Infectious Disease Containment Based on a Wireless Sensor System

Sun, Xiao; Lu, Zongqing; Zhang, Xiaomei; Salathé, Marcel; Cao, Guohong

doi:10.1109/access.2016.2551199

Cited by 18 publications

(26 citation statements)

References 30 publications

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“…e node "degree centrality" was used to reveal the most active nodes in the network and how well a node is connected with its neighbours-a node degree is the number of edge incidents on a node. e "betweenness centrality" was used to measure how many pairs of nodes a node can be connected to through a shortest path, while the "closeness centrality" was used to measure how contagious an infected patient (a node) is to others [9,17,24,25]. Similarly, "2-reach centrality" was used to explore the proportion of nodes that can reach a given node in 2 steps or less while "eigenvector centrality" was used to measure the importance of a node depending on the importance of its neighbours.…”

Section: Discussionmentioning

confidence: 99%

“…Over the years, researchers have been exploring how the knowledge of network structures could influence public health measures. For example, network analysis based on connectivity centrality was used to identify high-risk people for targeted vaccination in an effort to contain the spread of infectious disease [17]. Network analysis was used to investigate whether the density of the network contacts of persons infected by Mycobacterium tuberculosis was more likely to be tested positive for tuberculosis (TB) compared to the occurrence of TB clusters detected through network connections with clusters detected by molecular genotyping [18].…”

Section: Introductionmentioning

confidence: 99%

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Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Adegboye

Elfaki

2018

Canadian Journal of Infectious Diseases and Medical Microbiolog

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Contact history is crucial during an infectious disease outbreak and vital when seeking to understand and predict the spread of infectious diseases in human populations. The transmission connectivity networks of people infected with highly contagious Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia were assessed to identify super-spreading events among the infected patients between 2012 and 2016. Of the 1379 MERS cases recorded during the study period, 321 (23.3%) cases were linked to hospital infection, out of which 203 (14.7%) cases occurred among healthcare workers. There were 1113 isolated cases while the number of recorded contacts per MERS patient is between 1 (n=210) and 17 (n=1), with a mean of 0.27 (SD = 0.76). Five super-important nodes were identified based on their high number of connected contacts worthy of prioritization (at least degree of 5). The number of secondary cases in each SSE varies (range, 5–17). The eigenvector centrality was significantly (p < 0.05) associated with place of exposure, with hospitals having on average significantly higher eigenvector centrality than other places of exposure. Results suggested that being a healthcare worker has a higher eigenvector centrality score on average than being nonhealthcare workers. Pathogenic droplets are easily transmitted within a confined area of hospitals; therefore, control measures should be put in place to curtail the number of hospital visitors and movements of nonessential staff within the healthcare facility with MERS cases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Adegboye

Elfaki

2018

Canadian Journal of Infectious Diseases and Medical Microbiolog

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“…Gao and Liu [18] focused on the impacts of human behaviors on worm propagation and proposed a two-layer network model to protect large-scale dynamic mobile networks. e short-range worm relies on the direct connections between hardware interfaces and currently tends to attack wireless sensor networks [19][20][21] and vehicular networks [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

“…Equation (20) indicates that when the network changes, we only need to execute the SLP algorithm on the newly added nodes in the local environment and then run the merging program to avoid repetitive computation of the existing results. Figure 5 shows a three-step example of the SLP process.…”

Section: Online Community Managermentioning

confidence: 99%

CC²: Defending Hybrid Worm on Mobile Networks with Two‐Dimensional Circulation Control

et al. 2019

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As the hybrid worm can propagate by both personal social interactions and wireless communications, it has been identified as one of the most severe threats to the mobile Internet. This problem is expected to become worse with the boom of social applications and mobile services. In this work, we study the propagation dynamics of hybrid worms and propose a systematic countermeasure. The system maintains a set of community structure which describes the high-speed infection zone of worms and contains worm propagation by distributing the worm signature to the guard nodes selected from the periphery of each community. For those nodes that are geographically close but located in different communities , we evaluate the communication security between them based on the observed infection history and limit communications between insecure ones to avoid the worm spreading across communities. We also design an efficient worm signature forwarding strategy that enables most nodes in the network to reach an immune state before being infected by the worm. Extensive real-trace driven simulations verify the feasibility and effectiveness of the proposed methods.

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“…The method used in [4], [5] is based on the analysis of the eigenvalues of the community graph matrix to vaccinate the nodes that maximize these eigenvalues. The authors of [6] have collected contacts between people using sensors and then modeled them by a graph. Suitable nodes for vaccination are then selected using the centrality metrics.…”

Section: Introductionmentioning

confidence: 99%

Targeted Vaccination for COVID-19 Using Mobile Communication Networks

Jadidi

Moslemi

Jamshidiha

et al. 2020

2020 11th International Conference on Information and Knowledge Technology (IKT)

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Vaccination is an effective method for prevention of infectious diseases, but when the number of available vaccines is limited, it is not possible to vaccinate everyone in a society. In this paper, a two-step model is proposed to distribute a limited number of vaccines among the people of a society, in a way that would disrupt the transmission chain of the infectious disease most efficiently. In the first step, the vaccines are allocated to different communities in the society (e.g. cities in a country), and in the second step, the individuals whose vaccination removes the greatest number of transmission routes for the infection are identified in concordance with the regulations of international health organizations. In the second step, contact data is obtained from cellular networks and Bluetooth signals, and a graph-based modeling scheme is utilized in conjunction with a combined susceptibility metric specifically designed for selection of these individuals. The simulations indicate that a 30% drop in infection rate compared to random vaccination could be achieved.

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Infectious Disease Containment Based on a Wireless Sensor System

Cited by 18 publications

References 30 publications

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

CC²: Defending Hybrid Worm on Mobile Networks with Two‐Dimensional Circulation Control

Targeted Vaccination for COVID-19 Using Mobile Communication Networks

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Infectious Disease Containment Based on a Wireless Sensor System

Cited by 18 publications

References 30 publications

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

Network Analysis of MERS Coronavirus within Households, Communities, and Hospitals to Identify Most Centralized and Super-Spreading in the Arabian Peninsula, 2012 to 2016

CC2: Defending Hybrid Worm on Mobile Networks with Two‐Dimensional Circulation Control

Targeted Vaccination for COVID-19 Using Mobile Communication Networks

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Product

Resources

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CC²: Defending Hybrid Worm on Mobile Networks with Two‐Dimensional Circulation Control