Epidemic Diffusion Research Articles

Cyber Physical Systems Dependability Using CPS-IOT Monitoring.

Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called “INMP”, which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.

The impact of awareness diffusion on the spread of COVID-19 based on a two-layer SEIR/V-UA epidemic model.

In this paper, we propose a new susceptible–vaccinated–exposed–infected–recovered with unaware–aware (SEIR/V‐UA) model to study the mutual effect between the epidemic spreading and information diffusion. We investigate the dynamic processes of the model with a Kinetic equation and derive the expression for epidemic stability by the eigenvalues of the Jacobian matrix. Then, we validate the model by the Monte Carlo method and numerical simulation on a two‐layer scale‐free network. With the outbreak of COVID‐19, the spread of the epidemic in China prompted drastic measures for transmission containment. We examine the effects of these interventions based on modeling of the information‐epidemic and the data of the COVID‐19 epidemic case. The results further demonstrate that the epidemic spread can be affected by the effective transmission rate of awareness.

Interplay between epidemic spread and information diffusion on two-layered networks with partial mapping

We propose an epidemic model to investigate the interplay between disease spread and information diffusion on two-layered networks, in which there exists the mapping relationship among only partial node pairs. In the model, one layer characterizes the spread of epidemics in the physical contact network, while the other layer represents the spread of epidemic-related prevention information in the social network. If a pair of nodes have the correspondence, the individual with knowledge of prevention information will take effective measures to avoid being infected; otherwise, the epidemic and related information will independently spread on the corresponding network. Then, we build the dynamical equations for the epidemic dynamics by using the micro Markov chain (MMC) method, and the epidemic threshold can be calculated on the basis of adjacent matrix constructed from the contact network. The analytical results indicate that the correspondence rate between node pairs on two-layered networks can significantly influence this threshold. Finally, by comparing the MMC and Monte Carlo simulation results, it is found that they are highly consistent and thus MMC method can be used to predict the outbreak of epidemics.

Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks

The coronavirus disease 2019 (COVID-19) has been widely spread around the world, and the control and behavior dynamics are still one of the important research directions in the world. Based on the characteristics of COVID-19's spread, a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information. In the layer of epidemic spreading, the nodes can be divided into five categories, where the topology of the network represents the physical contact relationship of the population. The topological structure of the upper network shows the information interaction among the nodes, which can be divided into aware and unaware states. Awareness will make people play a positive role in preventing the epidemic diffusion, influencing the spread of the disease. Based on the above model, we have established the state transition equation through the microscopic Markov chain approach (MMCA), and proposed the propagation threshold calculation method under the epidemic model. Furthermore, MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network, respectively. The current results will be beneficial to the study of COVID-19, and propose a more rational and effective model for future research on epidemics.

Special issue on: The economics of Covid and other pandemics.

As a contribution to the ongoing worldwide economic research on epidemics in general and on the current Covid-19 pandemic in particular, the Journal of Public Economic Theory is inviting submissions related to the numerous public economics issues acutely raised by the current health crisis. The following topics are clearly of interest (but consideration will not be exclusively limited to these): Normative versus positive foundations of epidemic control policies; Optimal design of health systems in the face of epidemic uncertainty; Network diffusion of epidemics and epidemic control on networks; Game-theoretic frameworks for the analysis of epidemic diffusion and epidemic control; Private and social incentives for prevention and containment. Submitted papers need not be fully theoretical, although as per usual JPET guidelines, a significant theoretical component is expected for publication. Timeline: Submission deadline: August 1, 2021. Publication of the special issue: 2022.

A method based on Graph Theory and Three Way Decisions to evaluate critical regions in epidemic diffusion:: An analysis of COVID-19 in Italy.

The paper reports the results of an analysis of COVID-19 diffusion in Italy. The analysis was carried out with a new method based on the combined use of a 3 Way Decisions model and graph theory. Specifically, the data about infected people in the Italian regions is assessed by means of an evaluation function which allows the tri-partitioning of Italy and the identification of high, medium or low critical regions. The tri-partition is performed, along the temporal evolution of the COVID-19 diffusion, by calculating two threshold values which take into account the containment actions that, from time to time, the decision makers have implemented. The effects of a containment action are related to a reduction in the centrality value of a region. To estimate the effect of containment actions, we evaluated two approaches. The first is based on a uniform reduction in the centrality values of the regions, the second estimates the effects of containment actions starting from the mobility changes data provided by the Google Community Mobility reports. The results of our evaluation based on real data of the COVID-19 diffusion in Italy are encouraging and represent a good starting point for future extensions of the method.

