Epidemic Diffusion Research Articles

Preserving system activity while controlling epidemic spreading in adaptive temporal networks

Human behavior strongly influences the spread of infectious diseases: understanding the interplay between epidemic dynamics and adaptive behaviors is essential to improve response strategies to epidemics, with the goal of containing the epidemic while preserving a sufficient level of operativeness in the population. Through activity-driven temporal networks, we formulate a general framework which models a wide range of adaptive behaviors and mitigation strategies, observed in real populations. We analytically derive the conditions for a widespread diffusion of epidemics in the presence of arbitrary adaptive behaviors, highlighting the crucial role of correlations between agents behavior in the infected and in the susceptible state. We focus on the effects of sick leave, comparing the effectiveness of different strategies in reducing the impact of the epidemic and preserving the system operativeness. We show the critical relevance of heterogeneity in individual behavior: in homogeneous networks, all sick-leave strategies are equivalent and poorly effective, while in heterogeneous networks, strategies targeting the most vulnerable nodes are able to effectively mitigate the epidemic, also avoiding a deterioration in system activity and maintaining a low level of absenteeism. Interestingly, with targeted strategies both the minimum of population activity and the maximum of absenteeism anticipate the infection peak, which is effectively flattened and delayed, so that full operativeness is almost restored when the infection peak arrives. We also provide realistic estimates of the model parameters for influenza-like illness, thereby suggesting strategies for managing epidemics and absenteeism in realistic populations. Published by the American Physical Society 2024

Epidemics and society — A multiscale vision from the small world to the globally interconnected world

This paper shows how a new theory of epidemics can be developed for viral pandemics in a globally interconnected world. The study of the in-host dynamics and, in parallel, the spatial diffusion of epidemics defines the goal of our work, which looks ahead to new mathematical tools to model epidemics beyond the traditional approach of population dynamics. The approach takes into account the evolutionary nature of the virus, which can generate pseudo-Darwinian mutations and selection, while learning the presence of the virus and activating adaptive immunity. The study of immune competition plays a key role in both the in-host dynamics and the contagion dynamics.

Imperfect Vaccination Evolutionary Game Incorporating Individual Social Difference and Subjective Perception

Previous studies have focused on the assumption that individual attitudes toward epidemics, epidemic-related information, and vaccination are identical with much less effort attempting to consider psychological factors and individual heterogeneity. In this article, we employ a two-layer susceptible–infected–recovered–vaccinated/unaware–aware–unaware (SIRV-UAU) coupled network to depict the interplay between epidemic spreading and information diffusion. We propose an imperfect vaccination evolutionary game model integrating prospect theory (PT) to explore the effect of individual subjective perception and social difference on epidemic spreading and vaccination equilibrium. We find that the individual social difference in the epidemic spreading layer has a more significant impact on the epidemic threshold than that in the information diffusion layer. Individual psychological perception and vaccination behavior decision-making determine the vaccination equilibrium: under the PT, vaccination equilibrium increases with the decrease of the rationality coefficient when the vaccination cost is small. Furthermore, we analyze the effect of social differences on individual vaccination behavior decision-making and find that vaccination equilibrium increases with the increase of social reinforcement strength and primary protective probability. Finally, we discuss the effect of infection cost on vaccination equilibrium when the infection cost of vaccinated individuals is less than that of unvaccinated individuals and observe that a small infection cost helps improve the vaccination equilibrium.

TrajSGAN: A Semantic-Guiding Adversarial Network for Urban Trajectory Generation

Simulating human mobility contributes to city behavior discovery and decision-making. Although the sequence-based and image-based approaches have made impressive achievements, they still suffer from respective deficiencies such as omitting the depiction of spatial properties or ordinal dependency in trajectory. In this article, we take advantage of the above two paradigms and propose a semantic-guiding adversarial network (TrajSGAN) for generating human trajectories. Specifically, we first devise an attention-based generator to yield trajectory locations in a sequence-to-sequence manner. The encoded historical visits are queried with semantic knowledge (e.g., travel modes and trip purposes) and their important features are enhanced by the multihead attention mechanism. Then, we designate a rollout module to complete the unfinished trajectory sequence and transform it into an image that can depict its spatial structure. Finally, a convolutional neural network (CNN)-based discriminator signifies how “real” the trajectory image looks, and its output is regarded as a reward signal to update the generator by the policy gradient. Experimental results show that the proposed TrajSGAN model significantly outperforms the benchmarks under the MTL-Trajet mobility dataset, with the divergence of spatial-related metrics such as radius of gyration and travel distance reduced by 10%–27%. Furthermore, we apply the real and synthetic trajectories, respectively, to simulate the COVID-19 epidemic spreading under three preventive actions. The coefficient of determination metric between real and synthetic results achieves 91%–98%, indicating that the synthesized data from TrajSGAN can be leveraged to study the epidemic diffusion with an acceptable difference. All of these results verify the superiority and utility of our proposed method.

