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首页> 外文期刊>Journal of healthcare informatics research. >Forecasting Hospitalizations Due to COVID-19 in South Dakota, USA
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Forecasting Hospitalizations Due to COVID-19 in South Dakota, USA

机译：预测美国南达科他州Covid-19导致的住院

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摘要

Anticipating the number of hospital beds needed for patients with COVID-19 remains a challenge. Early efforts to predict hospital bed needs focused on deriving predictions from SIR models, largely at the level of countries, provinces, or states. In the USA, these models rely on data reported by state health agencies. However, predicting disease and hospitalization dynamics at the state level is complicated by geographic variation in disease parameters. In addition, it is difficult to make forecasts early in a pandemic due to minimal data. Bayesian approaches that allow models to be specified with informed prior information from areas that have already completed a disease curve can serve as prior estimates for areas that are beginning their curve. Here, a Bayesian non-linear regression (Weibull function) was used to forecast cumulative and active COVID-19 hospitalizations for SD, USA, based on data available up to 2020-07-22. As expected, early forecasts were dominated by prior information, which was derived from New York City. Importantly, hospitalization trends differed within South Dakota due to early peaks in an urban area, followed by later peaks in rural areas of the state. Combining these trends led to altered forecasts with relevant policy implications.
机译：预期Covid-19患者所需的医院病床数量仍然是一个挑战。早期的预测医院床需求的努力专注于从SIR模型中得出预测，这主要是在国家，省份或州的水平上。在美国，这些模型依赖于州卫生机构报告的数据。然而，预测州一级疾病和住院动力学因疾病参数的地理差异而变得复杂。此外，由于数据最少，很难在大流行中进行预测。可以通过已经完成疾病曲线的领域的知情信息来指定模型的贝叶斯方法可以作为开始曲线的领域的先前估计。在这里，根据2020-07-22的可用数据，使用了贝叶斯非线性回归（Weibull功能）预测美国SD的累积和主动共证。正如预期的那样，早期的预测是由先前的信息主导的，该信息来自纽约市。重要的是，由于城市地区的早期高峰，南达科他州的住院趋势有所不同，其次是该州农村地区的后期山峰。结合这些趋势导致预测与相关政策的影响发生了变化。

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