Abrupt epidemic outbreak could be well tackled by multiple pre-emptive provisions-A game approach considering structured and unstructured populations

Forecasting the dynamics of flu epidemics could be vital for policy-making concerning the allocation of public health resources. Reliable predictions about disease transmission networks also help fix the benchmark for reconciling diverse aspects to the decision-makers while selecting and implementing a suitable health intervention. To this aim, we propose an SIR/VM epidemic game model to reveal the dynamic evolution of intervention policies, entangling social feedbacks, behavioral responses, and viral transmission on several network topologies into a single framework. Besides vaccination, this study introduces intermediate defense measures (IDM) as an alternative provision to restraint the epidemic resurgence in structured and unstructured populations. Although heterogeneity is a commonly observed phenomenon in human populations, many antecedent studies typically preferred homogenous networks. Here, we investigate the disparities found in epidemic diffusion within homogeneous and heterogeneous networks, employing a mean-field approximation and multi-agent simulation, respectively. Additionally, we explore both network types simultaneously and justify their potential impacts on control provisions’ success. As a general tendency, vaccination and IDM complement each other within the entire parametric regions. Our study elucidates the coexistence of multiple policies as well as the abrupt emergence of stain points adopting several network topologies. A careful investigation on stain points reveals that hub agents solely rely on free-riding brings the endemic state of an epidemic, triggered by a sudden extinction of vaccinators and self-protectors. The emergence of too many self-interested people spoils the herd immunity state and initiates the outbreaks, heavily observed in well-mixed and scale-free networks. On the other hand, the coexistence of policies occurs mostly in the networks mentioned above but rarely seen in the lattice network. The robustness of the proposed model has been tested by adding mean-field theoretical results for a nonspatial population and agent-based simulated outcomes for spatial populations under a wide variety of parametric conditions. Model outcomes confirm that the game-payoff regulates the epidemic dynamics, while the epidemic propagation governs individuals’ health status. Moreover, we expose a complex interplay between cost and efficacy of control provisions and justify keeping the provisional costs reasonably lower would be an ultimate challenge to maintain disease attenuation. The central theme of this paper is thus to portray the holistic summary of epidemic prevalence and the relative contribution of each intervention to epidemic remission.

Temporal trend in the compensation claim applications for work-related COVID-19 in Italy.

Introduction:The SARS-CoV-2 pandemic is an impacting challenge for the occupational medicine and epidemiology. The identification of occupational groups most at risk of exposure is critical in contrasting and preventing the epidemic diffusion.Objective:To analyse the compensation claim applications collected by the Italian Workers’ Compensation Authority (INAIL) for identifying the occupational patterns and the temporal trend, in comparison with general population data.Methods:Daily time series of compensation claim applications for COVID-19 injuries in the period March-October 2020, has been analysed, estimating the epidemiological parameters of interest and comparing findings with general population data.Results:In the period March-October 2020, 65,804 compensation claim applications for COVID-19 have been collected by INAIL. The ratio between compensation claims applications and COVID-19 cases in the general population appears decreasing in the period, passing from 20% in the first pandemic period (March-April) to 3-4% in the September-October period. During the summer period, an increase in male and not Italian component and a decrease in mean age, Northern regions, and health care workers contributions to the occupational claims applications have been observed.Discussion:The comparative analyses between occupational and general population data allowed to describe how the so called “second wave” of COVID-19 epidemic in Italy involved the occupational settings less than the other risk factors. There is a need to go towards an occupational surveillance system for COVID-19 infection, with the aim of monitoring and preventing the occupational risk of infection, supporting insurance system effectiveness and managing vaccination policies.

Deciphering role of inter and intracity human dispersal on epidemic spread via coupled reaction-diffusion models

Human mobility has been significantly influencing public health since time immemorial. A susceptible-infected-deceased epidemic reaction diffusion network model using asymptotic transmission rate is proposed to portray the spatial spread of the epidemic among two cities due to population dispersion. Qualitative behaviour including global attractor and persistence property are obtained. We also study asymptotic behaviour of the whole network with the help of asymptotic behaviour at individual cities. The epidemic model shows up two equilibria, (i) the disease-free, and (ii) unique endemic equilibria. An expression that can be used to calculate the basic reproduction number for heterogeneous environment, for the entire network is obtained. We use graph theory to analyze the global stability of our diffusive two-city model. We also performed bifurcation analysis and discovered that endemic equilibrium changes stability via Hopf bifurcations. A significant reduction in the number of infectives were observed when proper migration rate is maintained between the cities. Numerical results are provided to illuminate and clarify theoretical findings. Simulation experiments for two-dimensional spatial models show that infectious populations will increase if contact heterogeneity is increased, but it will decline if infective populations perform more local random movement. We observe that infection risk may be understated if the parameters used to estimate the basic reproduction number remains unchanged through space or time.