Comparing micro-level and macro-level models for epidemic diffusion in the metro system

ABSTRACT Few studies focus on how the ground transport system has increased COVID-19 transmission; the details of its spread remain unclear. The absence of station-level data obstructs healthcare professionals from effectively targeting anti-epidemic measures. This study employs agent-based modeling through GAMA software to identify Taipei metro stations implicated in initial transmission. In addition, a macro-level estimator is applied as a baseline model to compare COVID-19 arrival sequences at each station. Utilizing electronic metro ticket data, passenger travel patterns are discerned. We found (1) the average infection order of all stations, according to both models were not significantly different; (2) however, this difference between two model results became significant when the sample size was decreased. (3) Of all the stations, Taipei Main Station was the first because it has the highest passenger volume and the most connections. These early infected stations are near Taipei Main station and commercial or hub stations.

Modeling Human Temporal and Spatial Structured Contacts for Epidemic Prediction

Human travel and social contacts are deemed as the key force in driving the transmission of infectious diseases. Consequently, epidemic models should represent individuals’ spatiotemporal contacts in detail to investigate the complex process of epidemic diffusion. Mathematical epidemic models usually assume a homogeneous human population and describe unstructured contacts. Most network-based epidemic models do not synthetically consider the temporal and spatial features of human structured contact behaviors. We here combined a social network with a bipartite network to build a multilayer agent network to model heterogeneous individuals’ temporal and spatial structured contacts. We used the largest collective outbreak of H1N1 influenza at a Chinese university in 2009 as a case study. Experimental results indicate that our models can reproduce individuals’ daily travel and social contact patterns, as well as the H1N1 influenza outbreak. We found that only quarantining dormitories to stop interbuilding transmission could not achieve a great effect in mitigating epidemic outbreaks at a university. The prohibition of students’ visiting across dormitory rooms was indispensable to prevent intrabuilding transmission of infectious diseases. Furthermore, it would be better to quarantine admitted case patients’ close contacts to control potentially latent individuals.

Temporal clustering of social interactions trades-off disease spreading and knowledge diffusion.

Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating 'social bubbles' with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.

Use of migration and mobility data in COVID-19 response: Evidence from the East Africa Community region

COVID-19 pandemic has given rise to unprecedented challenges to global health and mobility. A valuable lesson from this recent pandemic is that migration statistics can be relied on to illuminate the spread of an epidemic and model diffusion patterns once a highly contagious virus is detected in a country. This study reviews literature published between 2020 and 2021, giving insights into the generation and use of migration and mobility data in COVID-19 response in the East Africa Community (EAC). The reviewed studies regarding the EAC Regional COVID-19 Response Plan all point to the need for timely data, but do not specify requirements for mobility and migration statistics. Several studies featured in this review propounded innovative ways to obtain and use the data in COVID-19 modeling. The study concludes that there is potential for use of migration statistics in future pandemic response plans and recommends that the EAC mainstreams migration statistics within the pandemic response processes.

The dynamic coupling mechanism of information and epidemic propagations considering individual mobility in two-layer network framework

The epidemic had taken massive hurt on human beings for their lethal viruses and rapid spread, such as the outbreak of Corona Virus Disease 2019 (Covid-19). How to control the virus spreading came to be a tough question and drew a lot of research attentions at that time. By the empirical data analysis from Shanghai China, we find information diffusion is co-evolving with epidemic spread, and vice versa. In order to reveal the coupling mechanism of epidemic spread and information diffusion, or the influencing factors on epidemic spread, we present a two-layer network framework to investigate the coupling mechanism of information diffusion and epidemic spread, and discuss the influencing factors. Thus, from the perspective of considering individual behaviors (such as individual mobility), a coupling propagation model of information diffusion and epidemic spread in two-layer network framework is proposed by dynamic equations. With the theoretical and numerical analysis, it is found epidemic spread might be influenced by epidemic itself mechanism and individual mobility indicated by mobility frequency and range to a certain extent from the micro perspective. Moreover, the role of individual mobility in promoting or delaying epidemic spread depends on the distribution of epidemic network. While from the macro perspective, it is found that promoting information diffusion in social networks could reduce the epidemic spread scales effectively by calculating the epidemic threshold and comparing with Monte Carlo (MC) simulation experiments. In addition, the global control strength from government or social organizations is also a key factor for reducing epidemic spread. Results in this work provide some reasonable and practical strategies for management on epidemic prevention and control in future.

