主题：【原创】选择抗疫模式的一个可能决策依据（知己知彼，百战不殆） -- 学步桥

To estimate expected mortality, we developed six models, each fit separately by location. The first four models were based on first estimating the weekly (or monthly) seasonal pattern of mortality and then estimating the time trend in weekly or monthly mortality not explained by seasonality. We used a Bayesian spline to estimate the weekly seasonal pattern for each location using data from 2010, or the earliest year after 2010 when such data first became available, until around February, 2020, when the COVID-19 pandemic started for each location (appendix p 48). Second, using the same Bayesian spline, we estimated the time trend in the residuals (additional details provided in the appendix, pp 38–40). By combining the seasonal and secular trends, we generated predictions of the expected level of mortality in 2020 and 2021.

The specification of the spline can have a sizeable impact on the estimated expected mortality for a particular location. To make the results more robust to model specification, we included in our ensemble four variants according to where the second to last knot in the spline was placed: 6 months, 12 months, 18 months, and 24 months before the end of the period for the input data before the COVID-19 pandemic started for each location. We also included in the ensemble a Poisson model with fixed effects on week and year, and a model that assumed that expected mortality for 2020 and 2021 was the same as the corresponding weekly mortality observed in 2019. To derive weights for the different models in the ensemble, we assessed how each model performed in an out-of-sample predictive validity test. We fit the model to all data prior to March 1, 2019 and then evaluated how each model performed in predicting mortality between March, 2019, and February, 2020, compared with observed mortality in the same time period. We then weighted component models in the ensemble using 1 over the root mean squared error (RMSE) of the predictions for each component to down-weight component models with larger RMSE (and thus less accurate predictions) in the ensemble. A global weighting scheme was used for all locations. The distribution of RMSE by location for each of the six models included in the model ensemble and examples of the estimated excess mortality for each component model are provided in the appendix (p 49). Expected mortality from the ensemble model was subtracted from observed mortality in 2020 and 2021 to estimate excess mortality due to the COVID-19 pandemic.

（十一）加强医疗资源建设。制定分级分类诊疗方案、不同临床严重程度感染者入院标准、各类医疗机构发生疫情和医务人员感染处置方案，做好医务人员全员培训。做好住院床位和重症床位准备，增加救治资源。

因为奥密克戎的最长潜伏期是8天，根据专家的评估，密切接触者3天内检出阳性比例是81.4%，4天达到90.1%，5天达到94.5%，7天达到99.7%。根据这些结果，将隔离期限进一步优化到“5+3”。

血氧低于93要求直接叫急救。

【血氧92的危险】 - 大众养生网

龚新宇 副主任医师 中日友好医院 三甲 回答时间: 2022年05月14日

正常情况下,血氧92并不存在任何危险。一般情况下,人的血氧饱和一般在90-100之间,如果检查时,血氧饱和度低于80%,病人就会出现明显的缺氧症状,如胸闷、气喘病患有呼吸困难。血...

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