ArticleOsong public health and research perspectives2021
COVID-19 prediction models: a systematic literature review.
Article in Osong public health and research perspectives, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic.International journal of environmental research and public health · 2022Pooled it
- COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors.BMC public health · 2025Article
- An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics.Archives of public health = Archives belges de sante publique · 2025Article
- An effective drift-diffusion model for pandemic propagation and uncertainty prediction.Biophysical reports · 2024Article
- Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge.Emerging microbes & infections · 2024Review
- Assessing the Utility of Prediction Scores PAINT, ISARIC4C, CHIS, and COVID-GRAM at Admission and Seven Days after Symptom Onset for COVID-19 Mortality.Journal of personalized medicine · 2024Article
- COVID-19 surveillance report for Sudan, 2020 to 2021.Heliyon · 2024Article
- A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care.Patterns (New York, N.Y.) · 2024Article
- AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods.Osong public health and research perspectives · 2024Article
- Article
- Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan.Healthcare (Basel, Switzerland) · 2023Article
- Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency.AIMS public health · 2023Review
- Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting.PloS one · 2023Article
- Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era.PLOS global public health · 2023Article
- Assessment of the Benefits of Targeted Interventions for Pandemic Control in China Based on Machine Learning Method and Web Service for COVID-19 Policy Simulation.Biomedical and environmental sciences : BES · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.