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Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study

Forecasting models are used to generate early warnings to identify how a pandemic might evolve.

Philip J Turk, Shih-Hsiung Chou, Marc A Kowalkowski, Pooja P Palmer, Jennifer S Priem, Melanie D Spencer, Yhenneko J Taylor, Andrew D McWilliams

JMIR Public Health Surveill 2020;6(2):e19353

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Patient-Specific Predictive Modeling Using Random Forests: An Observational Study for the Critically Ill

Joon Lee

JMIR Med Inform 2017;5(1):e3

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Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet

Gunther Eysenbach

J Med Internet Res 2009;11(1):e11

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Readmission Risk Trajectories for Patients With Heart Failure Using a Dynamic Prediction Approach: Retrospective Study

Wei Jiang, Sauleh Siddiqui, Sean Barnes, Lili A Barouch, Frederick Korley, Diego A Martinez, Matthew Toerper, Stephanie Cabral, Eric Hamrock, Scott Levin

JMIR Med Inform 2019;7(4):e14756

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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

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Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic

Using mathematical models to exploit COVID-19 transmission dynamics, nowcasting also has been extended to COVID-19 forecasting systems [14,15].

Sharon K Greene, Sarah F McGough, Gretchen M Culp, Laura E Graf, Marc Lipsitch, Nicolas A Menzies, Rebecca Kahn

JMIR Public Health Surveill 2021;7(1):e25538

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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

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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

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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

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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

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