Social Confinement and Mesoscopic Localization of Epidemics on Networks

St-Onge, Guillaume; Thibeault, Vincent; Allard, Antoine; Dubé, Louis J.; Hébert‐Dufresne, Laurent

doi:10.1103/physrevlett.126.098301

Cited by 41 publications

(38 citation statements)

References 17 publications

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“…In Ref. [33], we investigate the impact of removing groups as a model of school closures and event cancellations. We find that delocalized dynamics are characterized by a linear relationship between outbreak size and the strength of our intervention, akin to mass-action models.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, in Sec. IV, we discuss possible extensions of our paper and some direct implications for the control of epidemics [33].…”

Section: Introductionmentioning

confidence: 98%

“…We present a complete analytical description of the mesoscopic localization regime, while we describe its impact on interventions in Ref. [33] to show how accounting for this localization regime is critical to our ability to control contagions on networks.…”

Section: Introductionmentioning

confidence: 99%

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Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks

et al. 2021

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Simple models of infectious diseases tend to assume random mixing of individuals, but real interactions are not random pairwise encounters: they occur within various types of gatherings such as workplaces, households, schools, and concerts, best described by a higher-order network structure. We model contagions on higher-order networks using group-based approximate master equations, in which we track all states and interactions within a group of nodes and assume a mean-field coupling between them. Using the susceptible-infected-susceptible dynamics, our approach reveals the existence of a mesoscopic localization regime, where a disease can concentrate and self-sustain only around large groups in the network overall organization. In this regime, the phase transition is smeared, characterized by an inhomogeneous activation of the groups. At the mesoscopic level, we observe that the distribution of infected nodes within groups of the same size can be very dispersed, even bimodal. When considering heterogeneous networks, both at the level of nodes and at the level of groups, we characterize analytically the region associated with mesoscopic localization in the structural parameter space. We put in perspective this phenomenon with eigenvector localization and discuss how a focus on higher-order structures is needed to discern the more subtle localization at the mesoscopic level. Finally, we discuss how mesoscopic localization affects the response to structural interventions and how this framework could provide important insights for a broad range of dynamics.

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Section: Discussionmentioning

confidence: 99%

“…Finally, in Sec. IV, we discuss possible extensions of our paper and some direct implications for the control of epidemics [33].…”

Section: Introductionmentioning

confidence: 98%

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Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks

et al. 2021

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“…Therefore, a mesoscopic simulation approach is described that offers the potential to serve as a continuum from macroscopic to microscopic levels, whose configurations can be customized based on data availability, desired modeling accuracy, targeted level of behavioral detail, and other such factors. Building over this mesoscopic framework, an incremental simulation approach based on what-if tree evolution is presented that offers new scaling capabilities that were not possible before in rapidly simulating thousands or millions of incrementally-varied scenarios over a large domain of a base simulation [2,25,26]. The mesoscopic model can be used in the incremental what-if tree evaluation on state-of-the-art accelerated computing platforms including supercomputers that offer thousands of GPUs, and effectively exploit the singleinstruction-multiple-data (SIMD) mode of high-performance parallel computing.…”

Section: Contributionsmentioning

confidence: 99%

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios

Perumalla

Alam

2021

Preprint

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In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what-if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that attempts to strike a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors such as at the individual entity level. The mesoscopic approach offers the possibility of incorporating complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also provides an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple data (SIMD). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what-if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what-if scenario tree evaluation has been implemented in software on modern GPUs. Synthetic simulation scenarios are explored and presented here to demonstrate the basic feasibility and computational characteristics of our approach. Results from the experiments on large population data illustrate the overall modeling methodology and computational run time performance on large numbers of synthetically generated what-if scenarios.

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“…The tremendous significance of analyzing and understanding the dynamics of epidemics is now abundantly clear in light of not only many major disease outbreaks of the past, but also with the most pronounced and consequential effects of COVID-19 worldwide 2,25,26 . Computer-based simulations are used for many purposes to deal with this problem, including prediction, confirmation, validation, exploration, enhancing our understanding, establishing limits, and so on.…”

Section: Introductionmentioning

confidence: 99%

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

Perumalla

Alam

2021

J Indian Inst Sci

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In simulation-based studies and analyses of epidemics, a major challenge lies in resolving the conflict between fidelity of models and the speed of their simulation. Another related challenge arises in dealing with the large number of what–if scenarios that need to be explored. Here, we describe new computational methods that together provide an approach to dealing with both challenges. A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. The mesoscopic approach offers the ability to incorporate complex compositions of multiple layers of dynamics even while retaining the potential for aggregate behaviors at varying levels. It also is an excellent match to the accelerator-based architectures of modern computing platforms in which graphical processing units (GPUs) can be exploited for fast simulation via the parallel execution mode of single instruction multiple thread (SIMT). The challenge of simulating a large number of scenarios is addressed via a method of sharing model state and computation across a tree of what–if scenarios that are localized, incremental changes to a large base simulation. A combination of the mesoscopic modeling approach and the incremental what–if scenario tree evaluation has been implemented in the software on modern GPUs. Synthetic simulation scenarios are presented to demonstrate the computational characteristics of our approach. Results from the experiments with large population data, including USA, UK, and India, illustrate the modeling methodology and computational performance on thousands of synthetically generated what–if scenarios. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

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Social Confinement and Mesoscopic Localization of Epidemics on Networks

Cited by 41 publications

References 17 publications

Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks

Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs

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