Epidemiological models usually contain a set of parameters that must be adjusted based on available observations. Once a model has been calibrated, it can be used as a forecasting tool to make predictions and to evaluate contingency plans. It is customary to employ only point estimators of model parameters for such predictions. However, some models may fit the same data reasonably well for a broad range of parameter values, and this flexibility means that predictions stemming from them will vary widely, depending on the particular values employed within the range that gives a good fit. When data are poor or incomplete, model uncertainty widens further. A way to circumvent this problem is to use Bayesian statistics to incorporate observations and use the full range of parameter estimates contained in the posterior distribution to adjust for uncertainties in model predictions. Specifically, given an epidemiological model and a probability distribution for observations, we use the ...

中文翻译：

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使用后验预测分布分析流行病模型：墨西哥城的COVID-19。

流行病学模型通常包含一组参数，必须根据可用的观察结果进行调整。校准模型后，可以将其用作预测工具以进行预测和评估应急计划。习惯上仅采用模型参数的点估计量进行此类预测。但是，某些模型对于较大范围的参数值可能会很好地拟合相同的数据，并且这种灵活性意味着根据它们得出的预测将有很大差异，具体取决于在良好拟合范围内采用的特定值。当数据不佳或不完整时，模型不确定性会进一步扩大。避免此问题的一种方法是使用贝叶斯统计量合并观察值，并使用后验分布中包含的所有参数估计值范围来调整模型预测中的不确定性。具体来说，给定流行病学模型和观察值的概率分布，我们使用...