Targeted Immunization Strategies Research Articles

Targeted Immunization Strategies and Designing Vaccine against Indian Nipah Virus Strain (NiV B) and Malaysian Variant (NiV M)

Targeted Immunization Strategies and Designing Vaccine against Indian Nipah Virus Strain (NiV B) and Malaysian Variant (NiV M)

Seroprevalence Assessment of Anti-Varicella Antibodies among Adults in the Province of Florence (Italy).

Background: Varicella infections follow a benign course in around 90% of cases, with more severe forms occurring in adults. To identify potential pockets of susceptibility and to improve targeted immunization strategies, this study aims to critically assess immunological status by evaluating varicella seroprevalence among adults (18-99 years) in the province of Florence (Italy), nearly a decade after Tuscany introduced the vaccination program. Methods: A convenience sample of 430 subjects aged 18 to 94 years (mean age 51.8 ± 18.8 years), stratified by age and sex (53.7% of subjects were female; N = 231), was collected between 2018 and 2019. Sero-analytical analyses were conducted utilizing EUROIMMUN Anti-VZV ELISA (IgG) kits. Results: Most of them were of Italian nationality (87.4%; N = 376). Among the 430 tested samples, 385 (89.5%) were positive and 39 (9.1%) were negative. The remaining six sera (1.4%), confirmed as equivocal, were excluded from further analysis. No significant differences were found based on sex (p-value = 0.706) or nationality (p-value = 0.112). The application of trend tests (Mantel-Haenszel; Kendall Tau-b) showed a significant trend (p < 0.024 and p < 0.032, respectively), with an increasing probability of finding a positive anti-varicella serological status passing from a lower age group (84.2%) to a higher one (93.0%). By considering the female population aged 18-49 years, the seroprevalence of anti-varicella antibodies was found to be 88.4%, with a susceptibility of 11.6%, highlighting the risk of acquiring infection during pregnancy. Conclusions: The introduction of varicella vaccination has had a significant impact on public health in Tuscany and in Italy more generally. However, further efforts should be made to reduce the number of individuals still susceptible in adulthood, with particular attention given to women of childbearing age and the promotion of vaccination through mass and social media and institutional websites.

Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks

Dynamics and Control Strategies for SLBRS Model of Computer Viruses Based on Complex Networks

Anti-rubella seroprevalence assessment in an adult sample population in Italy.

Despite global immunization efforts, rubella remains a public health concern, particularly in high- and middle-income countries. This study focused on rubella seroprevalence in the province of Florence, Italy, aiming to identify susceptibility clusters, especially among women in their childbearing age. A cross-sectional study was conducted between April 2018 and December 2019, enrolling 430 adult subjects (age over 18 years). Serum samples were collected, and anti-rubella antibodies were quantified using the ELISA test. Data were analyzed descriptively and compared by sex, nationality, and age groups using statistical tests. The overall rubella seroprevalence was high (92.3%), with no significant differences between genders or nationalities. Among childbearing-age females (18-49 years), the highest seroprevalence was observed in the 30-39 age group (94.1%). However, susceptibility clusters exceeding the 5% threshold set by WHO were identified, especially in females aged 40-49 years (7.0%). Despite high overall seroprevalence, the study identified pockets of susceptibility, even in childbearing age women. Continuous monitoring, targeted immunization strategies, and public health interventions are recommended to maintain rubella elimination, emphasizing the importance of sustained vaccination efforts to protect vulnerable populations.

A cleaved adhesin DNA vaccine targeting dendritic cell against Porphyromonas gingivalis-induced periodontal disease.

