title: Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data.
abstract: Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015-2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission.
  
  
  
  We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015-2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions.
  
  
  
  Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak.
authors.affiliation: Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.|Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, United States of America.
authors.forename: Sarah F
authors.initials: SF
authors.lastname: McGough
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Jared B
authors.initials: JB
authors.lastname: Hawkins
authors.affiliation: Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Computational Epidemiology Group, Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, United States of America.|Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Mauricio
authors.initials: M
authors.lastname: Santillana
mesh_terms: D004196:Disease Outbreaks; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015994:Incidence; D007843:Latin America / Q000453:epidemiology; D016014:Linear Models; D015233:Models, Statistical; D015999:Multivariate Analysis; D011159:Population Surveillance / Q000379:methods*; D061108:Social Media / Q000706:statistics & numerical data*; D000071244:Zika Virus; D000071243:Zika Virus Infection / Q000453:epidemiology*