ArticlePLoS computational biology2021
Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025Review
- How can timeliness and accuracy be balanced? The surveillance information reporting mechanism for public health emergencies based on the online direct reporting system.BMC public health · 2025Article
- Analysis of a mathematical model for malaria using data-driven approach.Scientific reports · 2025Article
- Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism.Scientific reports · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Machine learning in infectious diseases: potential applications and limitations.Annals of medicine · 2024Review
- Long-term trend prediction of pandemic combining the compartmental and deep learning models.Scientific reports · 2024Article
- Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.Scientific reports · 2024Article
- Analytical reference framework to analyze non-COVID-19 events.Population health metrics · 2023Article
- The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions.Immunoinformatics (Amsterdam, Netherlands) · 2023Review
- Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.EBioMedicine · 2023Article
- Dynamic model of respiratory infectious disease transmission in urban public transportation systems.Heliyon · 2023Article
- Towards evolutionary predictions: Current promises and challenges.Evolutionary applications · 2023Review
- Article
- Estimation of patient flow in hospitals using up-to-date data. Application to bed demand prediction during pandemic waves.PloS one · 2023Article
- Deep learning for Covid-19 forecasting: State-of-the-art review.Neurocomputing · 2022Article
- An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.PLoS computational biology · 2022Article
- Urban spatial risk prediction and optimization analysis of POI based on deep learning from the perspective of an epidemic.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization algorithm.Digital signal processing · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.
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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.