Assessing the impact of non-pharmaceutical interventions against COVID-19 on 64 notifiable infectious diseases in Australia: A Bayesian Structural Time Series model - Haque, Shovanur; Lambert, Stephen B.; Mengersen, Kerrie; Barr, Ian G.; Wang, Liping; Pongsumpun, Puntani; Li, Zhongjie; Yang, Weizhong; Vardoulakis, Sotiris; Bambrick, Hilary; Hu, Wenbiao (My University, 2025)
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.
Keywords: Bayesian structural time series model; Causal impact; COVID-19; Infectious disease; Non-pharmaceutical interventions; Stringency