Lorenzo Zino

Lorenzo Zino
Politecnico di Torino | polito · DET - Department of Electronics and Telecommunications

PhD in Pure and Applied Mathematics

About

73
Publications
2,600
Reads
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564
Citations
Citations since 2017
71 Research Items
562 Citations
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2017201820192020202120222023050100150200
Introduction
My research interests include but are not limited to dynamical processes over network systems (spread of epidemic diseases, opinion dynamics, diffusion of innovation etc.), applied probability, network models and analysis, and game theory. I am interested in the modeling, the analysis, and the control aspects of dynamics over networks.
Additional affiliations
October 2019 - September 2022
University of Groningen
Position
  • PostDoc Position
September 2019 - September 2019
Politecnico di Torino
Position
  • Visiting Scholar
June 2019 - July 2019
New York University
Position
  • Research Assistant
Education
November 2014 - October 2018
Politecnico di Torino
Field of study
  • Pure and Applied Mathematics
October 2012 - July 2014
Politecnico di Torino
Field of study
  • Applied Mathematics (Mathematical Engineering)
September 2009 - October 2012
Politecnico di Torino
Field of study
  • Applied Mathematics (Mathematics for Engineering)

Publications

Publications (73)
Article
Activity-driven networks are a powerful paradigm to study epidemic spreading over time-varying networks. Despite significant advances, most of the current understanding relies on discrete-time computer simulations, in which each node is assigned an activity potential from a continuous distribution. Here, we establish a continuous-time discrete-dist...
Article
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 coevolut...
Article
Full-text available
Social conventions change when individuals collectively adopt an alternative over the status quo, in a process known as social diffusion. Our repeated trials of a multi-round experiment provided data that helped motivate the proposal of an agent-based model of social diffusion that incorporates inertia and trend-seeking, two behavioural mechanisms...
Article
We study a controlled evolutionary dynamics that models the spread of a novel state in a network where the exogenous control aims to quickly spread the novel state. We estimate the performance of the system by analytically establishing upper and lower bounds on the expected time needed for the novel state to replace the original one. Such bounds ar...
Article
During the ongoing COVID-19 pandemic, mathematical models of epidemic spreading have emerged as powerful tools to produce valuable predictions of the evolution of the pandemic, helping public health authorities decide which intervention policies should be implemented. The study of these modelsÑgrounded in the systems theory and often analyzed using...
Article
Exploring a COVID‐19 Endemic Scenario In article 2200481, Agnieszka Truszkowska, Maurizio Porfiri and co‐workers discuss the implications of the concurrent spread of two COVID‐19 strains in the model town of New Rochelle, NY, building upon their previously published high‐resolution agent‐based model. Image by Anna Sawulska, Alain Boldini, and Mauri...
Article
Full-text available
This brief deals with the secure bipartite consensus problem for linear multi-agent systems (MASs) with Denial-of-Service (DoS) attacks subject to an observer-based dynamic event-triggered strategy (OBDES). Two event-triggered strategies involved with dynamic threshold are developed in controller and observer channels. Based on the connected struct...
Preprint
Motivated by the climate crisis that is currently ravaging the planet, we propose and analyze a novel framework for the evolution of anthropogenic climate impact in which the evolution of human environmental behavior and environmental impact is coupled. Our framework includes a human decision-making process that captures social influence, governmen...
Article
Our efforts as a society to combat the ongoing COVID‐19 pandemic are continuously challenged by the emergence of new variants. These variants can be more infectious than existing strains and many of them are also more resistant to available vaccines. The appearance of these new variants cause new surges of infections, exacerbated by infrastructural...
Article
Full-text available
Dynamic norms have recently emerged as a powerful method to encourage individuals to adopt an innovation by highlighting a growing trend in its uptake. However, there have been no concrete attempts to understand how this individual-level mechanism might shape the collective population behavior. Here, we develop a framework to examine this by encaps...
Article
Full-text available
The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting...
Preprint
Motivated by massive outbreaks of COVID-19 that occurred even in populations with high vaccine uptake, we propose a novel multi-population temporal network model for the spread of recurrent epidemic diseases. We study the effect of human behavior, testing, and vaccination campaigns on the control of local outbreaks and infection prevalence. Our mod...
Article
This paper is intended to solve the fully distributed secure bipartite consensus problem of nonlinear multi-agent systems (MASs) with quantized information under Denial-of-Service (DoS) attacks. The attacks, which constrained on attack frequency and duration are studied. Firstly, we propose a novel secure output feedback control protocol integrated...
Preprint
Modeling opinion formation and decision-making processes, important in their own rights, have been treated as separate problems in the study of dynamical models for social networks. Empirical studies suggest a deep intertwining between these two processes, and in this paper, we bridge the gap in the existing research by proposing a novel coevolutio...
Article
The ongoing pandemic is laying bare dramatic differences in the spread of COVID-19 across seemingly similar urban environments. Identifying the urban determinants that underlie these differences is an open research question, which can contribute to more epidemiologically resilient cities, optimized testing and detection strategies, and effective im...
Article
Predicting the Effects of Waning Vaccine Immunity Against COVID-19 through High-Resolution Agent-Based Modeling Mathematical models have proven to be indispensable in our fight against COVID-19. In article 2100521, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers expand on a high-resolution agent-based model published previously in this jour...
Article
Full-text available
The ongoing COVID-19 pandemic has led public health authorities to face the unprecedented challenge of planning a global vaccination campaign, which for most protocols entails the administration of two doses, separated by a bounded but flexible time interval. The partial immunity already offered by the first dose and the high levels of uncertainty...
Preprint
Full-text available
The spread of an epidemic disease and the population's collective behavioural response are deeply intertwined, influencing each other's evolution. Such a co-evolution typically has been overlooked in mathematical models, limiting their real-world applicability. To address this gap, we propose and analyse a behavioural-epidemic model, in which a sus...
Article
Full-text available
We propose a multi-layer network model for the spread of an infectious disease that accounts for interactions within the family, between children in classes and schools, and casual contacts in the population. The proposed framework is designed to test several what-if scenarios on school openings during the vaccination campaigns, thereby assessing t...
Article
This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of ne...
Preprint
In this letter, we deal with evolutionary game theoretic learning processes for population games on networks with dynamically evolving communities. Specifically, we propose a novel mathematical framework in which a deterministic, continuous-time replicator equation on a community network is coupled with a closed dynamic flow process between communi...
Article
The potential waning of the vaccination immunity to COVID‐19 could pose threats to public health, as it is tenable that the timing of such waning would synchronize with the near‐complete restoration of normalcy. Should also testing be relaxed, a resurgent COVID‐19 wave in winter 2021/2022 might be witnessed. In response to this risk, an additional...
Article
Full-text available
Objective The goal of this study was to dynamically model next-wave scenarios to observe the impact of different lockdown measures on the infection rates (IR) and mortality for two different prototype countries, mimicking the 1st year of the COVID-19 pandemic in Europe. Methods A dynamic simulation SIRD model was designed to assess the effectivene...
Article
The spread of an epidemic disease and the population's collective behavioral response are deeply intertwined, influencing each other's evolution. Such a co-evolution typically has been overlooked in mathematical models, limiting their real-world applicability. To address this gap, we propose and analyse a behavioral-epidemic model, in which a susce...
Article
In this letter, we deal with evolutionary game-theoretic learning processes for population games on networks with dynamically evolving communities. Specifically, we propose a novel mathematical framework in which a deterministic, continuous-time replicator equation on a community network is coupled with a closed dynamic flow process between communi...
Article
We propose a novel network epidemic model to elucidate the impact of deniers on the spread of epidemic diseases. Specifically, we study the spread of a recurrent epidemic disease, whose progression is captured by a susceptible–infected–susceptible model, in a population partitioned into two groups: cautious and deniers. Cautious individuals may ado...
Article
Full-text available
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' behavi...
Preprint
Motivated by the increasing number of COVID-19 cases that have been observed in many countries after the vaccination and relaxation of non-pharmaceutical interventions, we propose a mathematical model on time-varying networks for the spread of recurrent epidemic diseases in a partially vaccinated population. The model encapsulates several realistic...
Preprint
The COVID-19 pandemic is yet again on the verge of escalating, despite a hopeful case decrease recorded during spring and summer 2021, due to successful vaccination roll-outs. Together with the emergence of new variants, the potential waning of the vaccination immunity could pose threats to public health. It is tenable that the timing of such a gra...
Preprint
Full-text available
