Targeted Immunization Strategies Research Articles

Improved immunization strategy to reduce energy consumption on nodes traffic

The increasing requirement of transmission network sizes would result in huge energy consumption with communication traffic. Green communication technologies are expected to help in reducing energy consumption impact to environment. Therefore, it is important to design energy-efficient strategy that can decrease energy consumption. This paper proposes to use the acquaintance and improved targeted immunization strategies from complex systems to resolve energy consumption issues and uses traffic as measure standard to obtain a stable threshold. The simulation results show that the improved control strategy is better and more effective to save as much energy as possible.

System recovery-aware virus propagation model and its steady-state analysis

Network structures and human behaviors are considered as two important factors in virus defense currently. However, due to ignorance of network security, normal users usually take simple activities, such as reinstalling computer system, or using the computer recovery system to clear virus. How system recovery influences virus spreading is not taken into consideration currently. In this paper, a new virus propagation model considering the system recovery is proposed first, and then in its steady-state analysis, the virus propagation steady time and steady states are deduced. Experiment results show that models considering system recovery can effectively restrain virus propagation. Furthermore, algorithm with system recovery in BA scale-free network is proposed. Simulation result turns out that target immunization strategy with system recovery works better than traditional ones in BA network.

Immunization strategy based on the critical node in percolation transition

The problem of finding a better immunization strategy for controlling the spreading of the epidemic with limited resources has attracted much attention since its great theoretical significance and wide application. In this letter, we propose a novel and successful targeted immunization strategy based on percolation transition. Our strategy repeatedly looks for the critical nodes for immunizing. The critical node, which leads to the emergence of the giant connected component as the degree threshold increases, is determined when the maximal second-largest connected component disappears. To test the effectiveness of the proposed method, we conduct the experiments on several artificial networks and real-world networks. The results show that the proposed method outperforms the degree centrality strategy, the betweenness centrality strategy and the adaptive degree centrality strategy with 18% to 50% fewer immunized nodes for same amount of immunization.

Methodological criticisms in the evaluation of Pneumococcal Conjugate Vaccine effectiveness.

Globally, lower respiratory tract infections (LRTIs), including community-acquired pneumonia (CAP), cause considerable of morbidity and mortality in adults, especially in the elderly. In addition to age, underlying medical conditions are associated with an increased risk of CAP. From an aetiological point of view, Streptococcus pneumoniae is the leading cause of adult CAP throughout the world. Two types of vaccine are available for the prevention of pneumococcal diseases: the pneumococcal polysaccharide vaccine (PPV23) and the pneumococcal conjugate vaccine (PCV7, PCV10 and PCV13). An accurate understanding of the LRTIs burden and the types of subjects at risk of CAP, allow to find an appropriately targeted immunization strategy and provide baseline data to evaluate pneumococcal vaccine effectiveness. Given the high variability in available estimates of LRTIs burden and associated risk factors, the objective of the study was to discuss the methodological criticism in its evaluation, in the light of the gradual introduction of PCV13 immunization strategy targeted to elderly and risk groups in middle-high income countries.

An effective immunization strategy for airborne epidemics in modular and hierarchical social contact network

Social contact between individuals is the chief factor for airborne epidemic transmission among the crowd. Social contact networks, which describe the contact relationships among individuals, always exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated. We find that traditional global targeted immunization strategy would lose its superiority in controlling the epidemic propagation in the social contact networks with modular and hierarchical structure. Therefore, we propose a hierarchical targeted immunization strategy to settle this problem. In this novel strategy, importance of the hierarchical structure is considered. Transmission control experiments of influenza H1N1 are carried out based on a modular and hierarchical network model. Results obtained indicate that hierarchical structure of the network is more critical than the degrees of the immunized targets and the modular network layer is the most important for the epidemic propagation control. Finally, the efficacy and stability of this novel immunization strategy have been validated as well.

Immunization strategy for epidemic spreading on multilayer networks

In many real-world complex systems, individuals have many kinds of interactions among them, suggesting that it is necessary to consider a layered-structure framework to model systems such as social interactions. This structure can be captured by multilayer networks and can have major effects on the spreading of process that occurs over them, such as epidemics. In this letter we study a targeted immunization strategy for epidemic spreading over a multilayer network. We apply the strategy in one of the layers and study its effect in all layers of the network disregarding degree-degree correlation among layers. We found that the targeted strategy is not as efficient as in isolated networks, due to the fact that in order to stop the spreading of the disease it is necessary to immunize more than 80% of the individuals. However, the size of the epidemic is drastically reduced in the layer where the immunization strategy is applied compared to the case with no mitigation strategy. Thus, the immunization strategy has a major effect on the layer were it is applied, but does not efficiently protect the individuals of other layers.

Immunization of epidemics in multiplex networks.

Up to now, immunization of disease propagation has attracted great attention in both theoretical and experimental researches. However, vast majority of existing achievements are limited to the simple assumption of single layer networked population, which seems obviously inconsistent with recent development of complex network theory: each node could possess multiple roles in different topology connections. Inspired by this fact, we here propose the immunization strategies on multiplex networks, including multiplex node-based random (targeted) immunization and layer node-based random (targeted) immunization. With the theory of generating function, theoretical analysis is developed to calculate the immunization threshold, which is regarded as the most critical index for the effectiveness of addressed immunization strategies. Interestingly, both types of random immunization strategies show more efficiency in controlling disease spreading on multiplex Erdös-Rényi (ER) random networks; while targeted immunization strategies provide better protection on multiplex scale-free (SF) networks.