Effects of epidemic prevention on the university management

The information transmission network is different from the physical contact network. It is of great significance to study the spread range of epidemic diseases by distinguishing the topological structure of perceptual information transmission network from that of physical contact disease diffusion network. SIR model is used to describe the transmission process of epidemic, and it is very important to explore the disease diffusion model which integrates perceptual transmission and disease diffusion. Furthermore, with a multi-layer network coupling the diffusion of perceptual information and the spread of disease, the relationship between different layers is the key element of the system model. Using multi-layer network to describe the system in the real world, through the introduction of individual awareness propagation mechanism, this paper studies the interaction between epidemic diffusion and awareness propagation in the framework of multiple networks, and establishes multiple policy adjustment rules to study the propagation dynamics of awareness in different networks. Considering the two-layer network, the first layer network is described as physical contact network, and epidemic diseases spread through the physical contact network, which affects the mutual transmission of information at the level of awareness network. The other layer is awareness communication network. It is an important task to study the complex interaction between human society and biological infectious diseases. In this work, we study the influence of awareness and behavior based on multiple networks on infection density. The university management should pay attention to topological structures of networks and the strategies.

An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak.

With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining Graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.

Quantitative effects of network connectivity on epidemics

Epidemics are affected by the connectivity of nodes in networks in addition to the cooperation of infection transmission. We investigate quantitatively the effects of node connectivity on transmission dynamics by comparing epidemic diffusion in null models with gradual connection strength. Results show that: (1) the inhomogeneity of network connectivity accelerates the spreading of epidemics, this phenomenon is more significant in the early stage of propagation; (2) the enhancement of connectivity of homogenous nodes restrains epidemic spreading, and the spreading speed correlates negatively with connection strength; (3) the spreading speed of epidemics does not change linearly with the strength of rich-club property, which means that the connectivity among hub nodes does not appreciably affect disease diffusion.

Measuring the Environmental and Social Risk of Exposure to Future Epidemics

One of the problems in a not-too-distance future is the possibility of preventing and/or containing new infectious disease outbreaks that generate negatively impact on public health and economy of countries. The current monitoring of transmission dynamics is mainly based on reproduction number and fatality rates. However, this approach is a real-time monitoring of transmission dynamics for containing the spread of pandemic and, as a consequence, numbers of COVID-19 related infected individuals and deaths. Reproduction number does not provide, ex-ante, information to cope with future epidemics. The main goal of this study is to propose the index c (as contagions) that measures, ex-ante, the environmental risk of exposure of cities and/or regions to future epidemics of COVID-19 and similar vital agents. In particular, this index c synthetizes environmental, socioeconomic and health risk factors of cities/regions of exposure to infectious diseases and has a range from 1 (environmental and social weakness leading to high levels of infected individuals and deaths) to 0 (environment that reduces the risk of infectious diseases in society). The statistical evidence here seems in general to support the predictive results of the index c as particularly simple but superior indicator in detecting the global correlation between potential risk of high exposure of cities/regions to infectious diseases and actual high numbers of COVID-19 related infected individuals and deaths of cities/regions. The index c can support a proactive strategy to help policymakers firstly to prevent infectious diseases and, in case of diffusion of epidemics, to constrain new infectious diseases similar to COVID-19 with appropriate measures and technologies on environment and society.

Potential role of particulate matter in the spreading of COVID-19 in Northern Italy: first observational study based on initial epidemic diffusion

ObjectivesA number of studies have shown that the airborne transmission route could spread some viruses over a distance of 2 meters from an infected person. An epidemic model based only...

Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading

The interaction between epidemic spreading and information diffusion is an interdisciplinary research problem. During an epidemic, people tend to take self-protective measures to reduce the infection risk. However, with the diffusion of rumor, people may be difficult to make an appropriate choice. How to reduce the negative impact of rumor and to control epidemic has become a critical issue in the social network. Elaborate mathematical model is instructive to understand such complex dynamics. In this paper, we develop a two-layer network to model the interaction between the spread of epidemic and the competitive diffusions of information. The results show that knowledge diffusion can eradicate both rumor and epidemic, where the penetration intensity of knowledge into rumor plays a vital role. Specifically, the penetration intensity of knowledge significantly increases the thresholds for rumor and epidemic to break out, even when the self-protective measure is not perfectly effective. But eradicating rumor shouldn’t be equated with eradicating epidemic. The epidemic can be eradicated with rumor still diffusing, and the epidemic may keep spreading with rumor being eradicated. Moreover, the communication-layer network structure greatly affects the spread of epidemic in the contact-layer network. When people have more connections in the communication-layer network, the knowledge is more likely to diffuse widely, and the rumor and epidemic can be eradicated more efficiently. When the communication-layer network is sparse, a larger penetration intensity of knowledge into rumor is required to promote the diffusion of knowledge.

