School of Mathematical and Physical Sciences, University of Newcastle, Newcastle upon Tyne, NSW, Australia.
International Computer Science Institute, University of California-Berkeley, Berkeley, CA, United States of America.
PLoS One. 2020 Oct 2;15(10):e0240153. doi: 10.1371/journal.pone.0240153. eCollection 2020.
The novel coronavirus COVID-19 arrived on Australian shores around 25 January 2020. This paper presents a novel method of dynamically modeling and forecasting the COVID-19 pandemic in Australia with a high degree of accuracy and in a timely manner using limited data; a valuable resource that can be used to guide government decision-making on societal restrictions on a daily and/or weekly basis. The "partially-observable stochastic process" used in this study predicts not only the future actual values with extremely low error, but also the percentage of unobserved COVID-19 cases in the population. The model can further assist policy makers to assess the effectiveness of several possible alternative scenarios in their decision-making processes.
新型冠状病毒 COVID-19 于 2020 年 1 月 25 日左右抵达澳大利亚。本文提出了一种新颖的方法,使用有限的数据,以高度的准确性和及时性,对澳大利亚的 COVID-19 大流行进行动态建模和预测;这是一种宝贵的资源,可以用来指导政府每天和/或每周对社会限制做出决策。本研究中使用的“部分可观察随机过程”不仅可以非常低的误差预测未来的实际值,还可以预测人群中未观察到的 COVID-19 病例的百分比。该模型还可以帮助决策者在决策过程中评估几种可能的替代方案的效果。