Containing smartphone worm propagation with an influence maximization algorithm

In recent years, wide attention has been drawn to the problem of containing worm propagation in smartphones. Unlike existing containment models for worm propagation, we study how to prevent worm propagation through the immunization of key nodes (e.g., the top k influential nodes). Thus, we propose a novel containment model based on an influence maximization algorithm. In this model, we introduce a social relation graph to evaluate the influence of nodes and an election mechanism to find the most influential nodes. Finally, this model provides a targeted immunization strategy to disable worm propagation by immunizing the top k influential nodes. The experimental results show that the model not only finds the most influential top k nodes quickly, but also effectively restrains and controls worm propagation.

Controllability of complex networks based on propagation immunization

Control of complex network reflects humans' comprehension of complex system and the ability to reform it. Up-to-date research establishes the controllability theory of the complex networks based on linear system control theory. The theory could find a minimal set of driver nodes which controls all nodes' state in a linear time invariant complex network with any topology. However, this theory does not take into account the immune node or failure node which blocks the control signal. While inheriting the advantages of the theory, in the paper we first refine the complex network control model based on propagation immunization. Second we adopt four methods which belong to random immunization strategy and targeted immunization strategy to determine the immune nodes, and analyze the controllability of 14 real networks. The experimental results show that when the nodes which have higher degrees, betweeness or closeness are treated as immune nodes, the control of complex networks will become more difficult.

Modelling and analysis of influenza A (H1N1) on networks

BackgroundIn April 2009, a new strain of H1N1 influenza virus, referred to as pandemic influenza A (H1N1) was first detected in humans in the United States, followed by an outbreak in the state of Veracruz, Mexico. Soon afterwards, this new virus kept spreading worldwide resulting in a global outbreak. In China, the second Circular of the Ministry of Health pointed out that as of December 31, 2009, the country’s 31 provinces had reported 120,000 confirmed cases of H1N1.MethodsWe formulate an epidemic model of influenza A based on networks. We calculate the basic reproduction number and study the effects of various immunization schemes. The final size relation is derived for the network epidemic model. The model parameters are estimated via least-squares fitting of the model solution to the observed data in China.ResultsFor the network model, we prove that the disease-free equilibrium is globally asymptotically stable when the basic reproduction is less than one. The final size will depend on the vaccination starting time, T, the number of infective cases at time T and immunization schemes to follow. Our theoretical results are confirmed by numerical simulations. Using the parameter estimates based on the observation data of the cumulative number of hospital notifications, we estimate the basic reproduction number R0 to be 1.6809 in China.ConclusionsNetwork modelling supplies a useful tool for studying the transmission of H1N1 in China, capturing the main features of the spread of H1N1. While a uniform, mass-immunization strategy helps control the prevalence, a targeted immunization strategy focusing on specific groups with given connectivity may better control the endemic.

A high-resolution human contact network for infectious disease transmission

The most frequent infectious diseases in humans--and those with the highest potential for rapid pandemic spread--are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 m among 788 individuals. The data revealed a high-density network with typical small-world properties and a relatively homogeneous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.

Vaccination strategies.

By nature of their long-term deployment in challenging areas of the world, expatriate employees and their dependents are at increased risk for a number of infectious diseases. Targeted immunization strategies may decrease this risk. Expatriate employees and their dependents should be immunized before deployment, and programs should be established to provide booster immunizations if the risk is ongoing. When considering work-force productivity, work-force infectivity, and the common good, careful consideration should be given to establishing at least a basic immunization program for in-country nationals and their dependents.

Efficient immunization strategies for computer networks and populations.

We present an effective immunization strategy for computer networks and populations with broad and, in particular, scale-free degree distributions. The proposed strategy, acquaintance immunization, calls for the immunization of random acquaintances of random nodes (individuals). The strategy requires no knowledge of the node degrees or any other global knowledge, as do targeted immunization strategies. We study analytically the critical threshold for complete immunization. We also study the strategy with respect to the susceptible-infected-removed epidemiological model. We show that the immunization threshold is dramatically reduced with the suggested strategy, for all studied cases.

Topical immunization using nanoengineered genetic vaccines

DNA vaccines have been shown to elicit both broad humoral and cellular immune responses. Needle-free injection devices and the gene gun have been used to deliver these DNA vaccines to dendritic cells in the viable skin epidermis with some success. However, more cost-effective and dendritic cell (DC)-targeted immunization strategies are sought. To this end, a nanoengineered genetic vaccine for simple topical application was developed. Expressed β-galactosidase was used as a model antigen. Plasmid DNA was coated on the surface of preformed cationic nanoparticles engineered directly from warm oil-in-water (O/W) microemulsion precursors comprised of emulsifying wax as the oil phase and CTAB as a cationic surfactant. Mannan, a DC ligand, was coated on the nanoparticles with and without entrapped endosomolytic agents, dioleoyl phosphatidylethanolamine (DOPE) and cholesterol. In-vitro cell transfection studies were performed to confirm transgene expression with these pDNA-coated nanoparticles. An in-vitro Concanavalin A ( ConA) agglutination assay confirmed the presence of mannan on the surface of nanoparticles. The humoral and proliferative immune responses were assessed after topical application of these nanoengineered systems to the skin of shaved Balb/C mice. All pDNA-coated nanoparticles, especially the mannan-coated pDNA-nanoparticles with DOPE, resulted in significant enhancement in both antigen-specific IgG titers (16-fold) and splenocyte proliferation over ‘naked’ pDNA alone.