Forecasting hospital-level COVID-19 admissions using real-time mobility data.
Forecasting hospital-level COVID-19 admissions using real-time mobility data.
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DOI：
10.1038/s43856-023-00253-5
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发表时间：
2023-02-14
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影响因子：
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通讯作者：
Salmasian, Hojjat
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作者：
Klein, Brennan;Zenteno, Ana C;Joseph, Daisha;Zahedi, Mohammadmehdi;Hu, Michael;Copenhaver, Martin S;Kraemer, Moritz U G;Chinazzi, Matteo;Klompas, Michael;Vespignani, Alessandro;Scarpino, Samuel V;Salmasian, Hojjat
对于每一波新冠肺炎疫情，医院都不得不计划部署激增的容量和资源，以应对新冠肺炎入院人数的短暂大幅增加。虽然在预测新冠肺炎病例和住院人数的地区趋势方面付出了大量努力，但成功创建准确的医院水平预测的工具要少得多。大规模匿名移动电话数据已被证明与大流行前两波(2020年春季和2021年秋冬季)期间的区域病例计数相关。在这一成功的基础上，我们开发了一个多步骤的递归预测模型来预测单个医院的入院人数；该模型结合了以下数据：(I)医院级别的新冠肺炎入院人数，(Ii)全州范围的测试阳性数据，以及(Iii)大规模人员流动性、接触模式和通勤量的综合衡量标准。将大规模、汇总的流动性数据作为外生变量纳入预测模型，使我们能够提前21天做出医院特定的新冠肺炎入院预测。我们通过对马萨诸塞州五家医院在新冠肺炎大流行第一年期间的住院人数进行高度准确的预测来证明这一点。该模型的高预测能力是通过将有关用户联系模式、通勤量和移动范围的匿名聚合移动设备数据与COVID住院和检测阳性数据相结合实现的。移动性信息预测模型可以增加个别医院准确预测的提前期，为经理提供宝贵的时间来制定战略，如何最好地分配资源来管理即将到来的激增。在新冠肺炎大流行期间，医院需要根据未来几周的入院人数估计，在人员配备和准备工作方面做出具有挑战性的决定。使用机器学习方法的预测技术已经成功地应用于预测全州的医院入院人数，但准确预测个人医院入院人数的能力被证明是难以捉摸的。在这里，我们将从手机数据中获得的人员流动的细节纳入到一个模型中，该模型对21天后将住院的人数做出准确预测。这一模式将有助于管理人员和医疗工作者计划病人的人员配备和出院，以确保有足够的能力处理即将到来的入院治疗。Klein等人。使用移动数据预测马萨诸塞州五家医院的新冠肺炎入院人数。将有关用户联系模式、通勤量和移动范围的移动设备聚合数据与COVID住院和检测阳性数据相结合，可增加个别医院准确预测的提前期。
For each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts. Large-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume. Incorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic. The high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges. During the COVID-19 pandemic, hospitals have needed to make challenging decisions around staffing and preparedness based on estimates of the number of admissions multiple weeks ahead. Forecasting techniques using methods from machine learning have been successfully applied to predict hospital admissions statewide, but the ability to accurately predict individual hospital admissions has proved elusive. Here, we incorporate details of the movement of people obtained from mobile phone data into a model that makes accurate predictions of the number of people who will be hospitalized 21 days ahead. This model will be useful for administrators and healthcare workers to plan staffing and discharge of patients to ensure adequate capacity to deal with forthcoming hospital admissions. Klein et al. use mobility data to forecast COVID-19 admissions for five Massachusetts hospitals. Combining aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data increases the lead-time of accurate predictions for individual hospitals.