We demonstrate a Bayesian method for the “real-time” characterization and forecasting of partially observed COVID-19 epidemic. Characterization is the estimation of infection spread parameters using daily counts of symptomatic patients. The method is designed to help guide medical resource allocation in the early epoch of the outbreak. The estimation problem is posed as one of Bayesian inference and solved using a Markov chain Monte Carlo technique. The data used in this study was sourced before the arrival of the second wave of infection in July 2020. The proposed modeling approach, when applied at the country level, generally provides accurate forecasts at the regional, state and country level. The epidemiological model detected the flattening of the curve in California, after public health measures were instituted. The method also detected different disease dynamics when applied to specific regions of New Mexico.

中文翻译：

---

通过贝叶斯推理表征部分观察到的流行病：应用于 COVID-19

我们展示了一种用于“实时”表征和预测部分观察到的 COVID-19 流行病的贝叶斯方法。表征是使用有症状患者的每日计数来估计感染传播参数。该方法旨在帮助指导疫情早期的医疗资源分配。估计问题被提出为贝叶斯推理之一，并使用马尔可夫链蒙特卡罗技术解决。本研究中使用的数据来源于 2020 年 7 月第二波感染到来之前。拟议的建模方法在国家层面应用时，通常可以在区域、州和国家层面提供准确的预测。在制定公共卫生措施后，流行病学模型检测到加州的曲线变平。