tetano
Editor, Senior Moderator
Epidemiol Infect. 2014 Apr 23:1-13. [Epub ahead of print]
Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis.
Thomas EG1, McCaw JM1, Kelly HA2, Grant KA2, McVernon J1.
Author information
Abstract
SUMMARY 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.
PMID:
24759447
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24759447
Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis.
Thomas EG1, McCaw JM1, Kelly HA2, Grant KA2, McVernon J1.
Author information
Abstract
SUMMARY 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.
PMID:
24759447
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/24759447