DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction DOI Creative Commons
Chaoyue Sun, Ruogu Fang,

Marco Salemi

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Июль 17, 2023

In the midst of an outbreak or sustained epidemic, reliable prediction transmission risks and patterns spread is critical to inform public health programs. Projections growth decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used detection chains high-risk populations. Moreover, tree topology incorporation population parameters (phylodynamics) be useful reconstruct evolutionary dynamics epidemic across space time individuals. We now demonstrate utility phylodynamic for infection forecasting addition backtracking, developing a phylogeny-based deep learning system, called DeepDynaForecast . Our approach leverages primal-dual graph structure with shortcut multi-layer aggregation, it suited early identification emerging groups. accuracy using simulated data learned model empirical, large-scale from human immunodeficiency virus Florida between 2012 2020. framework available as open-source software (MIT license) at: https://github.com/lab-smile/DeepDynaForcast Author Summary During accurate reliably strategies. indicating significantly enhance optimization especially To address this, we present , cutting-edge algorithm designed pathogen dynamics. Uniquely, was trained on in-depth simulation more information phylogenetic sequence than any other field date, allowing classification samples according their (growth, static, decline) incredible accuracy. evaluated model’s performance both HIV conclude represents significant advancement genomics-mediated characterization has potential catalyze new research directions within virology, molecular biology, health.

Язык: Английский

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

и другие.

Neurocomputing, Год журнала: 2024, Номер 577, С. 127317 - 127317

Опубликована: Янв. 26, 2024

Язык: Английский

Процитировано

58
An Ensemble Approach to Predict a Sustainable Energy Plan for London Households DOI Open Access

Niraj Buyo,

Akbar Sheikh-Akbari, Farrukh Saleem

и другие.

Sustainability, Год журнала: 2025, Номер 17(2), С. 500 - 500

Опубликована: Янв. 10, 2025

The energy sector plays a vital role in driving environmental and social advancements. Accurately predicting demand across various time frames offers numerous benefits, such as facilitating sustainable transition planning of resources. This research focuses on consumption using three individual models: Prophet, eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM). Additionally, it proposes an ensemble model that combines the predictions from all to enhance overall accuracy. approach aims leverage strengths each for better prediction performance. We examine accuracy Mean Absolute Error (MAE), Percentage (MAPE), Root Square (RMSE) through means resource allocation. investigates use real data smart meters gathered 5567 London residences part UK Power Networks-led Low Carbon project Datastore. performance was recorded follows: 62.96% Prophet model, 70.37% LSTM, 66.66% XGBoost. In contrast, proposed which XGBoost, achieved impressive 81.48%, surpassing models. findings this study indicate enhances efficiency supports towards future. Consequently, can accurately forecast maximum loads distribution networks households. addition, work contributes improvement load forecasting networks, guide higher authorities developing plans.

Язык: Английский

Процитировано

2
Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review DOI Creative Commons
Saeed Shakibfar, Fredrik Nyberg, Huiqi Li

и другие.

Frontiers in Public Health, Год журнала: 2023, Номер 11

Опубликована: Июнь 20, 2023

Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary secondary data sources. Study eligibility criteria Cohort, clinical trials, meta-analyses, observational studies investigating or artificial intelligence were eligible. Articles without full text available in English language excluded. Data sources recorded Ovid MEDLINE from 01/01/2019 to 22/08/2022 screened. extraction We extracted information sources, AI models, epidemiological aspects retrieved studies. Bias assessment A bias models was done PROBAST. Participants Patients tested positive COVID-19. Results included 39 related AI-based prediction death The articles published period 2019-2022, mostly used Random Forest as model with best performance. trained cohorts individuals sampled populations European non-European countries, cohort sample size <5,000. collection generally demographics, records, laboratory results, pharmacological treatments (i.e., high-dimensional datasets). In most studies, internally validated cross-validation, but majority lacked external validation calibration. Covariates not prioritized ensemble approaches however, still showed moderately good performances Area under Receiver operating characteristic Curve (AUC) values >0.7. According PROBAST, all had high risk and/or concern regarding applicability. Conclusions broad range have been predict mortality. reported performance applicability detected.

Язык: Английский

Процитировано

16
Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing) DOI Open Access

Sridhar Siripurapu,

Naresh K. Darimireddy, Abdellah Chehri

и другие.

Electronics, Год журнала: 2023, Номер 12(3), С. 750 - 750

Опубликована: Фев. 2, 2023

In the realm of emergence and spread infectious diseases with pandemic potential throughout history, plenty pandemics (and epidemics), from plague to AIDS (1981) SARS (in 2003) bunch COVID variants, have tormented mankind. Though technological innovations are overwhelmingly progressing curb them—a significant number such astounded world, impacting billions lives posing uncovered challenges healthcare organizations clinical pathologists globally. view addressing these limitations, a critically exhaustive review is performed signify prospective role advancements highlight implicit problems associated rendering best quality lifesaving treatments patient community. The proposed work conducted in two parts. Part 1 essentially focused upon discussion advanced technologies akin artificial intelligence, Big Data, block chain technology, open-source cloud computing, etc. Research works governing applicability solving many issues prominently faced by doctors surgeons fields cardiology, medicine, neurology, orthopaedics, paediatrics, gynaecology, psychiatry, plastic surgery, etc., as well their curtailing numerous infectious, pathological, neurotic maladies thrown light off. Boundary conditions implicitly substantiated remedies coupled future directions presented at end.

