Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks.
<h4>Background</h4>Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.<h4>Method and findings</h4>Infectious...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0090536&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850190398746525696 |
|---|---|
| author | Philippe Barboza Laetitia Vaillant Yann Le Strat David M Hartley Noele P Nelson Abla Mawudeku Lawrence C Madoff Jens P Linge Nigel Collier John S Brownstein Pascal Astagneau |
| author_facet | Philippe Barboza Laetitia Vaillant Yann Le Strat David M Hartley Noele P Nelson Abla Mawudeku Lawrence C Madoff Jens P Linge Nigel Collier John S Brownstein Pascal Astagneau |
| author_sort | Philippe Barboza |
| collection | DOAJ |
| description | <h4>Background</h4>Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.<h4>Method and findings</h4>Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems' performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p=0001) whereas other indicators were similar (C-DR: p=020; C-Se, p=013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables.<h4>Conclusion</h4>Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p=00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance. |
| format | Article |
| id | doaj-art-2f7403c7b1e442f1b426daf4d2563c5b |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2f7403c7b1e442f1b426daf4d2563c5b2025-08-20T02:15:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0193e9053610.1371/journal.pone.0090536Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks.Philippe BarbozaLaetitia VaillantYann Le StratDavid M HartleyNoele P NelsonAbla MawudekuLawrence C MadoffJens P LingeNigel CollierJohn S BrownsteinPascal Astagneau<h4>Background</h4>Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives.<h4>Method and findings</h4>Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems' performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p=0001) whereas other indicators were similar (C-DR: p=020; C-Se, p=013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables.<h4>Conclusion</h4>Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p=00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0090536&type=printable |
| spellingShingle | Philippe Barboza Laetitia Vaillant Yann Le Strat David M Hartley Noele P Nelson Abla Mawudeku Lawrence C Madoff Jens P Linge Nigel Collier John S Brownstein Pascal Astagneau Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. PLoS ONE |
| title | Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. |
| title_full | Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. |
| title_fullStr | Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. |
| title_full_unstemmed | Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. |
| title_short | Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. |
| title_sort | factors influencing performance of internet based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0090536&type=printable |
| work_keys_str_mv | AT philippebarboza factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT laetitiavaillant factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT yannlestrat factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT davidmhartley factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT noelepnelson factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT ablamawudeku factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT lawrencecmadoff factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT jensplinge factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT nigelcollier factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT johnsbrownstein factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks AT pascalastagneau factorsinfluencingperformanceofinternetbasedbiosurveillancesystemsusedinepidemicintelligenceforearlydetectionofinfectiousdiseasesoutbreaks |