Deep learning models predict COVID-19 spread.
problem Predicting the spread of COVID-19 to mitigate its impact.
method Proposed DSPM and NRM models trained on 19.53M cases.
result Superior prediction performance of proposed models.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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48 results for “COVID-19 prediction”
DeCom predicts post-COVID RSV timing and intensity with NPI consideration.
problem Predicting RSV timing and intensity post-COVID with NPI impact.
method Deep coupled tensor factorization machine (DeCom) leveraging tensor factorization and residual modeling.
result DeCom achieves up to 46% lower RMSE and 49% lower MAE compared to baselines.
Study shows tweets about COVID-19 can predict stock market performance.
problem Understanding the impact of COVID-19 on stock markets.
method Text sentiment analysis of Twitter data to correlate tweets about COVID-19 with stock market performance.
result Strong relationship between COVID-19 sentiment and stock market performance can be predicted.
MLHO predicts COVID-19 adverse outcomes using past medical records.
problem Predicting adverse outcomes after COVID-19 infection.
method Iterative feature and algorithm selection, sequential representation mining.
result Mean AUC ROC of 0.91 for mortality prediction.
condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scalecs.LG
condLSTM-Q predicts COVID-19 deaths at county level with quantile forecasts.
problem Predicting COVID-19 mortality at fine geographical scales.
method Conditional Long Short-Term Memory networks with quantile output.
result Fine-scale quantile predictions inform about death toll distribution.
AI model predicts stock prices using social media data and hybrid neural networks.
problem Predicting stock price movements during the COVID-19 pandemic.
method Integrates social media trends and historical stock data using a hybrid CNN-BLSTM framework.
result The proposed framework outperforms traditional models in predicting stock price movements.
LSTM predicts COVID-19 growth patterns from global data.
problem Forecasting the spread of COVID-19 using big data and machine learning.
method Used multivariate long short-term memory (LSTM) to learn correlations over time.
result LSTM outperformed RNN in predicting COVID-19 growth with lower validation error.
Paper uses machine learning and SIR models to predict COVID-19 cases.
problem Predicting the spread of COVID-19 cases for control measures.
method Machine learning and SIR models (deterministic and stochastic) with numerical approximations.
result Predictions help in finding concrete actions to control the pandemic.
Predicts COVID-19 spread using dictionary learning and online NMF.
problem Limited daily case data for accurate prediction.
method Joint dictionary learning and online NMF for short evolution instances.
result Learned dictionary patterns improve predictions over time.
Study predicts future hospitalizations to manage COVID-19 patient surge.
problem Managing hospitalization needs during COVID-19 pandemic.
method Used 4 recurrent neural networks to predict hospitalization changes.
result Sequence to sequence model with attention achieved high accuracy and AUC.
The paper uses regression models to predict COVID-19 spread and its stock market impact.
problem Predicting and understanding the impact of COVID-19 on stock markets.
method Logistic curve model with Bayesian regression for predictive analytics.
result Different crises have different impacts on the same stocks.
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank studycs.LG
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
problem Developing tools to monitor high-risk patients during the COVID-19 pandemic.
method Data-driven random forest classification model using baseline characteristics and symptoms.
result Model predicts COVID-19 mortality with excellent performance (AUC: 0.91), identifying novel predictors.
The study uses stock market indicators to forecast COVID-19 cases.
problem Forecasting the spread of COVID-19 for resource allocation.
method Reinterpreting daily cases as candlesticks and applying stock market indicators.
result The stock market indicators show statistical significance in predicting COVID-19 cases.
Study develops models to predict severe COVID-19 progression.
problem Identifying high-risk individuals for severe COVID-19.
method Developed survival models using EHR data, learning clinical concepts to improve accuracy.
result Learned clinical concepts improve model performance, boosting C-index to 0.858.
Bayesian-Deep Learning model predicts Covid-19 evolution in Spain.
problem Estimating the future evolution of Covid-19 in Spain.
method Combines DL techniques with Bayesian Poisson-Gamma model for counts.
result Allows prediction of future evolution and estimation of scenarios.
Machine learning model diagnoses COVID-19 from routine blood tests.
problem Difficulty in diagnosing COVID-19 due to inconsistent blood parameter changes.
method Constructed a machine learning model using 5,333 patients with various infections and 160 COVID-19-positive patients.
result Cross-validated AUC of 0.97, sensitivity of 81.9%, specificity of 97.9%.