Arg-gingipain A (RgpA) is the primary virulence factor of Porphyromonas gingivalis and contains hemagglutinin adhesin (HA), which helps bacteria adhere to cells and proteins. Hemagglutinin's functional domains include cleaved adhesin (CA), which acts as a hemagglutination and hemoglobin-binding actor. Here, we confirmed that the HA and CA genes are immunogenic, and using adjuvant chemokine to target dendritic cells (DCs) enhanced protective autoimmunity against P. gingivalis-induced periodontal disease. C57 mice were immunized prophylactically with pVAX1-CA, pVAX1-HA, pVAX1, and phosphate-buffered saline (PBS) through intramuscular injection every 2 weeks for a total of three administrations before P. gingivalis-induced periodontitis. The DCs were analyzed using flow cytometry and ribonucleic acid sequencing (RNA-seq) transcriptomic assays following transfection with CA lentivirus. The efficacy of the co-delivered molecular adjuvant CA DNA vaccine was evaluated in vivo using flow cytometry, immunofluorescence techniques, and micro-computed tomography. After the immunization, both the pVAX1-CA and pVAX1-HA groups exhibited significantly elevated P. gingivalis-specific IgG and IgG1, as well as a reduction in bone loss around periodontitis-affected teeth, compared to the pVAX1 and PBS groups (p<0.05). The expression of CA promoted the secretion of HLA, CD86, CD83, and DC-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN) in DCs. Furthermore, the RNA-seq analysis revealed a significant increase in the chemokine (C-C motif) ligand 19 (p<0.05). A notable elevation in the quantities of DCs co-labeled with CD11c and major histocompatibility complex class II, along with an increase in interferon-gamma (IFN-γ) cells, was observed in the inguinal lymph nodes of mice subjected to CCL19-CA immunization. This outcome effectively illustrated the preservation of peri-implant bone mass in rats afflicted with P. gingivalis-induced peri-implantitis (p<0.05). The co-administration of a CCL19-conjugated CA DNA vaccine holds promise as an innovative and targeted immunization strategy against P. gingivalis-induced periodontitis and peri-implantitis.

Potential Intersections between lncRNA, Vascular Cognitive Impairment, and Immunization Strategies: Insights and Future Directions.

Vascular cognitive impairment (VCI) encompasses a wide range of cognitive disorders stemming from cerebrovascular issues, such as strokes or small vessel disease. These conditions often pose challenges to traditional diagnostic approaches due to their multifactorial nature and varied clinical presentations. Recently, next-generation sequencing (NGS) technologies have provided detailed analyses of long non-coding RNAs (lncRNAs) in the molecular pathobiology of VCI. These new findings help with molecular-based diagnostics and treatments of VCI. Within this realm, the concept of immune modulation, especially through specific vaccinations, emerges as a promising therapeutic strategy in VCI mitigation. In this review, we comprehensively elucidate the function of lncRNAs in VCI, emphasizing the advanced understanding of VCI's molecular underpinnings made possible through NGS technologies. Significant focus is placed on the immune system's role in VCI, particularly the neuroinflammatory processes which are consequential to cerebrovascular abnormalities. We believe that lncRNAs participate in regulating these immunological pathways, potentially guiding the development of vaccines targeting VCI. In this context, we propose a novel perspective: using knowledge about lncRNA profiles and functions to guide vaccine development, we can potentially exploit the body's immune response to mitigate or prevent VCI. This approach has the potential to revolutionize VCI management by introducing targeted immunization strategies informed by molecular signatures, a concept that remains largely unexplored in current research endeavors. In addition, we summarize current progress and propose future directions, advocating for robust, interdisciplinary studies to validate the potential intersections between lncRNA landscapes, VCI pathology, and immunology. This review aims to spur innovative research and promote the development of lncRNA-informed vaccine strategies as proactive interventions against the cognitive consequences of VCI.

Acquaintance immunization with limited knowledge of network structure

Optimal and efficient immunization of large networks remains a challenging task. Many theories and approaches have been suggested, however most of them require complete knowledge of the underlying network structure. Here, we study a targeted immunization strategy that incorporates the fact that there is often limited knowledge on the network structure. Previous work has suggested ‘acquaintance’ immunization, where rather than selecting a random individual to immunize, an individual is selected and then one of their acquaintances is immunized. Here, we generalize acquaintance immunization to the case where rather than selecting a random acquaintance, we examine the degrees of n acquaintances and immunize the one with the highest degree. We develop and solve an analytic framework for this model and verify our model with extensive numerical simulations. We determine the critical percolation threshold pc and the size of the giant component, , for arbitrary degree distributions. We also consider our immunization strategy on real-world networks and determine the variation of pc with increasing n. We find that our new approach improves on both acquaintance immunization and random immunization using limited knowledge.

Transmission Mechanism of Post-COVID-19 Emergency Supply Chain Based on Complex Network: An Improved SIR Model

Since the COVID-19 epidemic swept the world, the emergency supply chain (ESC) has faced serious uncertainty risks. To maintain the stability of the emergency supply, risk prevention and contingency measures must be prepared. In this paper, the authors first obtain the initial risk value of 0.4 using the fuzzy comprehensive evaluation approach and then build an improved SIR model based on a complex network to investigate the risk propagation law of the ESC. The simulation results show that (1) the high number of nodes becomes the initial risk source, the risk propagates faster and the peak value arrives two days earlier on average; (2) the initial infection rate gradually increases from 0.2 to 0.4, 0.6, and 0.8, and the risk spread speed also accelerates; (3) the recovery rate of network nodes increases gradually from 0.1 to 0.2, 0.3, and 0.4, and the influence range of risk propagation decreases inversely; (4) appropriately increasing the deletion rate of network nodes is conducive to improving the stability of the ESC network. Given the above ESC risk propagation law, this paper proposes relevant risk prevention measures and suggests that a risk early warning system of node enterprises should be established in combination with the target immunization strategy. For ESC risk management, the result has significant theoretical and practical implications.

