Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic.
Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic.
复制标题
DOI：
10.1016/j.heliyon.2023.e16015
复制
发表时间：
2023-05
期刊：
影响因子：
4
通讯作者：
Mahdi, Adam
中科院分区：
文献类型：
--
作者：
Harvey, John;Chan, Bryan;Srivastava, Tarun;Zarebski, Alexander E.;Dlotko, Pawel;Blaszczyk, Piotr;Parkinson, Rachel H.;White, Lisa J.;Aguas, Ricardo;Mahdi, Adam
对许多人来说，不同国家对COVID-19流行病“波”的讨论是国家对话的一部分，但没有硬性和快速的方法来描述可用数据中的这些波，并且它们与数学流行病学意义上的波的联系只是脆弱的。我们提出了一种算法，该算法处理一般时间序列，以识别时间序列价值的实质性，显著和持续的增长时期，可以合理地描述为“观测波”。这提供了一种在时间序列中描述观测到的波的客观手段。我们使用这种方法来综合不同国家的证据来研究波的类型、驱动因素和调制器。将该算法应用于与COVID-19相关的流行病学时间序列的输出符合视觉直觉和专家意见。检查个别国家的结果表明，连续观察到的波浪在病死率方面可能存在很大差异。此外，在大国，更详细的分析表明，连续观测到的波浪具有不同的地理范围。我们还展示了如何通过政府干预来调节波浪，并发现早期实施npi与减少观察到的波浪数量和减少这些波浪中的死亡率负担相关。通过算法方法可以识别观察到的疾病波，其结果可有效地用于分析流行病的进展。
A discussion of ‘waves’ of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as ‘observed waves’. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves. The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves. It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.
登录
查看更多内容
影响因子：
158.5
作者：
Li, Qun;Guan, Xuhua;Feng, Zijian
通讯作者：
Feng, Zijian
影响因子：
4.4
作者：
Vega T;Lozano JE;Meerhoff T;Snacken R;Mott J;Ortiz de Lejarazu R;Nunes B
通讯作者：
Nunes B
影响因子：
29.9
作者：
Haug, Nils;Geyrhofer, Lukas;Klimek, Peter
通讯作者：
Klimek, Peter
影响因子：
7.4
作者：
Kaxiras, Efthimios;Neofotistos, Georgios
通讯作者：
Neofotistos, Georgios
影响因子：
4
作者：
Chung YM;Hu CS;Lo YL;Wu HT
通讯作者：
Wu HT