The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In ...multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity.
Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions.
The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion.
The forecasting of the temporal evolution of real-world diseases is systematically performed via mean-field, compartmental models, such as the SIR model and its various variants. Here, we investigate ...the spreading of SIR dynamics over a system of interacting, active agents – whose dynamics generate random contacts among the agents, as well as temporal and spatial correlation and fluctuations – to assess whether mean-field-like SIR models are able to accurately describe the temporal evolution of an epidemics. We find that such models display temporal dynamics that are intrinsically and fundamentally different from the ones obtained in active agent simulations. Specifically, we numerically prove that no effective mean-field SIR model is consistent with the dynamics that emerge in agent-based simulations. Our results call into question the use of such models to forecast the evolution of real-world epidemics, independently of the method used to estimate the basic reproduction number.
•A novel epidemic model integrating awareness diffusion and epidemic propagation is proposed on the basis of multiplex networks.•A threshold-based model is used to characterize the diffusion of ...awareness, and the process of epidemic propagation is described by using SIR model.•The epidemic threshold is analytically derived through the micro-Markov chain approach.
The epidemic diseases have been threatening to human health, and it is of high importance to understand the properties of epidemic propagation among the population will help us to take some effective measures to prevent and control epidemic spreading. In this paper, we propose a novel epidemic model by using two-layer multiplex networks to investigate the multiple influence between awareness diffusion and epidemic propagation, where the upper layer represents the awareness diffusion regarding epidemics and the lower layer expresses the epidemic propagation. In the process of awareness diffusion, the unaware individuals will be aware of the epidemics if the ratio between their awareness neighbors and their degrees reaches the specified ratio. For the epidemic spreading in the lower layer, we use the classical SIR(susceptible-infected-recovered) model. We derive the epidemic threshold by using Micro-Markov chain approach. The analytical results indicate that the epidemic threshold is correlated with the awareness diffusion as well as the topology of epidemic networks. Finally, the simulation results further demonstrate the properties of epidemic propagation and validate the analytical results.
In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that ...provides a theoretical framework to investigate its spread within a community. Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically. To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by China, South Korea, India, Australia, USA, Italy and the state of Texas in the USA. The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot. Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions. Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures. We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease.
•COVID-19.•SIR-model reducible to logistic regression.•Forecast uncertainty quantification.•Revealing effect of epidemic prevention measures.
Basing on existence of the mathematically sequential ...reduction of the three-compartmental (Susceptible-Infected-Recovered/Removed) model to the Verhulst (logistic) equation with the parameters determined by the basic characteristic of epidemic process, this model is tested in application to the recent data on COVID-19 outbreak reported by the European Centre for Disease Prevention and Control. It is shown that such a simple model adequately reproduces the epidemic dynamics not only qualitatively but for a number of countries quantitatively with a high degree of correlation that allows to use it for predictive estimations. In addition, some features of SIR model are discussed in the context, how its parameters and conditions reflect measures attempted for the disease growth prevention that is also clearly indicated by deviations from such model solutions.
Identifying influential nodes in complex networks persists as a crucial issue due to its practical applications in the real world. The propagation model is a special method for identifying ...influential nodes based on propagation dynamics. However, most of propagation-based methods have not delved deeply into the impact of network topology on the propagation process. In this paper, we propose a method based on the dynamic propagation probability model, called DPP. The main idea of this method is to characterize the impact of a node on the basis of its propagation capacity during propagation process by using dynamic propagation probability within its three level neighborhood. This new metric redefines the propagation probability of neighbors by refining the propagation process, which allows the propagation probability to be transmitted in accordance with the network structure. To validate the performance of the proposed method, we compare with eight different methods from four aspects in 11 real-world networks. The experimental results demonstrate that the DPP method has good performance in most cases.
•A method based on the dynamic propagation probability model is proposed to identify influential nodes in complex networks.•It measures the impact of a node on the basis of its propagation capacity during propagation process by using dynamic propagation probability within its three level neighborhood.•The performance of the proposed method is validated from four aspects in different real-world networks.
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the ...Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts. Despite the relatively rich surveillance data and the recurrent nature of seasonal influenza, forecasting the timing and intensity of seasonal influenza in the U.S. remains challenging because the form of the disease transmission process is uncertain, the disease dynamics are only partially observed, and the public health observations are noisy. Fitting a probabilistic state-space model motivated by a deterministic mathematical model a susceptible-infectious-recovered (SIR) model is a promising approach for forecasting seasonal influenza while simultaneously accounting for multiple sources of uncertainty. A significant finding of this work is the importance of thoughtfully specifying the prior, as results critically depend on its specification. Our conditionally specified prior allows us to exploit known relationships between latent SIR initial conditions and parameters and functions of surveillance data. We demonstrate advantages of our approach relative to alternatives via a forecasting comparison using several forecast accuracy metrics.
•We build a new analytical framework for the information spreading called “awareness” combined with the susceptible-infected-recovered (SIR) model.•Our model can evaluate the effect of awareness ...along with a SIR epidemic model to reduce the effect of contagious disease.•We obtain the phase diagram for different parameter comparison to show the effect of awareness.
The information spreading of awareness can prompt the manners of human to ease the infectious possibility and assist to recover swiftly. A dynamic system of Susceptible-Infected-Recovered (SIR) with Unaware-Aware (UA) process (SIR-UA) is newly developed by using compartment model through analytical approach with assumption of an infinite and well-mixed population. Moreover, individuals in a population can be classified into six states as unaware susceptible(SU), aware susceptible(SA), unaware infected(IU), aware infected(IA), unaware recovered(RU), and aware recovered(RA). Compared with previous models, the new dynamic set of equations described the more widespread situation and incorporated all possible states of Unaware-Aware (UA) with SIR process. The effect of awareness is explored carefully to show the significance on epidemic model with time steps. Consequently, the properties of parameters on the epidemic awareness model are studied to deliberate different physical situations. Finally, full phase diagrams are explored to show the epidemic sizes of susceptible and recovered individuals for various parameters.
PDF
