不确定性大大减少。在区域级别的预测不确定性的差异可用于突出病毒扩散的延迟。我们的结果警告说，由于基础动力学的本质非线性性质，因此，长期推断流行计数必须非常谨慎，因为它们不仅取决于数据质量，而且还取决于流行病的阶段。这些结果表明，实时流行病学预测应包括广泛的不确定性范围，并敦促汇编高质量的感染数据集，包括无症状患者。
While COVID-19 is rapidly propagating around the globe, the need for providing real-time forecasts of the epidemics pushes fits of dynamical and statistical models to available data beyond their capabilities. Here we focus on statistical predictions of COVID-19 infections performed by fitting asymptotic distributions to actual data. By taking as a case-study the epidemic evolution of total COVID-19 infections in Chinese provinces and Italian regions, we find that predictions are characterized by large uncertainties at the early stages of the epidemic growth. Those uncertainties significantly reduce after the epidemics peak is reached. Differences in the uncertainty of the forecasts at a regional level can be used to highlight the delay in the spread of the virus. Our results warn that long term extrapolation of epidemics counts must be handled with extreme care as they crucially depend not only on the quality of data, but also on the stage of the epidemics, due to the intrinsically non-linear nature of the underlying dynamics. These results suggest that real-time epidemiological projections should include wide uncertainty ranges and urge for the needs of compiling high-quality datasets of infections counts, including asymptomatic patients.
