ArticleEpidemiology and infection2015
Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis.
Article in Epidemiology and infection, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement.Influenza and other respiratory viruses · 2024Article
- What can urban mobility data reveal about the spatial distribution of infection in a single city?BMC public health · 2019Article
- Model selection for seasonal influenza forecasting.Infectious Disease Modelling · 2017Article
- Retrospective forecasting of the 2010-2014 Melbourne influenza seasons using multiple surveillance systems.Epidemiology and infection · 2017Article
- Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.Influenza and other respiratory viruses · 2016Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Influenza surveillance enables systematic collection of data on spatially and demographically heterogeneous epidemics. Different data collection mechanisms record different aspects of the underlying epidemic with varying bias and noise. We aimed to characterize key differences in weekly incidence data from three influenza surveillance systems in Melbourne, Australia, from 2009 to 2012: laboratory-confirmed influenza notified to the Victorian Department of Health, influenza-like illness (ILI) reported through the Victorian General Practice Sentinel Surveillance scheme, and ILI cases presenting to the Melbourne Medical Deputising Service. Using nonlinear regression, we found that after adjusting for the effects of geographical region and age group, characteristics of the epidemic curve (including season length, timing of peak incidence and constant baseline activity) varied across the systems. We conclude that unmeasured factors endogenous to each surveillance system cause differences in the disease patterns recorded. Future research, particularly data synthesis studies, could benefit from accounting for these differences.
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