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Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches

Combining participatory surveillance with modeling and simulation can not only help to reduce participatory bias but can also improve real-time forecasting and thus help identify which interventions are most likely to be effective over time in a given area.In

John S Brownstein, Shuyu Chu, Achla Marathe, Madhav V Marathe, Andre T Nguyen, Daniela Paolotti, Nicola Perra, Daniela Perrotta, Mauricio Santillana, Samarth Swarup, Michele Tizzoni, Alessandro Vespignani, Anil Kumar S Vullikanti, Mandy L Wilson, Qian Zhang

JMIR Public Health Surveill 2017;3(4):e83

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review

Google Trends provides the field of big data with new opportunities, as it has been shown to be valid [13] and has been proven valuable [14,15], accurate [16], and beneficial [17] for forecasting.

Amaryllis Mavragani, Gabriela Ochoa, Konstantinos P Tsagarakis

J Med Internet Res 2018;20(11):e270

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study

Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study

We used surveillance data from 2008-2017 to establish the framework of the forecasting models.Forecasting Targets and the Renewal of the Surveillance Data and ModelsThe forecasting targets in our study were short-term forecasts—weekly number of influenza-like

Hao-Yuan Cheng, Yu-Chun Wu, Min-Hau Lin, Yu-Lun Liu, Yue-Yang Tsai, Jo-Hua Wu, Ke-Han Pan, Chih-Jung Ke, Chiu-Mei Chen, Ding-Ping Liu, I-Feng Lin, Jen-Hsiang Chuang

J Med Internet Res 2020;22(8):e15394

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

Peak Outpatient and Emergency Department Visit Forecasting for Patients With Chronic Respiratory Diseases Using Machine Learning Methods: Retrospective Cohort Study

Nevertheless, the crowding could be alleviated and mitigated considerably by forecasting levels of demand for OED care and giving health care staff an opportunity to prepare for this demand [5].

Junfeng Peng, Chuan Chen, Mi Zhou, Xiaohua Xie, Yuqi Zhou, Ching-Hsing Luo

JMIR Med Inform 2020;8(3):e13075

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach

DeepMood by Suhara et al [22] is a solution for forecasting severely depressed mood from self-reported histories using a recurrent neural network.

Jonas Busk, Maria Faurholt-Jepsen, Mads Frost, Jakob E Bardram, Lars Vedel Kessing, Ole Winther

JMIR Mhealth Uhealth 2020;8(4):e15028