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Author: search.filters.author.Hu, Wenbiao

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    Assessing the impact of non-pharmaceutical interventions against COVID-19 on 64 notifiable infectious diseases in Australia: A Bayesian Structural Time Series model
    (2025-03-01)
    Haque, Shovanur
    ;
    Lambert, Stephen B.
    ;
    Mengersen, Kerrie
    ;
    Barr, Ian G.
    ;
    Wang, Liping
    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.
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    Dengue outbreaks in the covid-19 era: Alarm raised for Asia
    (2021-10-01)
    Lu, Xinting
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    Bambrick, Hilary
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    Pongsumpun, Puntani
    ;
    Dhewantara, Pandji Wibawa
    ;
    Toan, Do Thi Thanh
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    Does Bangkok have a central role in the dengue dynamics of Thailand?
    (2020-01-13)
    Xu, Zhiwei
    ;
    Bambrick, Hilary
    ;
    Pongsumpun, Puntani
    ;
    Ming Tang, I.
    ;
    Yakob, Laith
    Background: Bangkok plays a central role in the commerce of Thailand. This study aimed to characterize the district-level spatial-temporal patterns of dengue in Thailand and explore if a dengue peak in Bangkok led the peaks of dengue in other Thai provinces. Methods: Monthly dengue data at district level in Thailand from January 2004 to December 2017 were obtained and used to assess the spatial and seasonal patterns of dengue in Thailand. As our seasonal decomposition and cross-correlation analyses showed that dengue in Bangkok peaked in November, which was a few months after the dengue peak in most other provinces, we used a time-series generalized linear model to explore if there was another province in which the dengue case number was most predictive of dengue case numbers in other Thai provinces. Results: The highest district-level annual dengue incidence rates (per 10,000) in the three time periods (i.e. 2004-2008, 2009-2013 and 2014-2017) were 58.08 (Samphanthawong), 85.93 (Mueang Krabi), and 66.60 (Mae Sariang), respectively. Dengue incidence rates in the western part of Northern Thailand, southern part of Central Thailand, southern part of Eastern Thailand, and Southern Thailand were higher than in other regions. Dengue in most districts of Thailand peaked in June, July or August, but dengue peaks in all districts of Bangkok occurred in November. The number of dengue cases in Nakhon Ratchasima was most predictive of the number of dengue cases in other provinces in Thailand by a one-month lag. Conclusions: Our results suggest that the dengue peak in Bangkok did not lead the peaks of dengue in other Thai provinces. Future research exploring how changes in socio-ecological factors (e.g. road network and climate factors) in Nakhon Ratchasima have affected the transmission of dengue in Thailand might shed some new light on the prevention and control of dengue.