Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 10, 2025

Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for modeling. While fusion AI and traditional approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview emerging integrated applied across spectrum infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our highlights practical value models, including advances in disease forecasting, model parameterization, calibration. However, key research gaps remain. These include need better incorporation realistic decision-making considerations, expanded exploration diverse datasets, further investigation into biological socio-behavioral mechanisms. Addressing these will unlock synergistic modeling to enhance understanding dynamics support more effective public health planning response. Artificial has improve diseases by incorporating data sources complex interactions. Here, authors conduct use summarise methodological advancements identify gaps.

Language: Английский

Epidemic Modeling using Hybrid of Time-varying SIRD, Particle Swarm Optimization, and Deep Learning

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Journal Year: 2023, Volume and Issue: unknown

Published: July 6, 2023

Epidemiological models are best suitable to model an epidemic if the spread pattern is stationary. To deal with non-stationary patterns and multiple waves of epidemic, we develop a hybrid encompassing modeling, particle swarm optimization, deep learning. The mainly caters three objectives for better prediction: 1. Periodic estimation parameters. 2. Incorporating impact all aspects using data fitting parameter optimization 3. Deep learning based prediction In our model, use system ordinary differential equations (ODEs) Susceptible-Infected-Recovered-Dead (SIRD) Particle Swarm Optimization (PSO) stacked-LSTM forecasting Initial or one time parameters not able epidemic. So, estimate periodically (weekly). We PSO identify optimum values next train on optimized parameters, perform upcoming four weeks. Further, fed LSTM forecasted into SIRD forecast number COVID-19 cases. evaluate highly affected countries namely; USA, India, UK. proposed waves, has outperformed existing methods datasets.

Language: Английский

From Policy to Prediction: Assessing Forecasting Accuracy in an Integrated Framework with Machine Learning and Disease Models

Journal of Computational Biology, Journal Year: 2024, Volume and Issue: 31(11), P. 1104 - 1117

Published: Aug. 2, 2024

To improve the forecasting accuracy of spread infectious diseases, a hybrid model was recently introduced where commonly assumed constant disease transmission rate actively estimated from enforced mitigating policy data by machine learning (ML) and then fed to an extended susceptible-infected-recovered forecast number infected cases. Testing only one ML model, that is, gradient boosting (GBM), work left open whether other models would perform better. Here, we compared GBMs, linear regressions, k-nearest neighbors, Bayesian networks (BNs) in COVID-19-infected cases United States Canadian provinces based on indices future 35 days. There no significant difference mean absolute percentage errors these over combined dataset [ H(3)=3.10,p=0.38]. In two provinces, observed H(3)=8.77,H(3)=8.07,p<0.05], yet posthoc tests revealed pairwise comparisons. Nevertheless, BNs significantly outperformed most training datasets. The results put forward have equal power overall, are best for data-fitting applications.

Language: Английский