评估澳大利亚针对新冠疫情的非药物干预措施对64种法定传染病的影响：贝叶斯结构时间序列模型

Assessing the impact of non-pharmaceutical interventions against COVID-19 on 64 notifiable infectious diseases in Australia: A Bayesian Structural Time Series model.

作者信息

Haque Shovanur, Lambert Stephen B, Mengersen Kerrie, Barr Ian G, Wang Liping, Pongsumpun Puntani, Li Zhongjie, Yang Weizhong, Vardoulakis Sotiris, Bambrick Hilary, Hu Wenbiao

机构信息

Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.

Communicable Disease Branch, Queensland Health, Brisbane, QLD, Australia; National Centre for Immunisation Research and Surveillance, Sydney Children's Hospitals Network, Westmead, NSW, Australia.

出版信息

J Infect Public Health. 2025 Mar;18(3):102679. doi: 10.1016/j.jiph.2025.102679. Epub 2025 Jan 27.

DOI:10.1016/j.jiph.2025.102679

PMID:39879910

Abstract

BACKGROUND

Several studies have examined the effect of non-pharmaceutical interventions (NPIs) on COVID-19 and other infectious diseases in Australia and globally. However, to our knowledge none have sufficiently explored their impact on other infectious diseases with robust time series model. In this study, we aimed to use Bayesian Structural Time Series model (BSTS) to systematically assess the impact of NPIs on 64 National Notifiable Infectious Diseases (NNIDs) by conducting a comprehensive and comparative analysis across eight disease categories within each Australian state and territory, as well as nationally.

METHODS

Monthly data on 64 NNIDs from eight categories were obtained from the Australian National Notifiable Disease Surveillance System. The incidence rates for each infectious disease in 2020 were compared with the 2015-2019 average and then with the expected rates in 2020 using a BSTS model. The study investigated the causal effects of 2020 interventions and analysed the impact of government policy restrictions at the national level from January 2020 to December 2022.

RESULTS

During the COVID-19 pandemic interventions in Australia, there was a 38 % (95 % Credible Interval [CI] [9 %, 54 %]) overall relative reduction in incidence reported across all disease categories compared to the 2015-2019 average. Significant reductions were observed in bloodborne diseases: 20 % (95 % CI [10 %, 29 %]), respiratory diseases: 79 % (95 % CI [52 %, 91 %]), and zoonoses: 8 % (95 % CI [1 %, 17 %]). Conversely, vector-borne diseases increased by 9 % over the same period. Reductions and intervention effects varied by state and territory, with higher policy stringency linked to fewer cases for some diseases.

CONCLUSIONS

COVID-19 NPIs also impacted the transmission of other infectious diseases, with varying effects across regions reflecting diverse outcomes in response strategies throughout Australia. The findings could inform public health strategies and provide scientific evidence to support the development of early warning systems for future disease outbreaks.

摘要

背景

多项研究探讨了非药物干预措施（NPIs）对澳大利亚及全球范围内的新冠疫情和其他传染病的影响。然而，据我们所知，尚无研究运用稳健的时间序列模型充分探究其对其他传染病的影响。在本研究中，我们旨在运用贝叶斯结构时间序列模型（BSTS），通过对澳大利亚每个州和领地内以及全国范围内的八个疾病类别进行全面且具对比性的分析，系统评估非药物干预措施对64种国家法定传染病（NNIDs）的影响。

方法

从澳大利亚国家法定疾病监测系统获取了八个类别的64种国家法定传染病的月度数据。使用BSTS模型将2020年每种传染病的发病率与2015 - 2019年的平均发病率进行比较，然后再与2020年的预期发病率进行比较。该研究调查了2020年干预措施的因果效应，并分析了2020年1月至2022年12月期间国家层面政府政策限制的影响。

结果

在澳大利亚新冠疫情期间的干预措施实施过程中，与2015 - 2019年平均水平相比，所有疾病类别的报告发病率总体相对下降了38%（95%可信区间[CI][9%，54%]）。血源性病减少显著：20%（95%CI[10%，29%]），呼吸道疾病：79%（95%CI[52%，91%]），人畜共患病：8%（95%CI[1%，17%]）。相反，同期虫媒传播疾病增加了9%。不同州和领地的发病率下降情况及干预效果各异，对于某些疾病而言，政策严格程度越高，病例数越少。