TY - JOUR
T1 - Validating the use of google trends to enhance pertussis surveillance in California
AU - Pollett, Simon
AU - Wood, Nicholas
AU - Boscardin, W. John
AU - Bengtsson, Henrik
AU - Schwarcz, Sandra
AU - Harriman, Kathleen
AU - Winter, Kathleen
AU - Rutherford, George
N1 - Publisher Copyright:
© 2015, Public Library of Science. All Rights Reserved.
PY - 2015/10/19
Y1 - 2015/10/19
N2 - Introduction and Methods: Pertussis has recently re-emerged in the United States. Timely surveillance is vital to estimate the burden of this disease accurately and to guide public health response. However, the surveillance of pertussis is limited by delays in reporting, consolidation and dissemination of data to relevant stakeholders. We fit and assessed a real-time predictive Google model for pertussis in California using weekly incidence data from 2009-2014.Results and Discussion: The linear model was moderately accurate (r = 0.88). Our findings cautiously offer a complementary, real-time signal to enhance pertussis surveillance in California and help to further define the limitations and potential of Google-based epidemic prediction in the rapidly evolving field of digital disease detection.
AB - Introduction and Methods: Pertussis has recently re-emerged in the United States. Timely surveillance is vital to estimate the burden of this disease accurately and to guide public health response. However, the surveillance of pertussis is limited by delays in reporting, consolidation and dissemination of data to relevant stakeholders. We fit and assessed a real-time predictive Google model for pertussis in California using weekly incidence data from 2009-2014.Results and Discussion: The linear model was moderately accurate (r = 0.88). Our findings cautiously offer a complementary, real-time signal to enhance pertussis surveillance in California and help to further define the limitations and potential of Google-based epidemic prediction in the rapidly evolving field of digital disease detection.
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U2 - 10.1371/currents.outbreaks.7119696b3e7523faa4543faac87c56c2
DO - 10.1371/currents.outbreaks.7119696b3e7523faa4543faac87c56c2
M3 - Article
AN - SCOPUS:84958576353
SN - 2157-3999
VL - 7
JO - PLoS Currents
JF - PLoS Currents
IS - OUTBREAKS
ER -
