- COVID-19 prediction models: a systematic literature review
-
Sheikh Muzaffar Shakeel, Nithya Sathya Kumar, Pranita Pandurang Madalli, Rashmi Srinivasaiah, Devappa Renuka Swamy
-
Osong Public Health Res Perspect. 2021;12(4):215-229. Published online August 13, 2021
-
DOI: https://doi.org/10.24171/j.phrp.2021.0100
-
-
10,185
View
-
216
Download
-
21
Web of Science
-
19
Crossref
-
Abstract
PDF
- As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.
-
Citations
Citations to this article as recorded by
- The Telemedicine Demand Index and its Utility in Managing COVID-19 Case Surges
Martin Yong Kwong Lee, Kie Beng Goh, Deanna Xiuting Koh, Si Jack Chong, Raymond Swee Boon Chua Telemedicine and e-Health.2024; 30(2): 545. CrossRef - Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants
J. Cuevas-Maraver, P.G. Kevrekidis, Q.Y. Chen, G.A. Kevrekidis, Y. Drossinos Mathematical Biosciences.2024; 367: 109109. CrossRef - The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews
Persefoni Talimtzi, Antonios Ntolkeras, Georgios Kostopoulos, Konstantinos I. Bougioukas, Eirini Pagkalidou, Andreas Ouranidis, Athanasia Pataka, Anna-Bettina Haidich Journal of Clinical Epidemiology.2024; 167: 111264. CrossRef - A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care
Junyi Gao, Yinghao Zhu, Wenqing Wang, Zixiang Wang, Guiying Dong, Wen Tang, Hao Wang, Yasha Wang, Ewen M. Harrison, Liantao Ma Patterns.2024; 5(4): 100951. CrossRef - A study of learning models for COVID-19 disease prediction
Sakshi Jain, Pradeep Kumar Roy Journal of Ambient Intelligence and Humanized Comp.2024; 15(4): 2581. CrossRef - AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods
Muhammad Usman Tariq, Shuhaida Binti Ismail Osong Public Health and Research Perspectives.2024; 15(2): 115. CrossRef - Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge
Hongyan Liao, Christopher J. Lyon, Binwu Ying, Tony Hu Emerging Microbes & Infections.2024;[Epub] CrossRef - Digital Technology Ecotone to Revolutionize Health Sector
Mario Coccia SSRN Electronic Journal.2024;[Epub] CrossRef - Leveraging advances in data-driven deep learning methods for hybrid epidemic modeling
Shi Chen, Daniel Janies, Rajib Paul, Jean-Claude Thill Epidemics.2024; 48: 100782. CrossRef - Assessing the Utility of Prediction Scores PAINT, ISARIC4C, CHIS, and COVID-GRAM at Admission and Seven Days after Symptom Onset for COVID-19 Mortality
Alina Doina Tanase, Oktrian FNU, Dan-Mihai Cristescu, Paula Irina Barata, Dana David, Emanuela-Lidia Petrescu, Daliana-Emanuela Bojoga, Teodora Hoinoiu, Alexandru Blidisel Journal of Personalized Medicine.2024; 14(9): 966. CrossRef - An effective drift-diffusion model for pandemic propagation and uncertainty prediction
Clara Bender, Abhimanyu Ghosh, Hamed Vakili, Preetam Ghosh, Avik W. Ghosh Biophysical Reports.2024; 4(4): 100182. CrossRef - Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan
Berik Koichubekov, Aliya Takuadina, Ilya Korshukov, Anar Turmukhambetova, Marina Sorokina Healthcare.2023; 11(5): 752. CrossRef - Early triage echocardiography to predict outcomes in patients admitted with COVID‐19: a multicenter study
Daniel Peck, Andrea Beaton, Maria Carmo Nunes, Nicholas Ollberding, Allison Hays, Pranoti Hiremath, Federico Asch, Nitin Malik, Christopher Fung, Craig Sable, Bruno Nascimento Echocardiography.2023; 40(5): 388. CrossRef - Static Seeding and Clustering of LSTM Embeddings to Learn From Loosely Time-Decoupled Events
Christian G. Manasseh, Razvan Veliche, Jared Bennett, Hamilton Scott Clouse IEEE Access.2023; 11: 64219. CrossRef - Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting
Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahmad, Lin Wang PLOS ONE.2023; 18(7): e0287755. CrossRef - Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study
Riku Klén, Ivan A Huespe, Felipe Aníbal Gregalio, Antonio Lalueza Lalueza Blanco, Miguel Pedrera Jimenez, Noelia Garcia Barrio, Pascual Ruben Valdez, Matias A Mirofsky, Bruno Boietti, Ricardo Gómez-Huelgas, José Manuel Casas-Rojo, Juan Miguel Antón-Santos eLife.2023;[Epub] CrossRef - Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era
Gabriel Berg de Almeida, Lorena Mendes Simon, Ângela Maria Bagattini, Michelle Quarti Machado da Rosa, Marcelo Eduardo Borges, José Alexandre Felizola Diniz Filho, Ricardo de Souza Kuchenbecker, Roberto André Kraenkel, Cláudia Pio Ferreira, Suzi Alves Cam PLOS Global Public Health.2023; 3(12): e0002679. CrossRef - Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic
Jose M. Martin-Moreno, Antoni Alegre-Martinez, Victor Martin-Gorgojo, Jose Luis Alfonso-Sanchez, Ferran Torres, Vicente Pallares-Carratala International Journal of Environmental Research an.2022; 19(9): 5546. CrossRef - Artificial intelligence and clinical deterioration
James Malycha, Stephen Bacchi, Oliver Redfern Current Opinion in Critical Care.2022; 28(3): 315. CrossRef
|