Joint effect of individual’s memory and attractiveness in temporal network on spreading dynamics

2019 ◽  
Vol 30 (01) ◽  
pp. 1950011 ◽  
Author(s):  
Mei Yang ◽  
Bing Wang ◽  
Yuexing Han
Keyword(s):  
Network Model ◽  
Joint Effect ◽  
Network Evolution ◽  
Disease Spread ◽  
Temporal Network ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Modeling Framework ◽  
Positive Correlations ◽  
The Impact

Most real networks have been found to evolve dynamically. In addition, heterogeneity of interaction strengths and individual’s memory have been proved to be important to affect network evolution in the area of complex networks. Based on a recent extension of the activity-driven modeling framework, i.e. activity-driven model with attractiveness, where individuals are characterized by their activities and attractiveness, we propose a new temporal network model by considering individuals’ memory. Similar to the previous network model, we also investigate heterogeneous distributions and different correlations between the two variables (activity and attractiveness). By numerical simulations, we illustrate the impact of memory on epidemic spreading processes unfolding on the proposed model. We find that, when individuals’ attractiveness is fixed, memory does not cause dramatic change on the epidemic threshold in the SIR process, but it inhibits the epidemic spreading by reducing the infected ratio. While for the SIS process, in the case of uncorrelated and positive correlations between the two variables, memory facilitates the epidemic outbreaks by shrinking the epidemic threshold. When the two variables are negatively correlated, memory does not take obvious impact on the epidemic threshold. Besides, for the two processes unfolding on the network with and without memory, the propagation threshold decreases with the enhancement of the positive correlation, however, it almost does not get much influence from the negative correlation strength. It indicates that for the individuals with same activity, more attractive individuals will promote the disease spread. The findings deepen our understanding of the role of individuals’ memory and their attractiveness in the epidemic spreading process.

Epidemic spreading on evolving networks

2019 ◽  
Vol 33 (23) ◽  
pp. 1950266 ◽  
Author(s):  
Jin-Xuan Yang
Keyword(s):  
Preferential Attachment ◽  
Small World ◽  
Network Evolution ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Scale Free ◽  
Evolving Networks ◽  
Power Law Exponent ◽  
Free Network ◽  
Over Time

Network structure will evolve over time, which will lead to changes in the spread of the epidemic. In this work, a network evolution model based on the principle of preferential attachment is proposed. The network will evolve into a scale-free network with a power-law exponent between 2 and 3 by our model, where the exponent is determined by the evolution parameters. We analyze the epidemic spreading process as the network evolves from a small-world one to a scale-free one, including the changes in epidemic threshold over time. The condition of epidemic threshold to increase is given with the evolution processes. The simulated results of real-world networks and synthetic networks show that as the network evolves at a low evolution rate, it is more conducive to preventing epidemic spreading.

Impacts of multitype interactions on epidemic spreading in temporal networks

2019 ◽  
Vol 31 (01) ◽  
pp. 2050020
Author(s):  
NingNing Dong ◽  
YueXing Han ◽  
Qing Li ◽  
Bing Wang
Keyword(s):  
Network Model ◽  
Network Structure ◽  
Transmission Rate ◽  
Mutual Effect ◽  
Observation Time ◽  
Dynamical Process ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Immunization Strategy ◽  
Immunization Strategies

Individuals have often been found to interact with each other with different intensity in a dynamical way due to their various types in real networks, which plays a fundamental role in dynamical process such as epidemic spreading. To understand the relationship between the network structure and the spreading process, we propose a kind of temporal network model which contains diverse types of individuals. Furthermore, we also assume that the transmission rate is also related to the individuals’ types. Theoretical analysis and numerical results show that the epidemic threshold is affected by several factors, such as parameters described network structure and the ratio of intra-transmission rate to inter-transmission rate. Finally, we investigate immunization strategies for the network model and propose an immunization strategy by considering the mutual effect of individual’s degree connected with the same type and those with different types. By comparing a kind of immunization strategies, we find that the proposed immunization strategy is able to suppress the outbreak with less observation time and that it is able to suppress the outbreak almost as efficient as the target immunization strategy with appropriate observation time.

scholarly journals A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2

2021 ◽  
Vol 17 (6) ◽  
pp. e1009058
Author(s):  
Edward M. Hill ◽  
Benjamin D. Atkins ◽  
Matt J. Keeling ◽  
Louise Dyson ◽  
Michael J. Tildesley
Keyword(s):  
Network Model ◽  
Significant Proportion ◽  
Disease Spread ◽  
Contact Tracing ◽  
Work Patterns ◽  
General Role ◽  
Work From Home ◽  
Working Practices ◽  
The Impact

As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated. We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider the risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home; using temporally asynchronous work patterns; and introducing measures to create ‘COVID-secure’ workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics. The progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk reduced the probability of large, prolonged outbreaks. Finally, following isolation guidance and engaging with contact tracing without other measures is an effective tool to curb transmission, but is highly sensitive to adherence levels. In the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.

scholarly journals Effective approach to epidemic containment using link equations in complex networks

