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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Social interaction layers in complex networks for the dynamical epidemic modeling of COVID-19 in Brazil

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Scabini, Leonardo F. S. [1] ; Ribas, Lucas C. [2] ; Neiva, Mariane B. [2] ; Junior, Altamir G. B. [1] ; Farfan, Alex J. F. [2] ; Bruno, Odemir M. [1, 2]
Total Authors: 6
[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, POB 369, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, USP, Ave Trabalhador Sao Carlense 400, BR-13566590 Sao Carlos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Web of Science Citations: 0

We are currently living in a state of uncertainty due to the pandemic caused by the SARS-CoV-2 virus. There are several factors involved in the epidemic spreading, such as the individual characteristics of each city/country. The true shape of the epidemic dynamics is a large, complex system, considerably hard to predict. In this context, Complex networks are a great candidate for analyzing these systems due to their ability to tackle structural and dynamic properties. Therefore, this study presents a new approach to model the COVID-19 epidemic using a multi-layer complex network, where nodes represent people, edges are social contacts, and layers represent different social activities. The model improves the traditional SIR, and it is applied to study the Brazilian epidemic considering data up to 05/26/2020, and analyzing possible future actions and their consequences. The network is characterized using statistics of infection, death, and hospitalization time. To simulate isolation, social distancing, or precautionary measures, we remove layers and reduce social contact's intensity. Results show that even taking various optimistic assumptions, the current isolation levels in Brazil still may lead to a critical scenario for the healthcare system and a considerable death toll (average of 149,000). If all activities return to normal, the epidemic growth may suffer a steep increase, and the demand for ICU beds may surpass three times the country's capacity. This situation would surely lead to a catastrophic scenario, as our estimation reaches an average of 212,000 deaths, even considering that all cases are effectively treated. The increase of isolation (up to a lockdown) shows to be the best option to keep the situation under the healthcare system capacity, aside from ensuring a faster decrease of new case occurrences (months of difference), and a significantly smaller death toll (average of 87,000). (C) 2020 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 16/18809-9 - Deep learning and complex networks applied to computer vision
Grantee:Odemir Martinez Bruno
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support Opportunities: Regular Research Grants
FAPESP's process: 19/07811-0 - Artificial neural networks and complex networks: an integrative study of topological properties and pattern recognition
Grantee:Leonardo Felipe dos Santos Scabini
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
Grantee:Lucas Correia Ribas
Support Opportunities: Scholarships in Brazil - Doctorate