Machine learning predicts COVID-19 activity in China.
problem Real-time forecasting of COVID-19 activity in Chinese provinces.
method Combines mechanistic disease models with digital traces (internet searches, news alerts). Uses clustering and data augmentation techniques.
result Stable and accurate forecasts 2 days ahead of current time, outperforming baseline models in 27 out of 32 provinces.
Model predicts increased social unrest during COVID-19 using social media data.
problem Detecting rising conflict potential in societies during pandemics.
method Neural implicit motive pattern recognition from social media texts.
result Significant increase in conflict indicators during the pandemic.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
Study predicts US stock market will continue to fall post-COVID-19.
problem Analyzing the recovery trend of the US stock market post-COVID-19.
method Used Deep Learning, Neuro Network, and Time-series analysis on S&P 500, Nasdaq 100, and Dow Jones Industrial Average data.
result LSTM model predicts US stock market will continue to fall post-COVID-19.
Deep learning model predicts severe COVID-19 outcomes.
problem Predicting severe COVID-19 outcomes in ED patients.
method Deep feature fusion model using EHR data and CXR images.
result CO-RISK score achieved AUC of 0.95 and 0.92 for 24 and 72 hours predictions, superior to human performance.
Small sample size hinders accurate long-term COVID-19 case predictions.
problem Difficulty in predicting medium and long-term COVID-19 case trends.
method Analysis of machine learning models' performance; feature selection; comparison of different models.
result Simple linear regression models provide reliable 2-week predictions but not beyond.
Hybrid model improves COVID-19 case forecasting accuracy.
problem Limited data and simplistic models for accurate prediction.
method Combining SEIR and RNN on a graph structure with local and edge features.
result Improves prediction accuracy on state-level COVID-19 data.
STNN models forecast COVID-19 spread with improved accuracy.
problem Forecasting the spread of COVID-19 worldwide.
method Spatio-temporal Neural Network (STNN) incorporating spatial and temporal data.
result STNN models outperform classical models in accuracy and handling both spatial and temporal data.
Method summarizes and predicts time series data for COVID-19 cases and deaths.
problem Summarizing and predicting time series data for multiple related time series.
method Hierarchical algorithm generating shapelets for centroids, nearest neighbor search for labeling, dynamic time warping for non-uniform lengths.
result Predictive model for individual time series based on aggregated statistics.
LSTM models predict low likelihood of another COVID-19 wave in India.
problem Inaccurate and unreliable COVID-19 infection forecasting models due to data limitations and model complexity.
method Application of LSTM, bidirectional LSTM, and encoder-decoder LSTM models for multi-step infection forecasting.
result Predictions indicate low likelihood of another wave in October and November 2021.
Conformal prediction fails under severe feature turnover in COVID-19 supply chain tasks.
problem Dealing with distribution shift in conformal prediction models.
method Using COVID-19 as a natural experiment across 8 supply chain tasks, analyzing SHAP explanations.
result Coverage drops vary widely (0% to 86.7%) and correlate with single-feature dependence.
Evaluating the Impact of COVID-19 Vaccination in the United Kingdom: A Gaussian Process Approachstat.ME
UK's rapid vaccine rollout linked to reduced COVID-19 mortality.
problem Assessing the impact of accelerated vaccine rollout on public health outcomes.
method Flexible probabilistic models combining interrupted time series analysis and synthetic control methods with multi-output Gaussian processes.
result Substantial reduction in COVID-19 mortality with little effect on transmission rates.
Mathematical models help keep vaccine prices low.
problem Pricing COVID-19 vaccines to ensure affordability and profitability.
method Optimization and game theory approaches modeling a duopoly market.
result Government can negotiate low prices while manufacturers earn profits.
Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detectioneess.IV
Bayesian CNN estimates uncertainty in COVID-19 detection.
problem Uncertainty in deep learning predictions for medical diagnosis.
method Drop-weight based Bayesian Convolutional Neural Networks (BCNN).
result Uncertainty correlates with prediction accuracy.
Model predicts US COVID-19 deaths with quantile estimates.
problem Predicting US COVID-19 deaths at county level.
method Hybrid machine learning and epidemiological approach, minimizing pinball loss.
result Quantile estimates accurately forecast deaths for different forecast periods.
Predictive models identify patients at risk of severe COVID-19.
problem Identifying patients at risk of severe COVID-19 to ease healthcare strain.
method Machine learning on routinely collected clinical data.
result Models predict positive SARS-CoV-2 tests, hospitalizations, and critical care with high accuracy.
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
problem Impact of COVID-19 on consumer behavior in the restaurant industry.
method Bayesian regression with Hamiltonian Monte Carlo.
result Estimates change in consumer behavior before and after the pandemic.
