FluTracking Research (Australia)
The FluTracking team publishes annual reports and occasional research reports in peer-reviewed journals. The latest list of articles can be accessed at PubMed. The list of annual reports is also available from the Department of Health.
Defining, controlling and analysing Indigenous data: commitment to historical consistency or commitment to Australian Aboriginal and Torres Strait Islander peoples? Crooks K, Carlson S, Dalton C. Public Health Research & Practice. 2019 Dec;29(4):e2941926.
FluTracking: Weekly online community-based surveillance of influenza-like illness in Australia, 2017 Annual Report. Moberly S, Carlson S, Durrheim D, Dalton C. Commun Dis Intell (2018). 2019 Jul 16; 43. doi: 10.33321/cdi.2019.43.31.
FluTracking weekly online community survey of influenza-like illness annual report, 2016. Carlson SJ, Cassano D, Butler MT, Durrheim DN, Dalton CB. Commun Dis Intell (2018). 2019 April 15; 43. doi: 10.33321/cdi.2019.43.15.
Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts. Moss R, Zarebski AE, Carlson SJ, McCaw JM. Trop Med Infect Dis. 2019 Jan 11;4(1). pii: E12. doi: 10.3390/tropicalmed4010012.
Insights From FluTracking: Thirteen Tips to Growing a Web-Based Participatory Surveillance System. Dalton C, Carlson S, Butler M, Cassano D, Clarke S, Fejsa J, Durrheim D. JMIR Public Health Surveill. 2017 Aug 17;3(3):e48. doi: 10.2196/publichealth.7333.
FluTracking weekly online community survey of influenza-like illness annual report, 2015. Dalton CB, Carlson SJ, Durrheim DN, Butler MT, Cheng AC, Kelly HA. Commun DIs Intell Q Rep. 2016 Dec 24;40(4):E512-E520.
FluTracking weekly online community survey of influenza-like illness: 2013 and 2014. Carlson SJ, Dalton CB, McCallum L, Butler MT, Fejsa J, Elvidge E, Durrheim DN. Commun Dis Intell Q Rep. 2015 Sep 30;39(3):E361-8
Building influenza surveillance pyramids in near real time, Australia. Dalton CB, Carlson SJ, Butler MT, Elvidge E, Durrheim DN. Emerg Infect Dis. 2013 Nov;19(11):1863-5. doi: 10.3201/eid1911.121878. PubMed PMID: 24207165; PubMed Central PMCID: PMC3837640.
FluTracking weekly online community survey of influenza-like illness annual report 2011 and 2012. Carlson SJ, Dalton CB, Butler MT, Fejsa J, Elvidge E, Durrheim DN. Commun Dis Intell Q Rep. 2013 Dec 31;37(4):E398-406. PubMed PMID: 24882237.
FluTracking weekly online community survey of influenza-like illness annual report, 2010. Dalton CB, Carlson SJ, Butler MT, Feisa J, Elvidge E, Durrheim DN. Commun Dis Intell Q Rep. 2011 Dec;35(4):288-93. PubMed PMID: 22624489.
Online FluTracking survey of influenza-like illness during pandemic (H1N1) 2009,Australia. Carlson SJ, Dalton CB, Durrheim DN, Fejsa J. Emerg Infect Dis. 2010 Dec;16(12):1960-2.
FluTracking provides a measure of field influenza vaccine effectiveness, Australia, 2007-2009. Carlson SJ, Durrheim DN, Dalton CB. Vaccine. 2010 Oct 4;28(42):6809-10. Epub 2010 Aug 21.PMID: 20732464
ASPREN surveillance system for influenza-like illness – A comparison with FluTracking and the National Notifiable Diseases Surveillance System. Parrella A, Dalton CB, Pearce R, Litt JC, Stocks N. Aust Fam Physician. 2009 Nov;38(11):932-6.
FluTracking: a weekly Australian community online survey of influenza-like illness in 2006, 2007 and 2008. Dalton C, Durrheim D, Fejsa J, Francis L, Carlson S, d’Espaignet ET, Tuyl F.Commun Dis Intell. 2009 Sep;33(3):316-22.
FluTracking surveillance: comparing 2007 New South Wales results with laboratory confirmed influenza notifications. Carlson SJ, Dalton CB, Tuyl FA, Durrheim DN, Fejsa J, Muscatello DJ, Francis JL, d’Espaignet ET. Commun Dis Intell. 2009 Sep;33(3):323-7
University of Melbourne: Spatio-temporal analysis of influenza transmission in the community: modelling and forecasting
Research proposal and aims
The goal of this research is to enhance the utilisation of existing routine influenza surveillance data, including FluTracking data, by using this data to develop estimation and prediction methods for the early detection and subsequent forecasting of influenza epidemics.
The application of Bayesian estimation for the purposes of forecasting, while standard in the meteorological sciences, is a recent innovation for infectious disease modelling. Few studies have combined mechanistic infection models (e.g., the classic SIR compartment model) with filtering methods in order to produce probabilistic forecasts [1-5]. However, these studies have only been applied to overseas data (predominantly from the USA) and no forecasts are available for any region of Australia.
We aim to establish methods for combining observations from multiple surveillance systems, [6-7] including the FluTracking influenza-like illness surveillance system, with mechanistic models of infection that will allow for systematic early detection of disease outbreaks and probabilistic forecasting of the subsequent outbreak dynamics. We will use local surveillance data from previous influenza seasons to evaluate a range of algorithms and evaluation metrics, in preparation for testing these methods in near-real-time during the 2015 and subsequent influenza seasons in metropolitan Melbourne.
- Ong et al., Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore, PLoS ONE 5(4): e10036,2010.
- Shaman and Karspeck, Forecasting seasonal outbreaks of influenza, PNAS 109(50): 20425-20430, 2012.
- Shaman et al., Real-time influenza forecasts during the 2012-2013 season, Nat Commun 4: 2837,2013.
- Skvortsov and Ristic, Monitoring and prediction of an epidemic outbreak using syndromic observations, Math Biosci 240(1): 12-19, 2012.
- Yang et al., Comparison of filtering methods for the modeling and retrospective forecasting of influenza epidemics, PLoS Comp Biol 10(4): e1003583, 2014.
- Carlson et al., FluTracking surveillance: comparing 2007 New South Wales results with laboratory confirmed influenza notifications, Commun Dis Intell 33(3): 323-327, 2009.
- Thomas et al., Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis, Epidemiol Infect 143(2): 1-13, 2014.