Kinetic models for systems of interacting agents with multiple microscopic states

We propose and investigate general kinetic models with transition probabilities that can describe the simultaneous change of multiple microscopic states of the interacting agents. These models can be applied to many problems in socio-economic sciences, where individuals may change both their compartment and their characteristic microscopic variable, as for instance kinetic models for epidemic diffusion or for international trade with possible transfers of agents. Mathematical properties of the kinetic model are proved, as existence and uniqueness of a solution for the Cauchy problem in suitable Wasserstein spaces. The quasi-invariant asymptotic regime, leading to simpler kinetic Fokker–Planck-type equations, is investigated and commented on in comparison with other existing models. Some numerical tests are performed in order to show the time evolution of distribution functions and of meaningful macroscopic fields, even in case of non-constant interaction rates and transfer probabilities.

Multifractal scaling analyses of the spatial diffusion pattern of COVID-19 pandemic in Chinese mainland

Revealing spatio-temporal evolution regularity in the spatial diffusion of epidemics is helpful for preventing and controlling the spread of epidemics. Based on the real-time COVID-19 datasets by prefecture-level cities, this paper is devoted to exploring the multifractal scaling in spatial diffusion pattern of COVID-19 pandemic and its evolution characteristics in Chinese mainland. The ArcGIS technology and box-counting method are employed to extract spatial data and the least square regression based on rescaling probability (μ-weight method) is used to calculate fractal parameters. The results show multifractal distribution of COVID-19 pandemic in China. The generalized correlation dimension spectrums are inverse S-shaped curves, but the fractal dimension values significantly exceed the Euclidean dimension of embedding space when moment order q«0. The local singularity spectrums are asymmetric unimodal curves, which slant to right. The fractal dimension growth curves are shown as quasi S-shaped curves. From these spectrums and growth curves, the main conclusions can be drawn as follows: First, self-similar patterns developed in the process of COVID-19 pandemic, which seems to be dominated by multifractal scaling law. Second, the spatial pattern of COVID-19 across China can be characterized by global clustering with local disordered diffusion. Third, the spatial diffusion process of COVID-19 in China experienced four stages, i.e., initial stage, the rapid diffusion stage, the hierarchical diffusion stage, and finally the contraction stage. This study suggests that multifractal theory can be utilized to characterize spatio-temporal diffusion of COVID-19 pandemic, and the case analyses may be instructive for further exploring natural laws of spatial diffusion.

Intuitionistic fuzzy multi-objective transportation model during pandemic COVID-19

The given paper discusses an intuitionistic fuzzy discrete time-space optimization model while taking into account the COVID-19 pandemic. The novelty of this study is characterized by the proposal of a distinctive multi-objective intuitionistic fuzzy transportation model in which the demand function varies with time as the number of infected, exposed and susceptible persons increase. Forecasting of demand is executed using the epidemic diffusion model. Also, a new approach to defuzzify triangular intuitionistic fuzzy numbers, depending upon the concept of the golden ratio, is presented. Intuitionistic fuzzy programming approach is used to obtain the pareto-optimal solution. A real-life numerical illustration is explained using the proposed methodology to examine its practical suitability. Outperforming the existing traditional method of solving a transportation problem verifies its proficiency.

How uncertainty shapes herding in the corporate use of artificial intelligence technology

In its recent form, Artificial intelligence (AI) is an ensemble of technologies, which can be defined as machine-based systems for effective enterprise automation and influential decisions”. If businesses that use AI can potentially reap a competitive advantage, the optimal exploitation of such a complex ensemble of technologies remains uncertain as well as requires to have competitive access to complements such as data or new skills. Existing models of organizational use of technologies often ignore either the dynamics of competitive interactions (which can lead to pre-emption or epidemic diffusion) or the role of uncertainty, or both. In the case of AI, one type of uncertainty is particularly important: uncertainty about the technology's use cases (i.e., what to do with it). This paper proposes to apply a real options perspective to the Technology-Organization-Environment (TOE) adoption framework in order to uncover important patterns in the use of AI among firms. The results are threefold: (1) we find evidence of significant epidemic effects in AI use, (2) uncertainty moderates epidemic effects, and (3) the impact of uncertainty depends on its source: an uncertain AI use case reduces herd behaviours while uncertainty about use case returns still favours them. These results highlight the importance of exploration and collective learning in the diffusion of emerging and complex technologies, especially when companies struggle to identify the most profitable use cases for the technology.