Understanding how to effectively control an epidemic spreading on a network is a problem of paramount importance for the scientific community. The ongoing COVID-19 pandemic has highlighted the need for policies that mitigate the spread, without relying on pharmaceutical interventions, that is, without the medical assurance of the recovery process....
Article
High-Resolution COVID-19 Agent-Based Modeling As COVID-19 vaccine is being rolled-out, theory and simulation tools could assist public health authorities in reopening the economy. In article number 2100157, Agnieszka Truszkowska, Maurizio Porfiri, and co-workers examine the role of the vaccination rate on the spreading of COVID-19 in a high-resolut...
Article
As COVID‐19 vaccine is being rolled out in the US, public health authorities are gradually reopening the economy. To date, there is no consensus on a common approach among local authorities. Here, a high‐resolution agent‐based model is proposed to examine the interplay between the increased immunity afforded by the vaccine roll‐out and the transmis...
Article
Full-text available
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 prob...
Preprint
Full-text available
We propose a multi-layer network model for the spread of COVID-19 that accounts for interactions within the family, between schoolmates, and casual contacts in the population. We utilize the proposed model-calibrated on epidemiological and demographic data-to investigate current questions concerning the implementation of non-pharmaceutical interven...
Article
Since 2020, COVID‐19 has wreaked havoc across the planet, taking the lives of more than one million people. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that can support effective policy‐making. In article number 2000277, Agnieszka Truszkowska, Maurizio Porfiri, and co‐workers report...
Preprint
During the ongoing COVID-19 pandemic, mathematical models of epidemic spreading have emerged as powerful tools to produce valuable predictions of the evolution of the pandemic, helping public health authorities decide which intervention policies should be implemented. The study of these models -- grounded in the systems theory and often analyzed us...
Preprint
Motivated by the literature on opinion dynamics and evolutionary game theory, we propose a novel mathematical framework to model the intertwined coevolution of opinions and decision-making in a complex social system. In the proposed framework, the members of a social community update their opinions and revise their actions as they learn of others'...
Article
To date, the only effective means to respond to the spreading of the COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reducing their social and economic consequences. Here, we introduce a meta-population m...
Article
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 im...
Preprint
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 im...
Article
We study the asymptotic behavior of deterministic, continuous-time imitation dynamics for population games over networks. The basic assumption of this learning mechanism --- encompassing the replicator dynamics --- is that players belonging to a single population exchange information through pairwise interactions, whereby they get aware of the acti...
Preprint
Full-text available
To date, the only effective means to respond to the spreading of COVID-19 pandemic are non-pharmaceutical interventions (NPIs), which entail policies to reduce social activity and mobility restrictions. Quantifying their effect is difficult, but it is key to reduce their social and economical consequences. Here, we introduce a meta-population model...
Article
Full-text available
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 th...
Preprint
We study the asymptotic behavior of deterministic, continuous-time imitation dynamics for population games over networks. The basic assumption of this learning mechanism -- encompassing the replicator dynamics -- is that players belonging to a single population exchange information through pairwise interactions, whereby they get aware of the action...
Preprint
During the course of an epidemic, individuals constantly make decisions on how to fight against epidemic spreading. Collectively, these individual decisions are critical to the global outcome of the epidemic, especially when no pharmaceutical interventions are available. However, existing epidemic models lack the ability to capture this complex dec...
Article
Motivated by the literature on opinion dynamics and evolutionary game theory, we propose a novel mathematical framework to model the intertwined coevolution of opinions and decision-making in a complex social system. In the proposed framework, the members of a social community update their opinions and revise their actions as they learn of others’...
Article
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 propensities to interact with others. By linearizing the dynamics about...
Article
In this letter, we propose an epidemic model over temporal networks that explicitly encapsulates two different control actions. We develop our model within the theoretical framework of activity driven networks (ADNs), which have emerged as a valuable tool to capture the complexity of dynamical processes on networks, coevolving at a comparable time...
Article
