Estimating the hospitalization risk for people with comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance. Traditional biostatistical methods for risk estimations require: (i) the number of infected people who were not hospitalized, which may be severely undercounted since many infected people were not tested; (ii) comorbidity information for people not hospitalized, which may not always be readily available. We aim to overcome these limitations by developing a Bayesian approach to estimate the risk ratio of hospitalization for COVID-19 patients with comorbidities.

中文翻译：

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用于估计 SARS-CoV-2 病毒感染合并症患者住院风险比的贝叶斯框架


估计患有 SARS-CoV-2 病毒感染合并症的人的住院风险对于制定公共卫生政策和指导非常重要。传统的风险评估生物统计方法需要：（i）没有住院的感染者人数，由于许多感染者没有接受检测，这一数字可能被严重低估； (ii) 未住院患者的合并症信息，这些信息可能并不总是容易获得。我们的目标是通过开发贝叶斯方法来估计患有合并症的 COVID-19 患者住院的风险比，从而克服这些局限性。