Dynamical analysis of Hyper-SIR rumor spreading model

In this paper, a hyper susceptible-infected-recovered (Hyper-SIR) rumor propagation model is proposed based on hypergraph, which can describe the higher-order information interactions among nodes in the networks. Notably, hyperdegree is substitute for degree to model rumor spreading, and some reasonable results are obtained. Firstly, the threshold of Hyper-SIR rumor propagation model is acquired. Then, the stability of rumor-free/prevailing equilibrium is discussed by employing Lyapunov stability theory and LaSalle’s invariance principle. Moreover, uniform immunization and targeted immunization strategies are introduced to control the propagation of rumors. Besides, sensitivity analysis shows the different effects of parameters between Hyper-SIR model and SIR model. Finally, the numerical simulations are displayed and the results show Hyper-SIR model reaches equilibrium faster than general SIR model.

Adult Hepatitis B Virus Vaccination Coverage in China from 2011 to 2021: A Systematic Review.

Background: The most effective way to prevent hepatitis B virus (HBV) infection is vaccination. Synthesized data on vaccination coverage in adults against hepatitis B in China are scarce. We aimed to estimate the hepatitis B vaccination rate in adults in China. Methods: We searched PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure, WanFang, and Sinomed databases for observational studies published between 1 January 2011 and 1 October 2021. Data were extracted using a standardized form to estimate the pooled vaccination coverage rate and 95% confidence intervals (CI) based on inclusion and exclusion criteria. Subgroup analysis was employed to explore heterogeneity. This study is registered in PROSPERO, CRD42021293175. Results: We identified 5128 records, of which 21 articles that included 34,6571 adults. The pooled coverage rate and 95% confidence intervals were 26.27% and 22.73–29.82%, respectively. The pooled coverage rates were 22.06% (95% CI: 15.35–28.78%), 33.81% (95% CI: 28.77–38.85%) and 23.50% (95% CI: 17.37–29.64%) in eastern China, central China and western China, respectively. Furthermore, males had a pooled hepatitis B vaccination coverage rate of 23.47% (95% CI: 15.61–31.33%), whereas, in females, the coverage rate was 26.60% (95% CI: 18.73–34.47%). The pooled hepatitis B vaccination coverage rate in the age group younger than 40 years was 36.93% (95% CI: 28.35–45.50%), while in the ≥40-year-old group, the pooled hepatitis B vaccination coverage rate was 17.09% (95% CI: 10.18–24.00%). The pooled hepatitis B vaccination coverage rate in urban areas (40.29%, 95% CI: 20.91–59.67%) was higher than in rural areas (16.54%, 95% CI: 7.80–25.29%). The average weighted, pooled hepatitis B vaccination coverage rate was 26.53% (20.25–32.81%) in 2011–2015 and 26.12% (22.04–30.20%) in 2016–2021. Conclusions: This systematic review provides the hepatitis B vaccination coverage rate of adults in China (26.27%). The low prevalence of vaccine-mediated immunity among adults in China underscores the urgent need for targeted immunization strategies for vulnerable Chinese adults to ensure progress toward the target of eliminating hepatitis B by 2030.

Identifying influential spreaders in complex networks for disease spread and control

Identifying influential spreaders is an important task in controlling the spread of information and epidemic diseases in complex networks. Many recent studies have indicated that the identification of influential spreaders is dependent on the spreading dynamics. Finding a general optimal order of node importance ranking is difficult because of the complexity of network structures and the physical background of dynamics. In this paper, we use four metrics, namely, betweenness, degree, H-index, and coreness, to measure the central attributes of nodes for constructing the disease spreading models and target immunization strategies. Numerical simulations show that spreading processes based on betweenness centrality lead to the widest range of propagation and the smallest epidemic threshold for all six networks (including four real networks and two BA scale-free networks generated according to Barabasi–Albert algorithm). The target immunization strategy based on the betweenness centrality of nodes is the most effective for BA scale-free networks but displays poor immune effect for real networks in identifying the most important spreaders for disease control. The immunization strategy based on node degrees is the most effective for the four real networks. Findings show that the target immune strategy based on the betweenness centrality of nodes works best for standard scale-free networks, whereas that based on node degrees works best for other nonstandard scale-free networks. The results can provide insights into understanding the different metrics of measuring node importance in disease transmission and control.