Agents and robots for collaborating and supporting physicians in healthcare scenarios.

Monitoring patients through robotics telehealth systems is an interesting scenario where patients' conditions, and their environment, are dynamic and unknown variables. We propose to improve telehealth systems' features to include the ability to serve patients with their needs, operating as human caregivers. The objective is to support the independent living of patients at home without losing the opportunity to monitor their health status. Application scenarios are several, and they spread from simple clinical assisting scenarios to an emergency one. For instance, in the case of a nursing home, the system would support in continuously monitoring the elderly patients. In contrast, in the case of an epidemic diffusion, such as COVID-19 pandemic, the system may help in all the early triage phases, significantly reducing the risk of contagion. However, the system has to let medical assistants perform actions remotely such as changing therapies or interacting with patients that need support. The paper proposes and describes a multi-agent architecture for intelligent medical care. We propose to use the beliefs-desires-intentions agent architecture, part of it is devised to be deployed in a robot. The result is an intelligent system that may allow robots the ability to select the most useful plan for unhandled situations and to communicate the choice to the physician for his validation and permission.

Size and timescale of epidemics in the SIR framework

The most important features to assess the severity of an epidemic are its size and its timescale. We discuss these features in a systematic way in the context of SIR and SIR-type models. We investigate in detail how the size and timescale of the epidemic can be changed by acting on the parameters characterizing the model. Using these results and having as guideline the COVID-19 epidemic in Italy, we compare the efficiency of different containment strategies for contrasting an epidemic diffusion such as social distancing, lockdown, tracing, early detection and isolation.

Optimising data diffusion while reducing local resources consumption in Opportunistic Mobile Crowdsensing

The combination of Mobile Crowdsensing (MCS) with Opportunistic Networking (OppNet) allows mobile users to share sensed data easily and conveniently without the use of fixed infrastructure. OppNet is based on intermittent connectivity among wireless mobile devices, in which mobile nodes may store, carry and forward messages (sensing information) by taking advantage of wireless ad hoc communication opportunities. A common approach for the diffusion of this sensing data in OppNet is the epidemic protocol, which carries out a fast data diffusion at the expense of increasing the usage of local buffers on mobile nodes and also the number of transmissions, thereby limiting scalability.A way to reduce this consumption of local resources is to set a message expiration time that forces the removal of old messages from local buffers. Since dropping messages too early may reduce the speed of information diffusion, we propose a dynamic expiration time setting to limit this effect. Moreover, we introduce an epidemic diffusion model for evaluating the impact of the expiration time. This model allows us to obtain optimal expiration times that achieve performances similar to those other approaches where no expiration is considered, with a significant reduction of local buffer and network usage. Furthermore, in our proposed model, the buffer utilisation remains steady with the number of nodes, whereas in other approaches it increases sharply. Finally, our approach is evaluated and validated in a mobile crowdsensing scenario, where students collect and broadcast information regarding a university campus, showing a significant reduction on buffer usage and nodes message transmissions, and therefore, decreasing battery consumption.

An Evolutionary Perspective of Virus Propagation

This paper presents an evolutionary algorithm that simulates simplified scenarios of the diffusion of an infectious disease within a given population. The proposed evolutionary epidemic diffusion (EED) computational model has a limited number of variables and parameters, but is still able to simulate a variety of configurations that have a good adherence to real-world cases. The use of two space distances and the calculation of spatial 2-dimensional entropy are also examined. Several simulations demonstrate the feasibility of the EED for testing distinct social, logistic and economy risks. The performance of the system dynamics is assessed by several variables and indices. The global information is efficiently condensed and visualized by means of multidimensional scaling.

Positive explicit and implicit computational techniques for reaction\u2013diffusion epidemic model of dengue disease dynamics

The aim of this work is to develop a novel explicit unconditionally positivity preserving finite difference (FD) scheme and an implicit positive FD scheme for the numerical solution of dengue epidemic reaction–diffusion model with incubation period of virus. The proposed schemes are unconditionally stable and preserve all the essential properties of the solution of the dengue reaction diffusion model. This proposed FD schemes are unconditionally dynamically consistent with positivity property and converge to the true equilibrium points of dengue epidemic reaction diffusion system. Comparison of the proposed scheme with the well-known existing techniques is also presented. The time efficiency of both the proposed schemes is also compared, with the two widely used techniques.