2018 ◽  
Vol 4 (12) ◽  
pp. eaau4212 ◽  
Author(s):  
Joan T. Matamalas ◽  
Alex Arenas ◽  
Sergio Gómez
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Accurate Determination ◽  
Air Transportation ◽  
Disease Spread ◽  
Epidemic Spreading ◽  
Critical Thresholds ◽  
Full Structure ◽  
The Impact

Epidemic containment is a major concern when confronting large-scale infections in complex networks. Many studies have been devoted to analytically understand how to restructure the network to minimize the impact of major outbreaks of infections at large scale. In many cases, the strategies are based on isolating certain nodes, while less attention has been paid to interventions on the links. In epidemic spreading, links inform about the probability of carrying the contagion of the disease from infected to susceptible individuals. Note that these states depend on the full structure of the network, and its determination is not straightforward from the knowledge of nodes’ states. Here, we confront this challenge and propose a set of discrete-time governing equations that can be closed and analyzed, assessing the contribution of links to spreading processes in complex networks. Our approach allows a scheme for the containment of epidemics based on deactivating the most important links in transmitting the disease. The model is validated in synthetic and real networks, yielding an accurate determination of epidemic incidence and critical thresholds. Epidemic containment based on link deactivation promises to be an effective tool to maintain functionality of networks while controlling the spread of diseases, such as disease spread through air transportation networks.

scholarly journals Epidemic Spreading with Heterogeneous Awareness on Human Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Yanling Lu ◽  
Guoping Jiang ◽  
Zhengxin Wang
Keyword(s):  
Theoretical Analysis ◽  
Epidemic Model ◽  
Arrival Time ◽  
Behavioral Responses ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Awareness Information ◽  
The Impact ◽  
Human Networks

The spontaneous awareness behavioral responses of individuals have a significant impact on epidemic spreading. In this paper, a modified Susceptible-Alert-Infected-Susceptible (SAIS) epidemic model with heterogeneous awareness is presented to study epidemic spreading in human networks and the impact of heterogeneous awareness on epidemic dynamics. In this model, when susceptible individuals receive awareness information about the presence of epidemic from their infected neighbor nodes, they will become alert individuals with heterogeneous awareness rate. Theoretical analysis and numerical simulations show that heterogeneous awareness can enhance the epidemic threshold with certain conditions and reduce the scale of virus outbreaks compared with no awareness. What is more, for the same awareness parameter, it also shows that heterogeneous awareness can slow effectively the spreading size and does not delay the arrival time of epidemic spreading peak compared with homogeneous awareness.

scholarly journals The Impact of the Global and Local Awareness Diffusion on Epidemic Transmission Considering the Heterogeneity of Individual Influences

2022 ◽  
Author(s):  
Haidong Xu ◽  
Ye Zhao ◽  
Dun Han
Keyword(s):  
Mean Field ◽  
Global Awareness ◽  
Protective Behavior ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Spreading Model ◽  
Information Layer ◽  
Local Awareness ◽  
Global And Local ◽  
The Impact

Abstract In this paper, we propose a coupled awareness - epidemic spreading model considering the heterogeneity of individual influences, which aims to explore the interaction between awareness diffusion and epidemic transmission. The considered heterogeneity of individual influences are threefold: the heterogeneity of individual influences in the information layer, the heterogeneity of individual influences in the epidemic layer and the heterogeneity of individual behavioral responses to epidemics. In addition, the individuals' receptive preference for information and the impacts of individuals' perceived local awareness ratio and individuals' perceived epidemic severity on self-protective behavior are included. The epidemic threshold is theoretically established according to the microscopic Markov chain approach and mean-field approach. Results indicate that the critical local and global awareness ratios have two-stage effects on the epidemic threshold. Besides, either the heterogeneity of individual influences in the information layer or the strength of individuals' responses to epidemics can influence the epidemic threshold with a nonlinear way. However, the heterogeneity of individual influences in the epidemic layer has few effect on the epidemic threshold, but can affects the magnitude of the final infected density.

scholarly journals A network modelling approach to assess non-pharmaceutical disease controls in a worker population: An application to SARS-CoV-2

2020 ◽  
Author(s):  
Edward M Hill ◽  
Benjamin D Atkins ◽  
Matt J Keeling ◽  
Louise Dyson ◽  
Michael J Tildesley
Keyword(s):  
Network Model ◽  
Significant Proportion ◽  
Disease Spread ◽  
Contact Tracing ◽  
Work Patterns ◽  
General Role ◽  
Work From Home ◽  
Working Practices ◽  
The Impact