Язык: Английский

Процитировано

13
Ensemble machine learning framework for predicting maternal health risk during pregnancy DOI Creative Commons
Alaa O. Khadidos, Farrukh Saleem, Shitharth Selvarajan

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 14, 2024

Язык: Английский

Процитировано

4
Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection DOI Creative Commons

Suya Jin,

Guiyan Liu, Qifeng Bai

и другие.

Mathematics, Год журнала: 2023, Номер 11(6), С. 1279 - 1279

Опубликована: Март 7, 2023

Deep learning is a sub-discipline of artificial intelligence that uses neural networks, machine technique, to extract patterns and make predictions from large datasets. In recent years, it has achieved rapid development widely used in numerous disciplines with fruitful results. Learning valuable information complex, high-dimensional, heterogeneous biomedical data key challenge transforming healthcare. this review, we provide an overview emerging deep-learning techniques, COVID-19 research involving deep learning, concrete examples methods diagnosis, prognosis, treatment management. can process medical imaging data, laboratory test results, other relevant diagnose diseases judge disease progression even recommend plans drug-use strategies accelerate drug improve quality. Furthermore, help governments develop proper prevention control measures. We also assess the current limitations challenges therapy precision for COVID-19, including lack phenotypically abundant need more interpretable models. Finally, discuss how barriers be overcome enable future clinical applications learning.

Язык: Английский

Процитировано

9
A Comparative Study of COVID-19 Dynamics in Major Turkish Cities Using Fractional Advection–Diffusion–Reaction Equations DOI Creative Commons
Larissa M. Batrancea,

Dilara Altan Koç,

Ömer Akgüller

и другие.

Fractal and Fractional, Год журнала: 2025, Номер 9(4), С. 201 - 201

Опубликована: Март 25, 2025

Robust epidemiological models are essential for managing COVID-19, especially in diverse urban settings. In this study, we present a fractional advection–diffusion–reaction model to analyze COVID-19 spread three major Turkish cities: Ankara, Istanbul, and Izmir. The employs Caputo-type time-fractional derivative, with its order dynamically determined by the Hurst exponent, capturing memory effects of disease transmission. A nonlinear reaction term self-reinforcing viral spread, while Gaussian forcing simulates public health interventions adjustable spatial temporal parameters. We solve resulting PDE using an implicit finite difference scheme that ensures numerical stability. Calibration weekly case data from February 2021 March 2022 reveals Ankara has exponent 0.4222, Istanbul 0.1932, Izmir 0.6085, indicating varied persistence characteristics. Distribution fitting shows Weibull best represents whereas two-component normal mixture suits Sensitivity analysis confirms key parameters, including duration, critically influence outcomes. These findings provide valuable insights policy planning, offering tailored forecasting tool epidemic management.

Язык: Английский

Процитировано

0
Mathematical Contact Tracing Models for the COVID-19 Pandemic: A Systematic Review of the Literature DOI Open Access
Honoria Ocagli, Gloria Brigiari,

Erica Marcolin

и другие.

Healthcare, Год журнала: 2025, Номер 13(8), С. 935 - 935

Опубликована: Апрель 18, 2025

Background: Contact tracing (CT) is a primary means of controlling infectious diseases, such as coronavirus disease 2019 (COVID-19), especially in the early months pandemic. Objectives: This work systematic review mathematical models used during COVID-19 pandemic that explicitly parameterise CT potential mitigator effects Methods: registered PROSPERO. A comprehensive literature search was conducted using PubMed, EMBASE, Cochrane Library, CINAHL, and Scopus databases. Two reviewers independently selected title/abstract, full text, data extraction, risk bias. Disagreements were resolved through discussion. The characteristics studies collected from each study. Results: total 53 articles out 2101 included. modelling main objective 23 studies, while remaining evaluated forecast transmission COVID-19. Most compartmental to simulate (26, 49.1%), others agent-based (16, 34%), branching processes (5, 9.4%), or other (6). applying consider separate compartment. Quarantine basic reproduction numbers also considered models. quality assessment scores ranged 13 26 28. Conclusions: Despite significant heterogeneity assumptions on relevant model parameters, this provides overview proposed evaluate pandemic, including non-pharmaceutical public health interventions CT. Prospero Registration: CRD42022359060.

Язык: Английский

Процитировано

0
Role of mathematical modeling to assess the dynamical behavior of infectious diseases DOI
Geeta Arora, Tashi Lhamo

AIP conference proceedings, Год журнала: 2025, Номер 3185, С. 020057 - 020057

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Driving Patient Data With AI for Patient Care Path Optimization DOI

Nouhaila Ben Khizzou,

Mourad Aarabe,

Meryem Bouizgar

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 75 - 104

Опубликована: Май 8, 2025

Background: Patient data and artificial intelligence are widely explored concepts for improving healthcare services. However, little is known about the application of lean thinking to quantify these two elements. Objective: The aim this study develop a theoretical framework combining patient intelligence, aimed at optimizing care pathway by identifying theories be mobilized address issue. Methods: A systematic review literature was carried out. Inclusion criteria were that article should focus on patient's pathway. Twenty articles included. Results: proposed can used create an overview potential driving patient-related with AI Further research needed investigate use quantifications data. Conclusions: This paper analyzes in science optimize pathways.

Язык: Английский

Процитировано

0