ArticleResults in physics2021
Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms.
Article in Results in physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 146 citations in OpenAlex.
- Early adherence predicts treatment outcomes and mortality in people living with HIV and multidrug-resistant tuberculosis.Annals of epidemiology · 2026Article
- Application of seasonal-adjusted hybrid models for forecasting Discomfort Index in a heat-prone region of Bangladesh.PloS one · 2026Article
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- Wastewater as an early indicator for short-term forecasting COVID-19 hospitalization in Germany.BMC public health · 2025Article
- Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand.Animals : an open access journal from MDPI · 2025Article
- Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting.Scientific reports · 2025Article
- Comparing Statistical and Machine Learning Methods for Time Series Forecasting in Data-Driven Logistics-A Simulation Study.Entropy (Basel, Switzerland) · 2024Article
- Forecasting and analyzing influenza activity in Hebei Province, China, using a CNN-LSTM hybrid model.BMC public health · 2024Article
- Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques.Scientific reports · 2024Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
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- A dynamic ensemble model for short-term forecasting in pandemic situations.PLOS global public health · 2024Article
- Machine learning-based mortality prediction models for smoker COVID-19 patients.BMC medical informatics and decision making · 2023Article
- A new hybrid prediction model of COVID-19 daily new case data.Engineering applications of artificial intelligence · 2023Article
- An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM.Scientific reports · 2023Article
- A novel prediction model based on decomposition-integration and error correction for COVID-19 daily confirmed and death cases.Computers in biology and medicine · 2023Article
- Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach.EBioMedicine · 2023Article
- DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm.SN computer science · 2023Article
- A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh.PloS one · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we establish daily confirmed infected cases prediction models for the time series data of America by applying both the long short-term memory (LSTM) and extreme gradient boosting (XGBoost) algorithms, and employ four performance parameters as MAE, MSE, RMSE, and MAPE to evaluate the effect of model fitting. LSTM is applied to reliably estimate accuracy due to the long-term attribute and diversity of COVID-19 epidemic data. Using XGBoost model, we conduct a sensitivity analysis to determine the robustness of predictive model to parameter features. Our results reveal that achieving a reduction in the contact rate between susceptible and infected individuals by isolated the uninfected individuals, can effectively reduce the number of daily confirmed cases. By combining the restrictive social distancing and contact tracing, the elimination of ongoing COVID-19 pandemic is possible. Our predictions are based on real time series data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced.
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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.