A machine learning model for automated contact tracing during disease outbreaks
This study aims to develop and evaluate a conceptual model for assessing the Risk of Infection (ROI) within the context of automated digital contact tracing during pandemics. The proposed model incorporates five input parameters: distance, overlap time, contamination interval, incubation time, and c...
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Elsevier
2025-06-01
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| Series: | Healthcare Analytics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442525000085 |
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| author | Zeyad Aklah Amean Al-Safi Marwa H. Abdali Khalid Al-jabery |
| author_facet | Zeyad Aklah Amean Al-Safi Marwa H. Abdali Khalid Al-jabery |
| author_sort | Zeyad Aklah |
| collection | DOAJ |
| description | This study aims to develop and evaluate a conceptual model for assessing the Risk of Infection (ROI) within the context of automated digital contact tracing during pandemics. The proposed model incorporates five input parameters: distance, overlap time, contamination interval, incubation time, and contact facility size. These parameters capture various aspects of disease transmission dynamics. The model employs logistic functions to quantify the influence of each parameter on the overall ROI. The evaluation of the model involves two methods: a partial evaluation to observe the impact of parameter pairs on ROI, and a full evaluation, which is trained on a dataset of 24,000 simulated scenarios to identify central clusters for high, medium, and low-risk categories using K-means and the Hidden Markov Model. Additionally, the model is tested on another 16,000 simulated scenarios to assess its overall performance. Results indicate that the Hidden Markov Model categorizes 63.8% of the testing dataset as low risk, 20.7% as medium risk, and 15.5% as high risk. In contrast, K-means classifies 44.3% as low risk, 30.7% as medium risk, and 25% as high risk. The evaluation metrics favor the Hidden Markov Model, which demonstrates higher performance in terms of Log-Likelihood, with a value of 50,688, as well as in the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), with values of -101,365.6430 and -101,319.5609, respectively. In both evaluations, the results validate the model’s ability to automate digital contact tracing based on the input parameters. Future studies could explore classification accuracy using real contact tracing datasets. The proposed approach enhances the efficiency of public health authorities by directing their efforts toward individuals with the highest risk of infection, rather than applying the same level of intervention indiscriminately to everyone. |
| format | Article |
| id | doaj-art-849b8bbdb8aa409f8e8363ef2ecf4d0b |
| institution | OA Journals |
| issn | 2772-4425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj-art-849b8bbdb8aa409f8e8363ef2ecf4d0b2025-08-20T02:35:57ZengElsevierHealthcare Analytics2772-44252025-06-01710038910.1016/j.health.2025.100389A machine learning model for automated contact tracing during disease outbreaksZeyad Aklah0Amean Al-Safi1Marwa H. Abdali2Khalid Al-jabery3Department of Information Technology , University of Thi-Qar, Nassiriya, 64001, Thi-Qar, Iraq; Corresponding author.Department of Electrical and Electronics Engineering, University of Thi-Qar, Nassiriya, 64001, Thi-Qar, IraqDepartment of Information Technology , University of Thi-Qar, Nassiriya, 64001, Thi-Qar, IraqBasrah Oil Company, Basrah, 61001, Basrah, IraqThis study aims to develop and evaluate a conceptual model for assessing the Risk of Infection (ROI) within the context of automated digital contact tracing during pandemics. The proposed model incorporates five input parameters: distance, overlap time, contamination interval, incubation time, and contact facility size. These parameters capture various aspects of disease transmission dynamics. The model employs logistic functions to quantify the influence of each parameter on the overall ROI. The evaluation of the model involves two methods: a partial evaluation to observe the impact of parameter pairs on ROI, and a full evaluation, which is trained on a dataset of 24,000 simulated scenarios to identify central clusters for high, medium, and low-risk categories using K-means and the Hidden Markov Model. Additionally, the model is tested on another 16,000 simulated scenarios to assess its overall performance. Results indicate that the Hidden Markov Model categorizes 63.8% of the testing dataset as low risk, 20.7% as medium risk, and 15.5% as high risk. In contrast, K-means classifies 44.3% as low risk, 30.7% as medium risk, and 25% as high risk. The evaluation metrics favor the Hidden Markov Model, which demonstrates higher performance in terms of Log-Likelihood, with a value of 50,688, as well as in the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), with values of -101,365.6430 and -101,319.5609, respectively. In both evaluations, the results validate the model’s ability to automate digital contact tracing based on the input parameters. Future studies could explore classification accuracy using real contact tracing datasets. The proposed approach enhances the efficiency of public health authorities by directing their efforts toward individuals with the highest risk of infection, rather than applying the same level of intervention indiscriminately to everyone.http://www.sciencedirect.com/science/article/pii/S2772442525000085Disease outbreaksContact tracingk-meansSigmoid functionLogistic functions |
| spellingShingle | Zeyad Aklah Amean Al-Safi Marwa H. Abdali Khalid Al-jabery A machine learning model for automated contact tracing during disease outbreaks Healthcare Analytics Disease outbreaks Contact tracing k-means Sigmoid function Logistic functions |
| title | A machine learning model for automated contact tracing during disease outbreaks |
| title_full | A machine learning model for automated contact tracing during disease outbreaks |
| title_fullStr | A machine learning model for automated contact tracing during disease outbreaks |
| title_full_unstemmed | A machine learning model for automated contact tracing during disease outbreaks |
| title_short | A machine learning model for automated contact tracing during disease outbreaks |
| title_sort | machine learning model for automated contact tracing during disease outbreaks |
| topic | Disease outbreaks Contact tracing k-means Sigmoid function Logistic functions |
| url | http://www.sciencedirect.com/science/article/pii/S2772442525000085 |
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