PDF(Pubmed)
Abstract：
Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc.). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.
摘要：
时间序列分析是流行病学中的一种有价值的工具，它以两种不同的方式补充了经典的流行病学模型：预测和预测。预测与基于各种内部和外部影响来解释过去和当前的数据有关，这些影响可能具有或不具有因果关系。预测是根据模型的预测能力和外部和/或内部影响的假设未来值，对可能的未来值进行探索。时间序列分析方法的优点是更易于使用（在更直接和线性模型的情况下，例如自回归积分移动平均）。尽管如此，它在预测时间上是有限的，与经典模型不同，如易感暴露感染去除模型。它在预测中的适用性来自于它对短期预测的更好的准确性。在其基本形式上，它对病原体的传播和突变机制或人和监管结构的反应(政府，公司,等。).相反，它直接从数据中估计。它的预测能力允许测试不同因素的假设，这些因素对大流行的传播有积极或消极影响；无论是学校关闭，新兴变体，等。它可用于新病例的死亡率或医院风险估计，血清阳性率研究，评估新兴变体的属性，估计超额死亡率及其与大流行的关系。
