A time-varying network model for sexually transmitted infections accounting for behavior and control actions

In this article, we propose a stochastic network model for the spread of common sexually transmitted infections (STIs). The model expands the standard susceptible–infected–susceptible model by incorporating asymptomatic infected individuals—who are unaware that they are posing a health threat to themselves and the population—and individuals' behavioral preferences with regard to the use of protective measures during encounters. Behavioral preferences evolve according to a nontrivial mechanism accounting for the cost of using protection, the perceived risk, and persuasive effects due to sexual encounters with different-minded individuals. The disease spreads on a time-varying network of sexual contacts, generated using a mechanism inspired by continuous-time activity-driven networks. Such a network accounts for regular partners and casual encounters, which are regulated by a negotiation process that accounts for the individuals' behavioral preferences. Finally, three control measures are included in the model: (i) condom (social) marketing campaigns, (ii) routine screening at STI clinics, and (iii) partner notification. We use a mean-field approach to analytically derive the epidemic threshold in the limit of large-scale populations, in the absence of a partner network, for two distinct negotiation processes. Our results indicate that routine screening is key to the eradication of local outbreaks, while condom marketing campaigns become effective only when combined with screening. Monte Carlo simulations are then employed to extend our analytical findings, casting lights on the role of the partner network and on partner notification as a control measure in the spread of STIs

A Coevolutionary Model for Actions and Opinions in Social Networks

© 2020 IEEE. In complex social networks, the decision-making mechanisms behind human actions and the cognitive processes that shape opinion formation processes are often intertwined, leading to complex and varied collective emergent behavior. In this paper, we propose a mathematical model that captures such a coevolution of actions and opinions. Following a discrete-time process, each individual decides between binary actions, aiming to coordinate with the actions of other members observed on a network of interactions and taking into account their own opinion. At the same time, the opinion of each individual evolves due to the opinions shared by other members, the actions observed on the network, and, possibly, an external influence source. We provide a global convergence result for a special case of the coupled dynamics. Steady state configurations in which all the individuals take the same action are then studied, elucidating the role of the model parameters and the network structure on the collective behavior of the system

Game-theoretic modeling of collective decision making during epidemics

The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral-epidemic model, in which an interplay of realistic factors shapes the coevolution of individual decision making and epidemics on a network. Although such a coevolution is deeply intertwined in the real world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our paradigm offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows us to provide analytical insights on the epidemic process and to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model

A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

Worldwide urbanization calls for a deeper understanding of epidemic spreading within urban environments. Here, we tackle this problem through an agent-based model, in which agents move in a two-dimensional physical space and interact according to proximity criteria. The planar space comprises several locations, which represent bounded regions of the urban space. Based on empirical evidence, we consider locations of different density and place them in a core-periphery structure, with higher density in the central areas and lower density in the peripheral ones. Each agent is assigned to a base location, which represents where their home is. Through analytical tools and numerical techniques, we study the formation mechanism of the network of contacts, which is characterized by the emergence of heterogeneous interaction patterns. We put forward an extensive simulation campaign to analyze the onset and evolution of contagious diseases spreading in the urban environment. Interestingly, we find that, in the presence of a core-periphery structure, the diffusion of the disease is not affected by the time agents spend inside their base location before leaving it, but it is influenced by their motion outside their base location: a strong tendency to return to the base location favors the spreading of the disease. A simplified one-dimensional version of the model is examined to gain analytical insight into the spreading process and support our numerical findings. Finally, we investigate the effectiveness of vaccination campaigns, supporting the intuition that vaccination in central and dense areas should be prioritized

A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

Worldwide urbanization calls for a deeper understanding of epidemic spreading within urban environments. Here, we tackle this problem through an agent-based model, in which agents move in a two-dimensional physical space and interact according to proximity criteria. The planar space comprises several locations, which represent bounded regions of the urban space. Based on empirical evidence, we consider locations of different density and place them in a core-periphery structure, with higher density in the central areas and lower density in the peripheral ones. Each agent is assigned to a base location, which represents where their home is. Through analytical tools and numerical techniques, we study the formation mechanism of the network of contacts, which is characterized by the emergence of heterogeneous interaction patterns. We put forward an extensive simulation campaign to analyze the onset and evolution of contagious diseases spreading in the urban environment. Interestingly, we find that, in the presence of a core-periphery structure, the diffusion of the disease is not affected by the time agents spend inside their base location before leaving it, but it is influenced by their motion outside their base location: a strong tendency to return to the base location favors the spreading of the disease. A simplified one-dimensional version of the model is examined to gain analytical insight into the spreading process and support our numerical findings. Finally, we investigate the effectiveness of vaccination campaigns, supporting the intuition that vaccination in central and dense areas should be prioritized

