Universitat de Barcelona. Institut de Recerca en Economia Aplicada. Research Group on Risk in Insurance and Finance. Barcelona, España.
Rev Saude Publica. 2022 Jun 13;56:51. doi: 10.11606/s1518-8787.2022056004315. eCollection 2022.
Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases.
Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed.
Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records.
Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease.
根据已确诊的阳性病例数来估计未来因新冠病毒（Covid-19）住院的人数。
利用西班牙国家流行病学监测网络（Renave）记录的新冠-19 面板数据，我们调整了一个具有乘法结构的回归模型，以解释和预测自 2020 年 5 月 11 日至 2021 年 9 月 20 日期间滞后的确诊阳性病例系列所导致的住院人数。我们还回顾了疫苗接种计划启动以来的时间流逝对住院人数的影响。
阳性病例系列中滞后 9 天的天数对住院人数具有最大的解释力。该模型解释住院人数的变异性很高（调整后的 R2：96.6%）。在疫苗接种计划启动之前，第 t 天的预期住院人数是第 t-9 天阳性病例数的 20.2%，上升到 0.906。疫苗接种计划启动后，这一比例每天减少 0.3%。使用相同的模型，我们发现，在第一波大流行中，阳性病例数是官方记录报告数的六倍多。
从截止到某一天检测到的新冠病例出发，所提出的模型可以提前九天估计住院人数。因此，它是预测卫生系统因该疾病而承受的医院压力的有用工具。
