Predicting Long-term Covid-19 Symptoms Using Machine Learning: A Case Study in Kurdistan Region of Iraq DOI Open Access

Aveen Kakamen Mustafa,

Ibrahim Ismael Hamarash

The Journal of The University of Duhok, Journal Year: 2023, Volume and Issue: 26(2), P. 605 - 612

Published: Dec. 21, 2023

The COVID-19 pandemic has introduced substantial challenges to individuals, communities, and healthcare systems worldwide. While initial responses primarily addressed the acute impact of virus, emerging evidence highlights a noteworthy portion individuals grappling with persistent symptoms even after recuperating from phase. This research delves into domain algorithms their application context COVID-19. Specifically, we employ Machine Learning (ML) techniques formulate robust model for assessing likelihood enduring long-term among in recovery Our investigation revolves around comprehensive dataset drawn 3,500 patients residing Kurdistan Region Iraq, all whom had previously contracted Employing combination hospital records direct/mobile interviews, systematically capture information pertaining six prevalent symptoms. Rigorous preprocessing are then applied collected data, ensuring standardization mitigating any inherent inconsistencies or biases. To achieve our objective, harness capabilities TensorFlow Keras libraries, leveraging deep learning algorithm. algorithm plays pivotal role predicting probability sustained recovered patients. endeavor demonstrates potential learning, especially when harnessed within well-structured coupled adept methodologies. Consequently, findings underscore viability utilizing as potent tools forecasting propensity symptom manifestation diagnosed

Language: Английский

Control technology of pathogenic biological aerosol: Review and prospect DOI
Hongbin Zhao, Xiangru Kong, Wanxiang Yao

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 243, P. 110679 - 110679

Published: July 31, 2023

Language: Английский

Citations

15
Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021 DOI Creative Commons
Seonghye Jeon,

Lydia Watson-Lewis,

Gabriel Rainisch

et al.

Emerging infectious diseases, Journal Year: 2024, Volume and Issue: 30(2)

Published: Jan. 5, 2024

Because of constrained personnel time, the Philadelphia Department Public Health (Philadelphia, PA, USA) adjusted its COVID-19 contact tracing protocol in summer 2021 by prioritizing recent cases and limiting staff time per case. This action reduced required hours to prevent each case from 21-30 8-11 hours, while maintaining program effectiveness.

Language: Английский

Citations

4
Use Tabu Search Particle Swarm Optimization Algorithm to Detect COVID-19 DOI
Shuwen Chen, Jiaji Wang, Huisheng Zhu

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Home-Based Testing and COVID-19 Isolation Recommendations, United States DOI Creative Commons
Patrick K. Moonan, Jonathan P. Smith, Brian F. Borah

et al.

Emerging infectious diseases, Journal Year: 2023, Volume and Issue: 29(9), P. 1921 - 1924

Published: Aug. 29, 2023

Using a nationally representative panel survey, we examined isolation behaviors among persons in the United States who had positive SARS-CoV-2 test results during January 2021-March 2022. Compared with received provider-administered results, home-based 29% (95% CI 5%-47%) lower odds of following recommendations.

Language: Английский

Citations

5
Cross-national benchmarking and acceptance of pandemic mitigation policies: human value approach DOI
Purnendu Mandal, Kallol Bagchi,

Godwin Udo

et al.

Benchmarking An International Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

Purpose This study analyzes the reasons for satisfaction or dissatisfaction among people with public health mitigation policies, particularly focus on human values. Recent studies reveal that citizenry of various nations reacted to government policy measures differently when asked if they are satisfied handling COVID-19. Human values such as openness-to-change and conservation might influence acceptance pandemic measures. Design/methodology/approach A structural equation model (SEM) is proposed, which incorporates strategies value variables. National survey data COVID-19 in Great Britain Italy used test several hypotheses. Findings The analysis suggests prioritizing health, monitoring tracking people, border closures restricting people’s movement played important roles pandemic. Individuals a high more likely have higher probability government’s During pandemic, citizens willing trade good economy health. They also sacrifice privacy efforts track spread. Originality/value unique combines variables policies determining during national crisis. SEM modeling framework presented analyze empirically

Language: Английский

Citations

0
Home-Based Testing and COVID-19 Isolation Recommendations, United States DOI Creative Commons
Patrick K. Moonan, Jonathan P. Smith, Brian F. Borah

et al.

Emerging infectious diseases, Journal Year: 2023, Volume and Issue: 29(9)

Published: Aug. 24, 2023

Abstract Using a nationally representative panel survey, we examined isolation behaviors among persons in the United States who had positive SARS-CoV-2 test results during January 2021–March 2022. Compared with received provider-administered results, home-based 29% (95% CI 5%–47%) lower odds of following recommendations.

Language: Английский

Citations

0
DP-UNet:Dual Branch Attention Multi-Layer Encoder and Progressive Fused Pyramid Pooling Network for Covid-19 Infection Region Segmentation DOI
Qi Mao, Wenfeng Wang,

Yi Tian

et al.

Published: Jan. 1, 2023

Computer-aided diagnostic imaging plays a crucial role in diagnosis of the Corona Virus Disease 2019 (COVID-19) infection. U-Net is popular COVID-19 segmentation, but during encoder pooling and decoder upsampling operations, it tends to lose global contextual information, which leads semantic gap between encoding decoding stages. To solve these problems, novel model using dual branch attention multi-layer progressive fusion pyramid network (DP-UNet) proposed developed this work. The module fully utilizes enough information from input lung infection images through extraction operations. Its lateral comprises an enhanced Parallel concurrent spatial channel Squeeze Excitation (PscSE), designed for recalibrating attention. At interface decoder, we propose module. This multi-scale continuous operations expand utilization by integrating different scales. It aims increase ability finely delineate boundaries lesions facilitate integration various-scale details within infected regions while minimizing addition parameters. experimental results revealed that method had DSC 0.8459, indicating outperforms other comparative models on region segmentation. Therefore, has potential application detection, labeling segmentation lesion areas.

Language: Английский

Citations

0
Predicting Long-term Covid-19 Symptoms Using Machine Learning: A Case Study in Kurdistan Region of Iraq DOI Open Access

Aveen Kakamen Mustafa,

Ibrahim Ismael Hamarash

The Journal of The University of Duhok, Journal Year: 2023, Volume and Issue: 26(2), P. 605 - 612

Published: Dec. 21, 2023

The COVID-19 pandemic has introduced substantial challenges to individuals, communities, and healthcare systems worldwide. While initial responses primarily addressed the acute impact of virus, emerging evidence highlights a noteworthy portion individuals grappling with persistent symptoms even after recuperating from phase. This research delves into domain algorithms their application context COVID-19. Specifically, we employ Machine Learning (ML) techniques formulate robust model for assessing likelihood enduring long-term among in recovery Our investigation revolves around comprehensive dataset drawn 3,500 patients residing Kurdistan Region Iraq, all whom had previously contracted Employing combination hospital records direct/mobile interviews, systematically capture information pertaining six prevalent symptoms. Rigorous preprocessing are then applied collected data, ensuring standardization mitigating any inherent inconsistencies or biases. To achieve our objective, harness capabilities TensorFlow Keras libraries, leveraging deep learning algorithm. algorithm plays pivotal role predicting probability sustained recovered patients. endeavor demonstrates potential learning, especially when harnessed within well-structured coupled adept methodologies. Consequently, findings underscore viability utilizing as potent tools forecasting propensity symptom manifestation diagnosed

Language: Английский

Citations

0