Meakin Sophie, Abbott Sam, Bosse Nikos, Munday James, Gruson Hugo, Hellewell Joel, Sherratt Katherine, Funk Sebastian
Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
medRxiv. 2022 Jan 19:2021.10.18.21265046. doi: 10.1101/2021.10.18.21265046.
Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.
We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.
All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.
Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.
在疫情环境下，预测医疗保健需求至关重要，这既能为态势感知提供信息，又有助于资源规划。理想情况下，预测应在不同时间和地点都具有稳健性。在英国的新冠疫情期间，人们一直担心英国新冠患者的医院护理需求会超过可用资源。
我们使用三种与疾病无关的预测模型，对2020年8月至2021年4月期间英格兰国民保健服务（NHS）信托机构的每日新冠医院入院人数进行了每周预测：自回归时间序列模型的均值集成、以7天滞后的本地病例为预测变量的线性回归模型，以及本地病例与延迟分布的缩放卷积。我们将它们的点预测和概率准确性与所有模型的均值集成进行比较，并与入院最后一天无变化的简单基线模型进行比较。我们使用加权区间得分（WIS）来衡量预测性能，并考虑其在不同情景（预测期长度、预测日期和地点）下的变化情况，以及当已知未来病例时入院预测的改善程度。
在大多数情景下，所有模型的表现均优于基线。预测准确性因预测日期和地点而异，这取决于疫情的发展轨迹，并且所有单个模型都有排名最高或最低的情况。在所有考虑的模型中，均值集成产生的预测既是最准确的，也是最一致准确的预测。使用未来观察到的病例而非预测病例时，预测准确性会提高，尤其是在较长的预测期内。
假设当前入院人数不变很少会比至少纳入一个趋势更好。在某些情景下，使用确诊的新冠病例作为预测变量可以改善入院预测，但这是可变的，并且取决于做出持续良好病例预测的能力。然而，集成预测可以做出在不同时间和地点都更一致准确的预测。鉴于对数据和计算的要求最低，我们的入院预测集成可用于预测未来疫情或大流行环境下的医疗保健需求。
