tetano
Editor, Senior Moderator
Clin Infect Dis. 2016 Sep 26. pii: ciw657. [Epub ahead of print]
[h=1]Evaluating Google Flu Trends in Latin America: important lessons for the next phase of digital disease detection.[/h] Pollett S[SUP]1[/SUP], Boscardin WJ[SUP]2[/SUP], Azziz-Baumgartner E[SUP]3[/SUP], Tinoco YO[SUP]4[/SUP], Soto G[SUP]4[/SUP], Romero C[SUP]4[/SUP], Kok J[SUP]5[/SUP], Biggerstaff M[SUP]3[/SUP], Viboud C[SUP]6[/SUP], Rutherford GW[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] Latin America has a substantial burden of influenza and rising Internet access and could thus benefit from real-time influenza epidemic prediction web-tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most of the Latin American countries nor in any low-middle income country. We aimed to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru and Uruguay.
[h=4]METHODS:[/h] Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011 - December 2014. Concurrent weekly Google-predicted influenza activity in the same countries were abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.
[h=4]RESULTS:[/h] There were frequent GFT prediction errors, with correlation ranging from r=-0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific of influenza virus activity.
[h=4]CONCLUSIONS:[/h] We note substantial inaccuracies with GFT-predicted influenza activity compared to FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future internet-based biosurveillance tools.
? The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.
PMID: 27678084 DOI: 10.1093/cid/ciw657
[PubMed - as supplied by publisher]
[h=1]Evaluating Google Flu Trends in Latin America: important lessons for the next phase of digital disease detection.[/h] Pollett S[SUP]1[/SUP], Boscardin WJ[SUP]2[/SUP], Azziz-Baumgartner E[SUP]3[/SUP], Tinoco YO[SUP]4[/SUP], Soto G[SUP]4[/SUP], Romero C[SUP]4[/SUP], Kok J[SUP]5[/SUP], Biggerstaff M[SUP]3[/SUP], Viboud C[SUP]6[/SUP], Rutherford GW[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] Latin America has a substantial burden of influenza and rising Internet access and could thus benefit from real-time influenza epidemic prediction web-tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most of the Latin American countries nor in any low-middle income country. We aimed to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru and Uruguay.
[h=4]METHODS:[/h] Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011 - December 2014. Concurrent weekly Google-predicted influenza activity in the same countries were abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.
[h=4]RESULTS:[/h] There were frequent GFT prediction errors, with correlation ranging from r=-0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific of influenza virus activity.
[h=4]CONCLUSIONS:[/h] We note substantial inaccuracies with GFT-predicted influenza activity compared to FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future internet-based biosurveillance tools.
? The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.
PMID: 27678084 DOI: 10.1093/cid/ciw657
[PubMed - as supplied by publisher]