A Literature Review of Social Network Analysis in Epidemic Prevention and Control

Wang, Xiaomei; An, Qi; Zhang, He; Fang, Wei

doi:10.1155/2021/3816221

Cited by 13 publications

(10 citation statements)

References 137 publications

(253 reference statements)

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“…The scale-free nature of online social networks is welldocumented [6,8,26] and suggest that the degree distribution of nodes within the network can be described using power law distributions [1,4,33]. We use this, together with previous work modelling initially on heterogeneous contact rates for infectious diseases (Liu 1986 etc) and subsequently considering epidemic spread on a social network [15,31,32] to describe the per capita victimisation rate in the form…”

Section: Internal Victimisation Processmentioning

confidence: 99%

Exploring the impact of how criminals interact with cyber-networks - a mathematical modeling approach

Chikore

Nyirenda-Kayuni

Chukwudum

et al. 2022

Preprint

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There is a growing interest in using mathematical models to understand crime dynamics, crime prevention, and detection. The past decade has experienced a relative reduction in conventional crimes, but this has been replaced by significant increases in cybercrime. We use a system of nonlinear differential equations to explore the impact of criminal location in relation to cyber networks on the amount of cybercrime. Using steady-state analysis and extensive numerical simulations, we find that the location of criminals relative to the network does not impact the system qualitatively although there are quantitative differences. Cyber networks that are more clustered are likely to experience greater levels of cybercrime but there is also a saturation effect that limits the level of victimisation as the number of criminals attempting to undertake crimes on a given network increases.

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Section: Internal Victimisation Processmentioning

confidence: 99%

Exploring the impact of how criminals interact with cyber-networks - a mathematical modeling approach

Chikore

Nyirenda-Kayuni

Chukwudum

et al. 2022

Preprint

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“…The structural and evolutionary characteristics of social networks are great significance for the assessment, control, monitoring, and prevention of epidemic diseases, and the migration of population dramatically increases the risk of COVID-19 infected and epidemic spread ( Hu et al, 2021 ), so we should adopt multiple combination strategies for epidemic prevention and control under population mobility and COVID-19 evolution, such as epidemic warning, control edge, isolation, etc. ( Wang et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Game Analysis on Epidemic Prevention and Resuming Production: Based on China’s Experience With COVID-19

Zhuang

Jiang

et al. 2021

Front. Psychol.

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Since the outbreak of COVID-19, it became urgent to deal with the relationship between the prevention and control of the epidemic and the resumption of work and production. The purpose of this study is to observe and describe which approach seemed more important for the Chinese government and people, and how this trend evolved through time. To this end, a game model of resuming production and preventing the epidemic is constructed, using the evolutionary stable strategy (ESS). By combing China’s measures on epidemic prevention and resuming production during critical periods of epidemic outbreak, it is clarified that the present stage is considered a period of equal emphasis on both epidemic prevention and resuming production. Based on the dynamic between these two strategies and further theoretical research, present policies should equally focus on both preventive and controlling measures as well as on the socioeconomic development for most countries in the world.

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“…The existing contact network studies lack the support of real-world disease transmission data and fail to show the dynamic evolution of disease spread. To an extent, it reduces the explanatory power of the study on the transmission of diseases [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…The contact network is therefore a duplication of the virus transmission network, which can reflect the disease transmission process at individual level [ 6 ]. Meanwhile, the relevant indicators of contact network can be studied by means of social network analysis [ 25 ]. In addition, cases published during an outbreak are an important source of real-world data for studying disease outbreak and spread.…”

Section: Introductionmentioning

confidence: 99%

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

Yang

Zhang

Gao

et al. 2022

IJERPH

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The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

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A Literature Review of Social Network Analysis in Epidemic Prevention and Control

Cited by 13 publications

References 137 publications

Exploring the impact of how criminals interact with cyber-networks - a mathematical modeling approach

Exploring the impact of how criminals interact with cyber-networks - a mathematical modeling approach

Game Analysis on Epidemic Prevention and Resuming Production: Based on China’s Experience With COVID-19

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

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