ArticleChaos, solitons, and fractals2022
A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data.
Article in Chaos, solitons, and fractals, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 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
- Fairness Correction in COVID-19 Predictive Models Using Demographic Optimization: Algorithm Development and Validation Study.Online journal of public health informatics · 2026Article
- A machine learning model and molecular clusters of epigenetic chromatin regulators in tuberculosis based on bioinformatics and clinical samples.Scientific reports · 2025Article
- Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany.BMC public health · 2025Article
- Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing.International journal of population data science · 2025Article
- Article
- Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target.BMC chemistry · 2024Article
- A dynamic ensemble model for short-term forecasting in pandemic situations.PLOS global public health · 2024Article
- Development of a novel machine learning model based on laboratory and imaging indices to predict acute cardiac injury in cancer patients with COVID-19 infection: a retrospective observational study.Journal of cancer research and clinical oncology · 2023Observational
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan.Healthcare (Basel, Switzerland) · 2023Article
- Machine Learning and COVID-19: Lessons from SARS-CoV-2.Advances in experimental medicine and biology · 2023Article
- An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation.The Lancet. Digital health · 2022Review
- Design and Implementation of 3D Animation Data Processing Development Platform Based on Artificial Intelligence.Computational intelligence and neuroscience · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the COVID-19 pandemic, predicting case spikes at the local level is important for a precise, targeted public health response and is generally done with compartmental models. The performance of compartmental models is highly dependent on the accuracy of their assumptions about disease dynamics within a population; thus, such models are susceptible to human error, unexpected events, or unknown characteristics of a novel infectious agent like COVID-19. We present a relatively non-parametric random forest model that forecasts the number of COVID-19 cases at the U.S. county level. Its most prioritized training features are derived from easily accessible, standard epidemiological data (i.e., regional test positivity rate) and the effective reproduction number (
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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.