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Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries

variety of data sources, such as internet search information, flu-related Twitter microblogs [6,7], crowdsourced flu surveillance [8,9], clinician search activity [10], electronic health records [11], and Wikipedia access [12,13], as summarized in a study by Santillana

Leonardo Clemente, Fred Lu, Mauricio Santillana

JMIR Public Health Surveill 2019;5(2):e12214

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Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System

Kristin Baltrusaitis, Mauricio Santillana, Adam W Crawley, Rumi Chunara, Mark Smolinski, John S Brownstein

JMIR Public Health Surveill 2017;3(2):e18

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Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

MT is the number of media articles at date T; DT is the number of deaths at date T; CT is the number of cumulative cases at date T; and ϵT + δt is the normally distributed error term.Models were dynamically recalibrated, similar to the method presented by Santillana

Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica Davis, Alessandro Vespignani, Mauricio Santillana

J Med Internet Res 2020;22(8):e20285

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Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models

Mechanistic Models” (J Med Internet Res 2020;22(8):e20285) the authors noted three errors.The order of authors in the original article was listed as:Canelle Poirier, Dianbo Liu, Leonardo Clemente, Xiyu Ding, Matteo Chinazzi, Jessica Davis, Alessandro Vespignani, Mauricio

Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica Davis, Alessandro Vespignani, Mauricio Santillana

J Med Internet Res 2020;22(9):e23996

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Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation

Kristin Baltrusaitis, Alessandro Vespignani, Roni Rosenfeld, Josh Gray, Dorrie Raymond, Mauricio Santillana

JMIR Public Health Surveill 2019;5(4):e13403

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A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives

Ruchit Nagar, Qingyu Yuan, Clark C Freifeld, Mauricio Santillana, Aaron Nojima, Rumi Chunara, John S Brownstein

J Med Internet Res 2014;16(10):e236

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Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak

Maimuna S Majumder, Mauricio Santillana, Sumiko R Mekaru, Denise P McGinnis, Kamran Khan, John S Brownstein

JMIR Public Health Surveill 2016;2(1):e30

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Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis

In Santillana et al [15], an ensemble approach combining these data sources outperformed any other methods in national flu predictions.

Fred Sun Lu, Suqin Hou, Kristin Baltrusaitis, Manan Shah, Jure Leskovec, Rok Sosic, Jared Hawkins, John Brownstein, Giuseppe Conidi, Julia Gunn, Josh Gray, Anna Zink, Mauricio Santillana

JMIR Public Health Surveill 2018;4(1):e4

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Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study

Santillana et al proposed a model using HBD and a machine learning algorithm (support vector machine [SVM]) with a good performance at the regional scale [7].

Canelle Poirier, Audrey Lavenu, Valérie Bertaud, Boris Campillo-Gimenez, Emmanuel Chazard, Marc Cuggia, Guillaume Bouzillé

JMIR Public Health Surveill 2018;4(4):e11361

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Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study

fold (4 instances) and the subsequent high variation in RMSE, evaluation of the LSTM model was repeated with 3-fold cross-validation and the obtained RMSE was 13.45 (SD 7.90).Past work on influenza and Zika virus predictions, for example, in the study by Santillana

Seyed Mohammad Ayyoubzadeh, Seyed Mehdi Ayyoubzadeh, Hoda Zahedi, Mahnaz Ahmadi, Sharareh R Niakan Kalhori

JMIR Public Health Surveill 2020;6(2):e18828

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