Impacts of multitype interactions on epidemic spreading in temporal networks

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.

Backbone reconstruction in temporal networks from epidemic data.

Many complex systems are characterized by time-varying patterns of interactions. These interactions comprise strong ties, driven by dyadic relationships, and weak ties, based on node-specific attributes. The interplay between strong and weak ties plays an important role on dynamical processes that could unfold on complex systems. However, seldom do we have access to precise information about the time-varying topology of interaction patterns. A particularly elusive question is to distinguish strong from weak ties, on the basis of the sole node dynamics. Building upon analytical results, we propose a statistically-principled algorithm to reconstruct the backbone of strong ties from data of a spreading process, consisting of the time series of individuals' states. Our method is numerically validated over a range of synthetic datasets, encapsulating salient features of real-world systems. Motivated by compelling evidence, we propose the integration of our algorithm in a targeted immunization strategy that prioritizes influential nodes in the inferred backbone. Through Monte Carlo simulations on synthetic networks and a real-world case study, we demonstrate the viability of our approach.

Seroepidemiology of measles in Beijing, China: a cross-sectional study

ABSTRACT The aim of this study was to assess the seroprevalence of measles and identify the high risk of measles infection in the general population of Beijing. A total of 2144 subjects aged 0–76 years old were selected using a multi-stage stratified sampling method. Socio-demographic characteristics, vaccination history, and disease history of measles were collected by questionnaire. Serum samples were tested for measles-specific IgG by using commercial ELISA kits. The overall seropositivity rate of measles was 79.80% (95% CI 78.1–81.5%) and standardized seropositivity rate was 84.61% (95% CI 84.12–87.10%), with the median concentration of 773.40IU/L. The area of Beijing with the highest seroprevalence was the central area [81.79% (95% CI 80.16–83.42%) and 855.84IU/L]. There were no significant differences in seropositivity rates of different genders (P = 0.074), history of measles infection (P = 0.421) and registered population (P = 0.598). The 1–4 age group had the highest seropositivity rate [94.06%(95% CI 93.06–95.06%)] and children below the age of 1 (0–12 months) had the lowest seropositivity rate [34.42% (95% CI 32.41–36.43%)]. The 30–34 and 35–39 age groups were relatively lower with 72.90% (95% CI 71.02–74.78%) and 74.65% (95% CI 72.81–76.49%) respectively. Seropositivity rates changed along with the incidence rates of measles periodically by years. As shown in the present study, the seroprevalence of measles antibody in Beijing have not yet met the threshold required to achieve measles elimination and therefore the risk of an epidemic of measles will be existing. Appropriate targeted immunization strategies and measures should be considered and carried out.

SE2IR Invest Market Rumor Spreading Model Considering Hesitating Mechanism

Abstract According to the actual situation of investor network, a SE2IR rumor spreading model with hesitating mechanism is proposed, and the corresponding mean-field equations is obtained on scale-free network. In this paper, we first combine the theory of spreading dynamics and find out the basic reproductive number R0. And then analyzes the stability of the rumor-free equilibrium and the final rumor size. Finally, we discuss random immune strategies and target immune strategies for the rumor spreading, respectively. Through numerical simulation, we can draw the following conclusions: Reducing the fuzziness and attractiveness of invest market rumor can effectively reduce the impact of rumor. And the target immunization strategy is more effective than the random immunization strategy for the communicators in the invest investor network.

On the stability of multilayer Boolean networks under targeted immunization

In this paper, we study targeted immunization in a multilayer Boolean network model for genetic regulatory networks. Given a specific set of nodes immune to perturbations, we find that the stability of a multilayer Boolean network is determined by the largest eigenvalue of the weighted non-backtracking matrix of corresponding aggregated network. Aimed to minimize this largest eigenvalue, we developed the metric of multilayer collective influence (MCI) to quantify the impact of immunizing individual nodes on the stability of the system. Compared with other competing heuristics, immunizing nodes with high MCI scores can stabilize an unstable multilayer network with higher efficiency on both synthetic and real-world networks. Moreover, despite that coupling nodes can exert direct influence across multiple layers, they are found to exhibit less importance as measured by the MCI score. Our work reveals the mechanism of maintaining the stability of multilayer Boolean networks and provides an efficient targeted immunization strategy, which can be potentially applied to the location of pathogenesis of diseases and the development of targeted therapy.

