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

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Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

2021 5th International Conference on Information Systems and Computer Networks (ISCON), Journal Year: 2023, Volume and Issue: unknown

Published: March 3, 2023

The COVID-19 Pandemic has been around for four years and remains a health concern everyone. Although things are somewhat returning to normal, increased incidence of cases in some regions the world (such as China, Japan, France, South Korea, etc.) bred worry anxiety world, including India. scientific community, which includes governmental organizations healthcare facilities, was eager learn how would develop. current work makes an attempt address this question by employing cutting-edge machine learning Deep Learning algorithms anticipate daily India over course next six months. For purpose famous timeseries were implemented LSTM, Bi-Directional LSTM Stacked Prophet. Owing success hybrid specific problem domains- present study also focuses on such like GRU-LSTM, CNN-LSTM with Attention. All these models have trained dataset performance metrics recorded. Of all models, simplistic performed better than complex ones. best result obtained Prophet, Bidirectional Vanilla LSTM. forecast reveals flat nature case load future

Language: Английский

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Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

International Journal of Environmental Research and Public Health, Journal Year: 2022, Volume and Issue: 19(9), P. 5099 - 5099

Published: April 22, 2022

COVID-19 is a disease caused by SARS-CoV-2 and has been declared worldwide pandemic the World Health Organization due to its rapid spread. Since first case was identified in Wuhan, China, battle against this deadly started disrupted almost every field of life. Medical staff laboratories are leading from front, but researchers various fields governmental agencies have also proposed healthy ideas protect each other. In article, Systematic Literature Review (SLR) presented highlight latest developments analyzing data using machine learning deep algorithms. The number studies related Machine Learning (ML), Deep (DL), mathematical models discussed research shown significant impact on forecasting spread COVID-19. results discussion study based PRISMA (Preferred Reporting Items for Reviews Meta-Analyses) guidelines. Out 218 articles selected at stage, 57 met criteria were included review process. findings therefore associated with those studies, which recorded that CNN (DL) SVM (ML) most used algorithms forecasting, classification, automatic detection. importance compartmental useful measuring epidemiological features Current suggest it will take around 1.7 140 days epidemic double size studies. 12 estimates basic reproduction range 0 7.1. main purpose illustrate use ML, DL, can be helpful generate valuable solutions higher authorities healthcare industry reduce epidemic.

Language: Английский

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Interpretable Temporal Attention Network for COVID-19 forecasting

Applied Soft Computing, Journal Year: 2022, Volume and Issue: 120, P. 108691 - 108691

Published: March 9, 2022

Language: Английский

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Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 224, P. 120034 - 120034

Published: April 5, 2023

Language: Английский

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Overcoming barriers in the use of artificial intelligence in point of care ultrasound

npj Digital Medicine, Journal Year: 2025, Volume and Issue: 8(1)

Published: April 19, 2025

Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians the acquisition and interpretation of images; however, there are growing concerns effectiveness trustworthiness. Here, we address key issues such as population bias, explainability training artificial this field propose approaches to ensure effectiveness.

Language: Английский

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An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect

Life, Journal Year: 2022, Volume and Issue: 12(5), P. 647 - 647

Published: April 27, 2022

Currently, the spread of COVID-19 is running at a constant pace. The current situation not so alarming, but every pandemic has history three waves. Two waves have been seen, and now expecting third wave. Compartmental models are one methods that predict severity pandemic. An enhanced SEIR model expected to new cases COVID-19. proposed an additional compartment vaccination. This SEIRV predicts when population vaccinated. simulated with conditions. first condition social distancing incorporated, while second included. combined result shows epidemic growth rate about 0.06 per day, number infected people doubles 10.7 days. Still, imparting distancing, obtained value R

Language: Английский

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GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks

Internet of Things, Journal Year: 2024, Volume and Issue: 26, P. 101214 - 101214

Published: May 7, 2024

Language: Английский

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A Security Games Inspired Approach for Distributed Control Of Pandemic Spread

Advanced Theory and Simulations, Journal Year: 2022, Volume and Issue: 6(2)

Published: Dec. 15, 2022

Abstract Pandemics are a source of extensive mortality, economic impairment, and dramatic social fluctuation. Once pandemic occurs, policymakers faced with the highly challenging task controlling it over time space. In this article, novel intervention policy that relies on strategic deployment inspection units (IUs) is proposed. These IUs allocated in environment, represented as graph, sample individuals who pass through same node. If sampled individual identified infected, she extracted from environment until recovers (or dies). A realistic simulation‐based evaluation Influenza pathogen using both synthetic real‐world data provided. The results demonstrate potential significant benefits proposed PIP mitigating spread which can complement other standard policies such distancing mask‐wearing.

Language: Английский

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Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Life, Journal Year: 2024, Volume and Issue: 14(7), P. 783 - 783

Published: June 21, 2024

By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control spread SARS-CoV-2 virus. Along with this, epidemiological machine learning studies have been frequently published. While these models can be perceived as precise and policy-relevant guide governments towards optimal containment policies, their black box nature hamper building trust relying confidently on prescriptions proposed. This paper focuses interpretable AI-based in context recent pandemic. We systematically review existing studies, which jointly incorporate AI, epidemiology, explainable approaches (XAI). First, we propose conceptual framework by synthesizing main methodological features pipelines SARS-CoV-2. Upon proposed analyzing selected reflect current research gaps toolboxes how fill generate enhanced policy support next potential

Language: Английский

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