Using silent area analysis to inform a COVID-19 public health response in Hunter New England, regional New South Wales
DOI:
https://doi.org/10.33321/cdi.2023.47.24Keywords:
silent areas, SARS CoV-2, COVID-19, population, testing ratesAbstract
In 2020 and 2021, in the context of nationwide efforts to suppress SARS CoV-2 virus transmission while awaiting a vaccine, public health teams were responsible for finding and isolating all cases and quarantining their contacts. The success of this strategy required very high case ascertainment and thus, by inference, ready access to PCR testing, even in large rural areas such as Hunter New England in New South Wales.
‘Silent area’ analysis entailed the scheduled regular comparison of case and testing rates at local-government-area resolution against larger area and state-wide rates. This analysis provided an easily understood metric for identifying areas with lower testing rates, and for direction of surging of local testing capacity in such areas, by the local health district in partnership with public health services and private laboratory services. Complementary intensive community messaging was also utilised to promote increased testing in identified areas.
Downloads
References
Butler M, Paterson BJ, Martin N, Hobday L, Thorley B, Durrheim DN. ‘Silent’ and ‘noisy’ areas: acute flaccid paralysis surveillance at subnational level, Australia, 2001-2015. Int Health. 2017;9(3):1904. doi: https://doi.org/10.1093/inthealth/ihx007.
Australian Bureau of Statistics (ABS). 1270.0.55.005 – Australian Statistical Geography Standard (ASGC): Volume 5 – Remoteness Structure, July 2016. [Webpage.] Canberra: ABS; 16 March 2018. [Accessed on 24 November 2022.] Available from: https://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/1270.0.55.005Main+Features1July%202016.
Curtis AJ, Ajayakumar J, Curtis J, Brown S. Spatial syndromic surveillance and COVID-19 in the U.S.: local cluster mapping for pandemic preparedness. Int J Environ Res Public Health. 2022;19(15):8931. doi: https://doi.org/10.3390/ijerph19158931.
Hertelendy AJ, Goniewicz K, Khorram-Manesh A. The COVID-19 pandemic: how predictive analysis, artificial intelligence and GIS can be integrated into a clinical command system to improve disaster response and preparedness. Am J Emerg Med. 2021;45:671–2. doi: https://doi.org/10.1016/j.ajem.2020.10.049.
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2023 Communicable Diseases Intelligence
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