The complexity of interaction patterns among individuals in social systems plays a fundamental role on the inception and spreading of epidemic outbreaks. Empirical evidence has shown that the network of social interactions may co-evolve with the spread of the disease at comparable time-scales. Time-varying features have also been documented in the...
Article
Social groups such as schools of fish or flocks of birds display collective dynamics that can be modulated by group leaders, which facilitate decision-making toward a consensus state beneficial to the entire group. For instance, leaders could alert the group about attacking predators or the presence of food sources. Motivated by biological insight...
Article
The problem of self-coordination of a network of dynamical systems toward a common state is often referred to as the consensus problem. In view of its wide range of applications, the consensus problem has been extensively studied in the last decades. However, most of the available results focus on static networks, challenging our mathematical under...
Article
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...
Preprint
We study the spread of a novel state in a network in the presence of an exogenous control. The controlled evolutionary dynamics model we study is a non-homogeneous Markov process describing the evolution of the states of all nodes in the network. Through a rigorous analysis of this system, we establish upper and lower bounds on the expected time ne...
Article
Full-text available
Abstract We deal with the problem of modeling and characterizing the community structure of complex systems. First, we propose a mathematical model for directed temporal networks based on the paradigm of activity driven networks. Many features of real-world systems are encapsulated in our model, such as hierarchical and overlapping community struct...
Article
Understanding the dynamics of social networks is the objective of interdisciplinary research ranging from animal collective behaviour to epidemiology, political science and marketing. Social influence is key to comprehending emergent group behaviour, but we know little about how inter-individual relationships emerge in the first place. We conducted...
Conference Paper
In this paper we deal with the problem of unveiling the community structure of a system in which the network of connections among components evolves in time. First, we propose a general and flexible model for temporal networks, based on the activity-driven network paradigm, which is capable of modeling both the temporal evolution of the system and...
Article
We consider a broad class of stochastic imitation dynamics over networks, encompassing several well known learning models such as the replicator dynamics. In the considered models, players have no global information about the game structure: they only know their own current utility and the one of neighbor players contacted through pairwise interact...
Article
Due to their wide adaptability to different application fields spanning from opinion dynamics to biology, the analysis of evolutionary dynamics is a compelling problem in the science of networks and systems. In this paper, we deal with controlled evolutionary dynamics in networks. We discuss a novel approach to model these phenomena, which enables...
Article
Network theory has greatly contributed to an improved understanding of epidemic processes, offering an empowering framework for the analysis of real-world data, prediction of disease outbreaks, and formulation of containment strategies.However, the current state of knowledge largely relies on time-invariant networks, which are not adequate to captu...
Article
In this paper, we present a novel estimation of the time to extinction of a Susceptible-Infected-Susceptible (SIS) epidemic model over a general network of interactions. Specifically, we prove that, for an effective infection rate above a threshold depending on the topology of the network, the time to extinction grows exponentially in the size of t...
Article
Imitation dynamics for population games are studied and their asymptotic properties analyzed. In the considered class of imitation dynamics - that encompass the replicator equation as well as other models previously considered in evolutionary biology - players have no global information about the game structure, and all they know is their own curre...
Conference Paper
We present a novel approach to model the diffusion of a mutant in a geographical network. This is a key issue in the control of epidemics, since many diseases are transmitted by intermediate hosts that could be substituted with genetically modified organisms (GMOs) with a mutation that prevents them from spreading the pathogen. The main strength of...
Article
Will a new smartphone application diffuse deeply in the population or will it sink into oblivion soon? To predict this, we argue that common models of spread of innovations based on cascade dynamics or epidemics may not be fully adequate. Therefore we propose a novel stochastic network dynamics modeling the spread of a new technological asset, whos...

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Project (1)
Project
We aim to investigate the spreading of infectious diseases using mathematical models of epidemics. Using a systems and control theoretic framework, we aim to better understand the key characteristics of the disease itself, the contact network, and social influences that determine the spreading dynamics. Finally, methods for controlling the spread will be devised to slow or stop epidemic outbreaks from occurring.