We present a simple, near-real-time Bayesian method to infer and forecast a multiwave outbreak, and demonstrate it on the COVID-19 pandemic. The approach uses timely epidemiological data that has been widely available for COVID-19. It provides short-term forecasts of the outbreak’s evolution, which can then be used for medical resource planning. The method postulates one- and multiwave infection models, which are convolved with the incubation-period distribution to yield competing disease models. The disease models’ parameters are estimated via Markov chain Monte Carlo sampling and information-theoretic criteria are used to select between them for use in forecasting. The method is demonstrated on two- and three-wave COVID-19 outbreaks in California, New Mexico and Florida, as observed during Summer-Winter 2020. We find that the method is robust to noise, provides useful forecasts (along with uncertainty bounds) and that it reliably detected when the initial single-wave COVID-19 outbreaks transformed into successive surges as containment efforts in these states failed by the end of Spring 2020.

我们提出了一种简单的、近乎实时的贝叶斯方法来推断和预测多波爆发，并在 COVID-19 大流行中进行了演示。该方法使用已广泛用于 COVID-19 的及时流行病学数据。它提供了对疫情演变的短期预测，然后可用于医疗资源规划。该方法假设一波和多波感染模型，这些模型与潜伏期分布卷积以产生竞争疾病模型。疾病模型的参数通过马尔可夫链蒙特卡罗抽样进行估计，并使用信息论标准在它们之间进行选择以用于预测。正如在 2020 年夏冬期间观察到的那样，该方法在加利福尼亚、新墨西哥州和佛罗里达州的两波和三波 COVID-19 爆发中得到了证明。