Tobin Ruarai J, Walker Camelia R, Moss Robert, McCaw James M, Price David J, Shearer Freya M
School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, Australia.
Infectious Disease Dynamics Unit, Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
Commun Med (Lond). 2025 Aug 12;5(1):349. doi: 10.1038/s43856-025-01086-0.
Monitoring the number of COVID-19 patients in hospital beds was a critical component of Australia's real-time surveillance strategy for the disease. From 2021 to 2023, we produced short-term forecasts of bed occupancy to support public health decision-making.
We present a model for forecasting the number of ward and intensive care unit (ICU) beds occupied by COVID-19 cases. The model simulates the stochastic progression of COVID-19 patients through the hospital system and is fit to reported occupancy counts using an approximate Bayesian method. We do not directly model infection dynamics-instead, taking independently produced forecasts of case incidence as an input-enabling the independent development of our model from that of the underlying case forecast(s).
Here, we evaluate the performance of 21-day forecasts of ward and ICU occupancy across Australia's eight states and territories produced across the period March and September 2022. We find forecasts are on average biased downwards immediately prior to epidemic peaks and biased upwards post-peak. Forecast performance is best in jurisdictions with the largest population sizes.
Our forecasts of COVID-19 hospital burden were reported weekly to national decision-making committees to support Australia's public health response. Our modular approach for forecasting clinical burden is found to enable both the independent development of our model from that of the underlying case forecast(s) and the performance benefits of an ensemble case forecast to be leveraged by our occupancy forecasts.
监测新冠病毒疾病住院患者数量是澳大利亚该疾病实时监测策略的关键组成部分。在2021年至2023年期间，我们进行了床位占用情况的短期预测，以支持公共卫生决策。
我们提出了一个预测新冠病例占用病房和重症监护病房（ICU）床位数量的模型。该模型模拟了新冠患者在医院系统中的随机进展情况，并使用近似贝叶斯方法与报告的占用计数相拟合。我们没有直接对感染动态进行建模，而是将独立生成的病例发病率预测作为输入，从而使我们的模型能够独立于基础病例预测进行开发。
在此，我们评估了2022年3月至9月期间澳大利亚八个州和领地对病房和ICU占用情况的21天预测的性能。我们发现，在疫情高峰前，预测平均向下偏差，在高峰后向上偏差。在人口规模最大的司法管辖区，预测性能最佳。
我们对新冠住院负担的预测每周向国家决策委员会报告，以支持澳大利亚的公共卫生应对措施。我们发现，我们预测临床负担的模块化方法既能够使我们的模型独立于基础病例预测进行开发，又能使我们的占用预测利用综合病例预测的性能优势。
