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Prediction System-Based Community Partition for Tuberculosis Outbreak Spread

Prediction System-Based Community Partition for Tuberculosis Outbreak Spread

Fatima-Zohra Younsi, Djamila Hamdadou
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 20
ISSN: 1935-570X|EISSN: 1935-5718|EISBN13: 9781683180289|DOI: 10.4018/IJITSA.289998
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MLA

Younsi, Fatima-Zohra, and Djamila Hamdadou. "Prediction System-Based Community Partition for Tuberculosis Outbreak Spread." IJITSA vol.15, no.1 2022: pp.1-20. http://doi.org/10.4018/IJITSA.289998

APA

Younsi, F. & Hamdadou, D. (2022). Prediction System-Based Community Partition for Tuberculosis Outbreak Spread. International Journal of Information Technologies and Systems Approach (IJITSA), 15(1), 1-20. http://doi.org/10.4018/IJITSA.289998

Chicago

Younsi, Fatima-Zohra, and Djamila Hamdadou. "Prediction System-Based Community Partition for Tuberculosis Outbreak Spread," International Journal of Information Technologies and Systems Approach (IJITSA) 15, no.1: 1-20. http://doi.org/10.4018/IJITSA.289998

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Abstract

In this work, our goal is to design and investigate a new simulation system based on detection communities for control and prediction of TB outbreak. The latter is mainly based on four subsystems, namely: Susceptible-Infected-Removed (SIR) system, detection community system, visualization system and prediction system. The SIR including reservoir within Small World (SW) network system is applied to take better advantage of its modeling property and understanding epidemic spread. In order to characterize the influence of communities’ structure, we use Louvain method to identify communities in human complex network. Then, we propose a predictive approach for identifying the hottest outbreak communities based on communities’ detection, as well as mapping areas at risk. Current results show the performance of the proposed system and the important role of detection communities in the process of epidemic spreading and prediction.

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