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 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. • Introduce a Risk of Infection (ROI) evaluation model for automated digital contact tracing during pandemics. • Utilize logistic functions, k-means clustering, and Hidden Markov Models to assess ROI based on distance, overlap time, contamination interval, incubation time, and contact facility size. • Train the model on 24,000 simulated scenarios and test it on an additional 16,000 to ensure effectiveness in risk classification. • Differentiate simulated scenarios into high, medium, and low-risk categories. K-means classified 25% as high-risk, 30.7% as medium-risk, and 44.3% as low-risk. The Hidden Markov Model classified 15.5% as high-risk, 20.7% as medium-risk, and 63.8% as low-risk. • Enhance the efficiency of public health authorities by targeting interventions towards individuals at the highest risk of infection.