2015
DOI: 10.5210/ojphi.v7i1.5719
|View full text |Cite
|
Sign up to set email alerts
|

An Early Warning Influenza Model using Alberta Real- Time Syndromic Data (ARTSSN)

Abstract: We developed early warning algorithms for influenza using data from the Alberta Real-Time Syndromic Surveillance Net (ARTSSN). In addition to looking for signatures of potential pandemics, the model was operationalized by using the algorithms to provide regular weekly forecasts on the influenza trends in Alberta during 2012-2014. We describe the development of the early warning model and the predicted influenza peak time and attack rate results. We report on the usefulness of this model using real-time ARTSSN … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2018
2018
2023
2023

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 1 publication
0
1
0
Order By: Relevance
“…While we are not discounting the use of Australian influenza surveillance data, the data should be treated the same way as surveillance data obtained from any other country and used together as global intelligence to inform influenza trends and activity that could occur in Canada. Timely and robust national and sub-national surveillance data is a great asset in aiding the development of within-season predictions that can provide lead time and inform within-season resource and capacity planning, as well as mitigation measures (( 36 )).…”
Section: Discussionmentioning
confidence: 99%
“…While we are not discounting the use of Australian influenza surveillance data, the data should be treated the same way as surveillance data obtained from any other country and used together as global intelligence to inform influenza trends and activity that could occur in Canada. Timely and robust national and sub-national surveillance data is a great asset in aiding the development of within-season predictions that can provide lead time and inform within-season resource and capacity planning, as well as mitigation measures (( 36 )).…”
Section: Discussionmentioning
confidence: 99%