A model predictive control approach to optimally devise a two-dose vaccination rollout: A case study on COVID-19 in Italy

The COVID-19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two-dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID-19, which includes the effect of nonpharmaceutical interventions and a concurrent two-dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio-economic costs associated with the epidemics. We calibrate our model to the 2021 COVID-19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first-dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first-dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID-19 vaccination campaign, and increase preparedness for future epidemics

Analysis of the Heterogeneous Vectorial Network Model of Collective Motion

We analyze the vectorial network model, a stochastic protocol that describes collective motion of groups of agents, randomly mixing in a planar space. Motivated by biological and technical applications, we focus on a heterogeneous form of the model, where agents have different propensity to interact with others. By linearizing the dynamics about a synchronous state and leveraging an eigenvalue perturbation argument, we establish a closed-form expression for the mean-square convergence rate to the synchronous state in the absence of additive noise. These closed-form findings are extended to study the effect of added noise on the agents' coordination, captured by the polarization of the group. Our results reveal that heterogeneity has a detrimental effect on both the convergence rate and the polarization, which is nonlinearly moderated by the average number of connections in the group. Numerical simulations are provided to support our theoretical findings.</p

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of “what-if” scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY—one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches—in hospitals or drive-through facilities—and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features

Altered sirtuin expression is associated with node-positive breast cancer

Sirtuins are genes implicated in cellular and organismal ageing. Consequently, they are speculated to be involved in diseases of ageing including cancer. Various cancers with widely differing prognosis have been shown to have differing and characteristic expression of these genes; however, the relationship between sirtuin expression and cancer progression is unclear. In order to correlate cancer progression and sirtuin expression, we have assessed sirtuin expression as a function of primary cell ageing and compared sirtuin expression in normal, ‘nonmalignant' breast biopsies to breast cancer biopsies using real-time polymerase chain reaction (PCR). Levels of SIRT7 expression were significantly increased in breast cancer (P<0.0001). Increased levels of SIRT3 and SIRT7 transcription were also associated with node-positive breast cancer (P<0.05 and P<0.0001, respectively). This study has demonstrated differential sirtuin expression between nonmalignant and malignant breast tissue, with consequent diagnostic and therapeutic implications

No effect of 14 day consumption of whole grain diet compared to refined grain diet on antioxidant measures in healthy, young subjects: a pilot study

<p>Abstract</p> <p>Background</p> <p>Epidemiological evidence supports that a diet high in whole grains is associated with lowered risk of chronic diseases included coronary heart disease, obesity, type 2 diabetes, and some types of cancer. One potential mechanism for the protective properties of whole grains is their antioxidant content. The aim of this study was to compare differences in antioxidant measures when subjects consumed either refined or whole grain diets.</p> <p>Methods</p> <p>Twenty healthy subjects took part in a randomized, crossover dietary intervention study. Subjects consumed either a refined grain or whole grain diet for 14 days and then the other diet for the next 14 days. Male subjects consumed 8 servings of grains per day and female subjects consumed 6 servings of grains per day. Blood and urine samples were collected at the end of each diet. Antioxidant measures included oxygen radical absorbance capacity (ORAC) in blood, and isoprostanes and thiobarbituric acid reactive substances (TBARS) in urine.</p> <p>Results</p> <p>The whole grain diet was significantly higher in dietary fiber, vitamin B6, folate, selenium, copper, zinc, iron, magnesium and cystine compared to the refined grain diet. Despite high intakes of whole grains, no significant differences were seen in any of the antioxidant measures between the refined and whole grain diets.</p> <p>Conclusions</p> <p>No differences in antioxidant measures were found when subjects consumed whole grain diets compared to refined grain diets.</p