we focused on the geographic range of denvs in latin america and the caribbean , where dengue is widespread and expanding in range .
we attempted to identify areas contiguous with previously known dengue - endemic zones where new denv transmission is occurring by using reports of recent outbreaks .
known dengue - endemic areas were defined as dengue risk areas identified by the us centers for disease control and prevention ( atlanta , ga , usa ) health information for international travel ( commonly referred to as the yellow book ) , 2010 ( 7 ) and 2012 ( 8) editions .
each edition of this book reflects the known distribution of dengue risk in the prior 2 years . to characterize spread according to the yellow book , we identified areas that were classified as no known dengue risk in 2010 but were changed to risk areas in the 2012 edition ( hereafter referred to as new dengue - endemic areas ) .
outbreak data for december 1 , 2009march 18 , 2011 , were collected from healthmap ( http://www.healthmap.org/en/an ) , an open access online infectious disease outbreak monitoring system ( 9,10 ) .
healthmap integrates outbreak - related data from > 30,000 electronic sources , including the news media , promed - mail , and other electronic public health reporting sources , by using algorithms to classify the diseases and locations associated with each report . because we wanted to identify spread into new dengue - endemic zones , we limited our analyses to areas that were identified as having no known dengue risk in the 2010 yellow book but that were contiguous with > 1 risk areas in the 2010 yellow book .
we fitted a bivariate gaussian mixture model to the extracted healthmap alerts to model a continuous surface of outbreak density ( technical appendix ) .
this modeled outbreak probability density surface represents a risk map of recent denv spread into areas of previously unknown dengue endemicity according to the 2010 yellow book ( figure 1 ) .
we compared our map with the geographic distribution of new dengue - endemic areas identified in the 2012 yellow book .
details of the datasets , models , and statistical methods are available in the technical appendix .
b ) central mexico ; c ) central south america ; d ) eastern amazonas , brazil .
. areas to which dengue was identified in the 2010 yellow book are shaded in gray .
new dengue - endemic areas identified in the 2012 yellow book are outlined in blue .
modeled healthmap alert probability density surface is shown in a gradient from yellow to red with yellow areas predicted as having lower alert densities and red areas predicted as having higher alert densities according to the model .
areas outlined with heavy black solid lines were classified as high healthmap alert density but were not identified in either yellow book edition as dengue risk areas .
areas outlined with heavy black dashed lines were classified as low healthmap alert density but were identified in the 2012 yellow book as areas at risk for dengue .
figure 1 shows that high dengue outbreak activity occurred adjacent to previously recognized dengue - endemic zones in 6 states in central mexico and in parts of northern argentina , southern brazil , bolivia , and paraguay .
we used receiver - operating characteristic analysis with cross - validation ( figure 2 ) to set a threshold dengue report density that best identifies new dengue - endemic areas ( figure 1 ; technical appendix ) .
of the 19 new dengue - endemic areas reported in the 2012 yellow book , this threshold identified 14 ( 74% ) as being at elevated risk of endemicity , according to the dengue outbreak probability density estimated by our model .
of the 41 areas that remained unidentified as dengue - endemic areas in the 2012 yellow book , our model classified 35 ( 85% ) as having reduced risk of endemicity .
receiver - operating characteristic plot of 5-fold cross - validated healthmap alert density - based classification with new dengue - endemic areas identified by the 2012 yellow book as the standard .
when compared with the yellow book , our model incorrectly classified 6 areas as at elevated risk ( figure 1 ) .
all alerts in these areas described outbreaks of cases acquired in a nearby known dengue - endemic region of the country .
one alert also warned of the recent discovery of dengue vector mosquito larvae by the local surveillance program .
the model also classified 5 yellow book dengue - endemic areas as at reduced risk ( figure 1 ) .
although other explanations likely exist , the low observed sensitivity in these areas illustrates certain limits of any system that relies on internet - based information flow for monitoring disease spread .
electronic event  based surveillance systems such as healthmap and others are frequently used by public health authorities , travelers , physicians and patients , to gain a real - time understanding of global outbreak activity .
the healthmap dengue feed , denguemap , is currently part of the online dengue information resource of the centers for disease control and prevention ( http://www.cdc.gov/dengue/ ) . used in combination with traditional case reporting , healthmap and other electronic surveillance systems have proven value for enhancing the timeliness of outbreak discovery and information dissemination ( 11 ) .
however , these information sources may also provide added value for monitoring ongoing spread . although the signal of denv activity detected by healthmap is relatively robust , it has certain limitations .
first , the signal tends to be sparse in areas with limited reporting because of low population density or incomplete coverage by the news or social media .
second , the signal can be surrounded by background noise because separating reports caused by cases in travelers from true autochthonous transmission is difficult with automated methods . by limiting our analysis to areas contiguous with known dengue - endemic areas and smoothing outbreak alerts into an outbreak - density surface
although this analysis was performed retrospectively , the timeliness of this signal far outperforms any traditional surveillance data stream . passive case report - based surveillance systems typically operate at a delay of weeks to months , which limits their value for providing a picture of geographic spread , especially on an international scale where surveillance delays may be even more prolonged .
we have demonstrated a novel approach to real - time monitoring of recent expansion of denv activity in latin america .
using outbreak reports captured by healthmap , we identified a signal of geographic expansion of dengue activity that would precede official reports of the geographic distribution of dengue - endemic areas .
currently , no reliable surveillance system is in widespread use that reports the distribution of denv activity on an ongoing basis and enables near real - time monitoring of trends in geographic expansion .
such a system should enhance the ability of regional and global public health authorities to dynamically allocate resources within a time frame that might effectively avert a full - blown epidemic .
like other large - scale surveillance data sources , our results must be interpreted cautiously . however ,
when used in conjunction with traditional surveillance methods , our approach has the potential to provide a timely estimate of changes in the geographic distribution of dengue , a critical component of targeted prevention and control efforts .
bivariate gaussian mixture model applied to extracted healthmap alerts to model a continuous surface of outbreak density .
 dengue , a potentially fatal disease , is spreading around the world . 
 an estimated 2.5 billion people in tropical and subtropical regions are at risk . 
 early detection of outbreaks is crucial to prevention and control of dengue virus and other viruses . 
 case reporting may often take weeks or months . 
 therefore , researchers explored whether electronic sources of real - time information ( such as internet news outlets , health expert mailing lists , social media sites , and queries to online search engines ) might be faster , and they were . 
 although information from unofficial sources should be interpreted with caution , when used in conjunction with traditional case reporting , real - time electronic surveillance can help public health authorities allocate resources in time to avert full - blown epidemics . 
The Study
Conclusions
Supplementary Material