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

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


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

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


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

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


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

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


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

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