Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.

Pub Date : 2022-09-01 Epub Date: 2022-07-27 DOI:10.1002/cjs.11723
Dirk Douwes-Schultz, Shuo Sun, Alexandra M Schmidt, Erica E M Moodie
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Abstract

Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component. We extend EE models to include a distributed-lag model in order to investigate the association between mobility and the number of reported COVID-19 cases; we additionally include a weekly first-order random walk to capture additional temporal variation. Further, we introduce a shifted negative binomial weighting scheme for the past counts that is more flexible than previously proposed weighting schemes. We perform inference under a Bayesian framework to incorporate parameter uncertainty into model forecasts. We illustrate our methods using data from four US counties.

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将流动性数据纳入 COVID-19 预测的扩展贝叶斯流行病学模型。
预测每天 COVID-19 病例的数量对于医院和其他公共资源的短期规划至关重要。用于预测 COVID-19 病例的一个潜在重要信息是移动设备定位数据,该数据可测量个人在家逗留的时间。地方病-流行病(EE)时间序列模型是最近提出的自回归模型,其中当前平均病例数被模拟为过去病例数乘以自回归率的加权平均值,再加上地方病部分。我们对 EE 模型进行了扩展,加入了分布式滞后模型,以研究流动性与报告的 COVID-19 病例数之间的关系;我们还加入了每周一阶随机游走,以捕捉更多的时间变化。此外,我们还为过去的计数引入了一个移位负二叉加权方案,该方案比之前提出的加权方案更加灵活。我们在贝叶斯框架下进行推理,将参数的不确定性纳入模型预测。我们使用美国四个县的数据来说明我们的方法。
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