Deep learning ensembles improve COVID-19 detection from chest X-rays.
problem Detecting COVID-19 from chest X-rays using machine learning.
method Custom CNN and ImageNet models, transfer learning, iterative pruning, ensemble learning.
result 99.01% accuracy in detecting COVID-19 from chest X-rays.
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreakstat.ML
This paper compares ML models for predicting COVID-19 trends.
problem Forecasting the spread of COVID-19 to reduce its impact.
method Applied ARDL method to identify relationships, then used ML models (SVM, RF, KNN, ANN) for prediction.
result Models accurately forecasted COVID-19 cases with low MAPE.
The paper predicts financial markets using news text and semantic network analysis.
problem Predicting financial markets with news data.
method Semantic network analysis of news text to assess economic keywords' importance.
result The index captures financial market phases and predicts returns and volatilities.
Study uses healthcare claims data to identify Covid-19 risk factors without prior selection.
problem Identify risk factors for severe Covid-19 cases.
method Fine-grained hierarchical information from medical classification systems used to analyze over 33,000 covariates.
result Method has better predictive ability than pre-specified morbidity groups.
Study uses ML to predict non-participation in ELSA COVID-19 follow-up studies.
problem Predicting non-participation in follow-up surveys due to various factors.
method Used machine learning algorithms including KNN, RF, AdaBoost, logistic regression, NN, and SVC.
result Random Forest (RF) outperforms other models in balanced accuracy.
DA-PredGAN uses GANs for accurate COVID-19 spread predictions and data assimilation.
problem Predicting and understanding the spread of COVID-19.
method Generative adversarial network (GAN) for predictions and data assimilation.
result DA-PredGAN accurately predicts and assimilates COVID-19 spread data.
Modification of a Numerical Method Using FIR Filters in a Time-dependent SIR Model for COVID-19stat.ML
Researchers use FIR filters to predict COVID-19 infections and recoveries.
problem Predicting the spread of COVID-19 without a vaccine.
method Modified numerical method using FIR filters and ridge regression.
result Modified algorithm yields better approximation errors.
New model predicts financial connectedness via COVID-19 spread.
problem Predicting financial connectedness during COVID-19 spread.
method Semiparametric matrix regression model with Bayesian hierarchical mixture prior.
result Model captures heterogeneity in network responses to risk factors.
Incremental machine learning models predict COVID-19 cases more efficiently than traditional methods.
problem Predicting the spread of COVID-19 cases in real-time across multiple countries.
method Comparison of online incremental machine learning algorithms against traditional LSTM models.
result Incremental machine learning models are more efficient and computationally cheaper than traditional methods.
Study predicts U.S. county COVID-19 growth using demographic and social distancing data.
problem Predicting county-level COVID-19 growth during the pandemic.
method Spectral clustering, correlation matrix, demographic features, social distancing scores, LSTM model.
result Effective prediction of future county growth using demographic and social distancing data.
This study interprets machine learning models to identify biomarkers for severe COVID-19 infection.
problem The black-box nature of machine learning models makes it difficult for medical researchers to understand and trust their predictions.
method The study uses permutation feature importance, Partial Dependence Plot, Individual Conditional Expectation, Accumulated Local Effects, Local Interpretable Model-agnostic Explanations, and Shapley Additive Explanation to interpret four machine learning models.
result The study identifies NTproBNP, CRP, LDH, LYM, leukocytes, eosinophils, and platelets as biomarkers associated with severe COVID-19 infection.
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
problem Impact of COVID-19 on stock and oil prices forecasting.
method Integrates Stationary Wavelet Transform and Bidirectional Long Short-Term Memory networks.
result BDLSTM+WT-ADA achieved satisfactory results in Crude Oil price forecasting.
Study evaluates stock price forecasting models during the pandemic.
problem Forecasting stock prices during the Covid-19 pandemic.
method Four models (Long-Short Term Memory, XGBoost, Autoregression, Last Value) were tested on stock prices of Facebook, Amazon, Tesla, Google, and Apple.
result Autoregression and Last Value models outperform other models due to strong correlation between prices.
Model predicts COVID-19 progression with interpretability.
problem Accurate and credible forecasting of COVID-19 progression.
method Integrates machine learning into disease modeling, uses interpretable encoders.
result More accurate forecasts than state-of-the-art alternatives.
Model predicts COVID-19 cases with high accuracy using mobility data.
problem Accurate forecasting of COVID-19 cases for resource management.
method Heterogeneous infection rate model with human mobility, linearization, weighted least squares.
result Extremely accurate predictions of confirmed cases at country and state levels.