A framework for reconstructing transmission networks in infectious diseases

Applied Network Science

Research

Rola Hatoum¹  Jihad Touma²  Sara Najem²  Stefano Monni³  Hawraa Sweidan⁴  Nada Ghosn⁴  Hamad Hassan⁵  Chadi Abdallah⁶  Ghaleb Faour⁶ 

[1] Center for Advanced Mathematical Sciences, American University of Beirut, Beirut, Lebanon;Department of Physics, American University of Beirut, Beirut, Lebanon;Center for Advanced Mathematical Sciences, American University of Beirut, Beirut, Lebanon;Department of Physics, American University of Beirut, Beirut, Lebanon;Department of Mathematics, American University of Beirut, Beirut, Lebanon;Epidemiological Surveillance Program, Ministry of Public Health, Beirut, Lebanon;Faculty of Public Health, Lebanese University, Beirut, Lebanon;National Center for Remote Sensing, National Council for Scientific Research (CNRS), Beirut, Lebanon;

关键词: Network reconstruction;    Betweenness centrality;    Autoregressive model;    COVID-19;    Optimal control;   

DOI  :  10.1007/s41109-022-00525-4

 received in 2022-09-06, accepted in 2022-12-12,  发布年份 2022

来源: Springer

【 摘 要 】

In this paper, we propose a general framework for the reconstruction of the underlying cross-regional transmission network contributing to the spread of an infectious disease. We employ an autoregressive model that allows to decompose the mean number of infections into three components that describe: intra-locality infections, inter-locality infections, and infections from other sources such as travelers arriving to a country from abroad. This model is commonly used in the identification of spatiotemporal patterns in seasonal infectious diseases and thus in forecasting infection counts. However, our contribution lies in identifying the inter-locality term as a time-evolving network, and rather than using the model for forecasting, we focus on the network properties without any assumption on seasonality or recurrence of the disease. The topology of the network is then studied to get insight into the disease dynamics. Building on this, and particularly on the centrality of the nodes of the identified network, a strategy for intervention and disease control is devised.

【 授权许可】

CC BY   
© The Author(s) 2022

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