Eigenvector Localization in Real Networks and Its Implications for Epidemic Spreading

The spectral properties of the adjacency matrix, in particular its largest eigenvalue and the associated principal eigenvector, dominate many structural and dynamical properties of complex networks. Here we focus on the localization properties of the principal eigenvector in real networks. We show that in most cases it is either localized on the star defined by the node with largest degree (hub) and its nearest neighbors, or on the densely connected subgraph defined by the maximum $K$-core in a $K$-core decomposition. The localization of the principal eigenvector is often strongly correlated with the value of the largest eigenvalue, which is given by the local eigenvalue of the corresponding localization subgraph, but different scenarios sometimes occur. We additionally show that simple targeted immunization strategies for epidemic spreading are extremely sensitive to the actual localization set.

Research on edges immunization strategy for complex network based on SIS-CA model

This paper proposes a targeted edges immunization strategy, which aims to immune a certain number of the most important edges from the perspective of protecting transmission path. To better simulate the dynamic changes of risks propagation in real complex networks, an SIS-CA (Susceptible-Infected-Susceptible Cellular Automation) propagation model is developed, which combines SIS (Susceptible-Infected-Susceptible) model with the cellular automata (CA) method and takes nonuniformity and adjustment time into consideration. Based on SIS-CA propagation model, the feasibility of targeted edges immunization strategy was analysed in comparison with other targeted immunization strategy in the WS (Watts-Strogatz) small-world network model, BA (Barabasi-Albert) scale-free network model, and an aircraft manufacturing enterprise collaboration network (MECN) known to have the features of complex networks. The test results show that compared with conventional targeted immunization strategies, the proposed targeted edges immunization strategy obtains a better immunization efficacy via protecting fewer key edges.

Improved targeted immunization strategies based on two rounds of selection

In the case of high degree targeted immunization where the number of vaccine is limited, when more than one node associated with the same degree meets the requirement of high degree centrality, how can we choose a certain number of nodes from those nodes, so that the number of immunized nodes will not exceed the limit? In this paper, we introduce a new idea derived from the selection process of second-round exam to solve this problem and then propose three improved targeted immunization strategies. In these proposed strategies, the immunized nodes are selected through two rounds of selection, where we increase the quotas of first-round selection according the evaluation criterion of degree centrality and then consider another characteristic parameter of node, such as node’s clustering coefficient, betweenness and closeness, to help choose targeted nodes in the second-round selection. To validate the effectiveness of the proposed strategies, we compare them with the degree immunizations including the high degree targeted and the high degree adaptive immunizations using two metrics: the size of the largest connected component of immunized network and the number of infected nodes. Simulation results demonstrate that the proposed strategies based on two rounds of sorting are effective for heterogeneous networks and their immunization effects are better than that of the degree immunizations.

Prime and Target Immunization Protects Against Liver-Stage Malaria

Abstract A highly effective vaccine against malaria is urgently needed, with leading vaccination strategies targeting primarily the pre-erythrocytic liver stages of the Plasmodium falciparum lifecycle. Malaria antigen specific CD8+ T-cells to are known to be protective, with heterologous prime-boost viral vector immunization being an effective method of induction. Non-replicating viral vectored vaccines have shown a remarkable capacity to induce systemic CD8+ T-cell responses in animals and humans. To date, the greatest immunogenicity has been obtained through a heterologous prime boost regimen, where vaccination with an adenoviral vector is followed 8 weeks later by a Modified Vaccinia Ankara virus (MVA) boost. A novel vaccine strategy aimed at priming CD8+ T-cells in the periphery and subsequently targeting them to hepatic tissue with protein loaded poly(lactic-co-glycolic acid) nanoparticles or recombinant viral vectors administered by specific immunization routes was developed. Durable Ag-specific CD8+ T-cells exhibiting a phenotype of tissue-resident memory (TRM) T-cells were generated in the liver, with a ten-fold increase over the conventional heterologous vector regime. Importantly, in a P. berghei sporozoite challenge model of liver-stage malaria, this strategy was found to result in unprecedented sterile protection across several clinically relevant antigens and mouse strains. This prime and target immunization strategy for malaria may provide a general approach for prevention or immunotherapy against pathogens that infect the liver.