Background: As part of a concerted pandemic response to protect public health, businesses can enact non-pharmaceutical controls to minimise exposure to pathogens in workplaces and premises open to the public. Amendments to working practices can lead to the amount, duration and/or proximity of interactions being changed, ultimately altering the dynamics of disease spread. These modifications could be specific to the type of business being operated. Methods: We use a data-driven approach to parameterise an individual-based network model for transmission of SARS-CoV-2 amongst the working population, stratified into work sectors. The network is comprised of layered contacts to consider risk of spread in multiple encounter settings (workplaces, households, social and other). We analyse several interventions targeted towards working practices: mandating a fraction of the population to work from home, using temporally asynchronous work patterns and introducing measures to create `COVID-secure' workplaces. We also assess the general role of adherence to (or effectiveness of) isolation and test and trace measures and demonstrate the impact of all these interventions across a variety of relevant metrics. Results: The progress of the epidemic can be significantly hindered by instructing a significant proportion of the workforce to work from home. Furthermore, if required to be present at the workplace, asynchronous work patterns can help to reduce infections when compared with scenarios where all workers work on the same days, particularly for longer working weeks. When assessing COVID-secure workplace measures, we found that smaller work teams and a greater reduction in transmission risk led to a flatter temporal profile for both infections and the number of people isolating, and reduced the probability of large, long outbreaks. Finally, following isolation guidance and engaging with contact tracing alone is an effective tool to curb transmission, but is highly sensitive to adherence levels. Conclusions: In the absence of sufficient adherence to non-pharmaceutical interventions, our results indicate a high likelihood of SARS-CoV-2 spreading widely throughout a worker population. Given the heterogeneity of demographic attributes across worker roles, in addition to the individual nature of controls such as contact tracing, we demonstrate the utility of a network model approach to investigate workplace-targeted intervention strategies and the role of test, trace and isolation in tackling disease spread.

scholarly journals Self-Awareness-Based Resource Allocation Strategy for Containment of Epidemic Spreading

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaolong Chen ◽  
Quanhui Liu ◽  
Ruijie Wang ◽  
Qing Li ◽  
Wei Wang
Keyword(s):  
Resource Allocation ◽  
Network Structure ◽  
Coupled Model ◽  
Tuning Parameter ◽  
Self Awareness ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Optimal Point ◽  
Allocation Model ◽  
The Impact

Resource support between individuals is of particular importance in controlling or mitigating epidemic spreading, especially during pandemics. However, there remains the question of how we can protect ourselves from being infected while helping others by donating resources in fighting against the epidemic. To answer the question, we propose a novel resource allocation model by considering the awareness of self-protection of individuals. In the model, a tuning parameter is introduced to quantify the reaction strength of individuals when they are aware of the disease. And then, a coupled model of resource allocation and disease spreading is proposed to study the impact of self-awareness on resource allocation and its impact on the dynamics of epidemic spreading. Through theoretical analysis and extensive Monte Carlo simulations, we find that in the stationary state, the system converges to two states: the whole healthy or the completely infected, which indicates an abrupt increase in the prevalence when there is a shortage of resources. More importantly, we find that too cautious and too selfless for the people during the outbreak of an epidemic are both not suitable for disease control. Through extensive simulations, we locate the optimal point, at which there is a maximum value of the epidemic threshold, and an outbreak can be delayed to the greatest extent. At last, we study further the effects of the network structure on the coupled dynamics. We find that the degree heterogeneity promotes the outbreak of disease, and the network structure does not alter the optimal phenomenon in behavior response. Based on the results of this study, a constructive suggestion is that in the face of a global pandemic, individuals or countries should strengthen mutual support and cooperation while doing their own prevention to suppress the epidemic optimally.

scholarly journals Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

2019 ◽  
Author(s):  
Gustavo Machado ◽  
Jason Ardila Galvis ◽  
Francisco Paulo Nunes Lopes ◽  
Joana Voges ◽  
Antônio Augusto Rosa Medeiros ◽  
...  
Keyword(s):  
Disease Control ◽  
Disease Surveillance ◽  
Control Strategies ◽  
Disease Spread ◽  
Growth Stages ◽  
Case Scenario ◽  
Modeling Framework ◽  
Worst Case ◽  
Movement Data ◽  
The Impact

SummaryTracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control interventions. Overall, the proposed modeling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data is available.

Finite size effects in epidemic spreading: the problem of overpopulated systems

Open Physics ◽  
2013 ◽  
Vol 11 (12) ◽  
Author(s):  
Wojciech Ganczarek
Keyword(s):  
Mixing Time ◽  
Finite Size ◽  
Network Size ◽  
Epidemic Spreading ◽  
Epidemic Threshold ◽  
Time Step ◽  
Finite Size Effects ◽  
Sexually Transmitted ◽  
The Impact ◽  
Quasi Stationary Distribution

AbstractIn this paper we analyze the impact of network size on the dynamics of epidemic spreading. In particular, we investigate the pace of infection in overpopulated systems. In order to do that, we design a model for epidemic spreading on a finite complex network with a restriction to at most one contamination per time step, which can serve as a model for sexually transmitted diseases spreading in some student communes. Because of the highly discrete character of the process, the analysis cannot use the continuous approximation widely exploited for most models. Using a discrete approach, we investigate the epidemic threshold and the quasi-stationary distribution. The main results are two theorems about the mixing time for the process: it scales like the logarithm of the network size and it is proportional to the inverse of the distance from the epidemic threshold.

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