Predicting human contacts through alternating direction method of multipliers

Chunlin Huang () and Dongbo Bu
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Chunlin Huang: National Computer Network Emergency Response, Technical Team/Coordination Center of China, Beijing 100029, P. R. China
Dongbo Bu: Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, P. R. China3University of Chinese Academy of Sciences, Beijing 100049, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 07, 1-18

Abstract: Transmission of respiratory infectious diseases depends greatly on human close-proximity contacts, making thorough understanding of current and upcoming contacts essential for epidemic containment. Although different devices and software have been developed for contact data collection, there are few effective methods for contact prediction available in the near future as far as the authors know. In this study, we propose an approach to predict human contacts. We first extract human features together with their significances from the human contacts through alternating direction method of multipliers (ADMM), then predict future significances based on periodicity of contacts, and finally construct future contacts from human features and future significances. With the help of contact data collected in a Chinese University, we compare this approach with a trivial method of directly averaging known contacts. The comparison shows that our approach generates contacts deviating less from the true ones.

Keywords: Human contact prediction; alternating direction method of multipliers; periodicity (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S012918311940014X

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