Title |
Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009–2014 - Volume 24, Number 10—October 2018 - Emerging Infectious Diseases journal - CDC
|
---|---|
Published in |
Emerging Infectious Diseases, October 2018
|
DOI | 10.3201/eid2410.171940 |
Pubmed ID | |
Authors |
Armin Spreco, Olle Eriksson, Örjan Dahlström, Benjamin John Cowling, Toomas Timpka |
Abstract |
The growing availability of big data in healthcare and public health opens possibilities for infectious disease control in local settings. We prospectively evaluated a method for integrated local detection and prediction (nowcasting) of influenza epidemics over 5 years, using the total population in Östergötland County, Sweden. We used routine health information system data on influenza-diagnosis cases and syndromic telenursing data for July 2009-June 2014 to evaluate epidemic detection, peak-timing prediction, and peak-intensity prediction. Detection performance was satisfactory throughout the period, except for the 2011-12 influenza A(H3N2) season, which followed a season with influenza B and pandemic influenza A(H1N1)pdm09 virus activity. Peak-timing prediction performance was satisfactory for the 4 influenza seasons but not the pandemic. Peak-intensity levels were correctly categorized for the pandemic and 2 of 4 influenza seasons. We recommend using versions of this method modified with regard to local use context for further evaluations using standard methods. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Science communicators (journalists, bloggers, editors) | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 37 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 6 | 16% |
Researcher | 6 | 16% |
Student > Bachelor | 4 | 11% |
Professor | 3 | 8% |
Student > Doctoral Student | 2 | 5% |
Other | 7 | 19% |
Unknown | 9 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 9 | 24% |
Business, Management and Accounting | 3 | 8% |
Nursing and Health Professions | 2 | 5% |
Computer Science | 2 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
Other | 7 | 19% |
Unknown | 13 | 35% |