Ecology and space in the COVID-19 epidemic diffusion: a multifactorial analysis of Italy’s provinces

ABSTRACT This study aimed to identify the ecological and spatial correlates of the COVID-19 secondary transmission mechanisms. The dataset comprised cross-province daily COVID-19 cases recorded in Italy from 25 February to 26 May 2020. Several physical and socio-economic variables and a broad set of spatial matrices were employed to explain epidemic spreading. First, the serial transmission interval was identified through autoregressive models. Second, the impact of the ecological and spatial variables on the epidemic’s secondary transmission was analysed by means of ordinary least squares and spatial regression models. The study found that, against widespread belief, the epidemic diffusion was higher in the more affluent and developed territorial units. As for the spatial component, a secondary transmission spatial spillover emerged. Contiguity and distance but also mobility fluxes proved to be significant in this transmission. This study’s contribution is methodological and empirical. As for methodology, it shows that spatial models can be used not only to identify epidemic localisation but also to predict epidemic transmission, provided the problem of the multidirectional nature of spatial dependence is adequately dealt with. Moreover, this study shows that no satisfactory understanding of the epidemic would be achieved without considering both ecological and spatial determinants. As for the empirical aspect, it was found that the main contribution to epidemic spreading came from the anthropologic context rather than the physical one. In particular, direct and indirect indicators of mobility, transport and social interaction emerged as crucial for the epidemic transmission.

The "Bald Disease" of the Sea Urchin Paracentrotus lividus: Pathogenicity, Molecular Identification of the Causative Agent and Therapeutic Approach.

In recent decades, various species of Mediterranean sea urchins, including Paracentrotus lividus, have been subject to widespread seasonal episodes of mass mortality whose causative agents are still unclear. In particular, P. lividus is subject to late winter events of mortality, due to a disease manifested by a massive loss of spines and the presence of greenish amorphous material on the tests (i.e., the sea urchin skeleton consisting of spongeous calcite). Documented mortality events show a seasonal epidemic diffusion and might produce economic losses also in aquaculture facilities, besides the environmental constraints to its diffusion. We collected individuals showing conspicuous lesions on the body surface and reared them in recirculated aquaria. Samples of external mucous were collected along with coelomic liquids and cultured to isolate bacterial and fungal strains, further submitted to molecular identification through the amplification of prokaryotic 16S rDNA. In addition, pools of infected sea urchins were reared in recirculated tanks after short baths in a formulated therapeutic compound and their survival rates were compared to non-treated individuals for variable periods. Here, we aimed at a redescription of the etiopathogenetic nature of the parasites and tested the efficacy of a possible treatment, to be proposed for aquaculture purposes.

Weibull dynamics and power-law diffusion of epidemics in small world 2D networks

We demonstrate that the Weibull statistics characterize the time dependence of the epidemic spread in several idealized cases on 2-D Cartesian lattices, with and without small world structure. These cases include infection from nearest and next nearest neighbors; infection from long-distant neighbors; infection due to external influence. The Weibull closed form for the aggregated disease propagation is used as an analytic tool to elucidate the effect of small world topology in the diffusion scaling. It may be also used to formalize and contain the extensive diversity of real life cases. Along these lines, besides the straightforward implementation of the studied cases in epidemiology, our work proposes a power-law scaling in the Bass model for applications in the diffusion of the technological innovation.

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease.

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

Generating mobility networks with generative adversarial networks

The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people’s movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.

The impact of positive and negative information on SIR-like epidemics in delayed multiplex networks.

In order to better study the interaction between epidemic propagation and information diffusion, a new coupling model on multiplex networks with time delay is put forward in this paper. One layer represents the information diffusion about epidemics. There is not only information about the positive prevention of infectious diseases but also negative preventive information. Meanwhile, the dissemination of information at this layer will be influenced by the mass media, which can convey positive and reliable preventive measures to help the public avoid exposure to contagion. The other layer represents the transmission of infectious diseases, and the public in this layer no longer only exchange information related to infectious diseases in the virtual social network like the information layer but spread infectious diseases through contact among people. The classical SIR model is used to model for epidemic propagation. Since each infected individual needs to spend enough time to recover, the infected one at one time does not necessarily change to the recovered one at the next time, so time delay is an essential factor to be considered in the model. Based on the microscopic Markov chain approach, this paper obtains an explicit expression for epidemic threshold in the two-layered multiplex networks with time delay, which reveals some main factors affecting epidemic threshold. In particular, the time delay has a noticeable effect on the epidemic threshold to some extent. Finally, the influence of these main factors on the epidemic threshold and their interaction are proved through numerical simulations.

Mixed Game-Based AoI Optimization for Combating COVID-19 With AI Bots

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings.