Towards efficient large scale epidemiological simulations in EpiGraph

Martín, Gonzalo; Singh, David E.; Marinescu, Maria-Cristina; Carretero, Jesús

doi:10.1016/j.parco.2014.09.004

Cited by 13 publications

(15 citation statements)

References 27 publications

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“…In each time step, the algorithm considers each city in the simulated territory (line 2). A city has a given population which is modeled based on the Spanish census data 1 , with the associated social connections between the individuals. Line 5 updates the health status of each infected individual of each city, as indicated by the epidemic 1 National Statistics Institute (INE).…”

Section: Introductionmentioning

confidence: 99%

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area

Singh

Marinescu

Guzmán-Merino

et al. 2021

Front. Public Health

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This work presents simulation results for different mitigation and confinement scenarios for the propagation of COVID-19 in the metropolitan area of Madrid. These scenarios were implemented and tested using EpiGraph, an epidemic simulator which has been extended to simulate COVID-19 propagation. EpiGraph implements a social interaction model, which realistically captures a large number of characteristics of individuals and groups, as well as their individual interconnections, which are extracted from connection patterns in social networks. Besides the epidemiological and social interaction components, it also models people's short and long-distance movements as part of a transportation model. These features, together with the capacity to simulate scenarios with millions of individuals and apply different contention and mitigation measures, gives EpiGraph the potential to reproduce the COVID-19 evolution and study medium-term effects of the virus when applying mitigation methods. EpiGraph, obtains closely aligned infected and death curves related to the first wave in the Madrid metropolitan area, achieving similar seroprevalence values. We also show that selective lockdown for people over 60 would reduce the number of deaths. In addition, evaluate the effect of the use of face masks after the first wave, which shows that the percentage of people that comply with mask use is a crucial factor for mitigating the infection's spread.

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Section: Introductionmentioning

confidence: 99%

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area

Singh

Marinescu

Guzmán-Merino

et al. 2021

Front. Public Health

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“…Many solutions exploit parallelization at the distributed level in order to enhance performance. Martin et al present an MPI‐based epidemic simulator, EpiGraph, used to study the propagation of infectious diseases between regions due to people movements. EpiGraph can be executed efficiently both on clusters and shared‐memory nodes.…”

Section: Related Workmentioning

confidence: 99%

“…To summarize, some of the related work for executing epidemic simulations does not exploit clouds and their associated parallelization, cost, and agility benefits . Other related work exploits clouds but lacks support for elasticity, multi‐cloud deployment, fault tolerance, or required facilities beyond bag‐of‐tasks execution .…”

Section: Related Workmentioning

confidence: 99%

A service‐based framework for building and executing epidemic simulation applications in the cloud

Parlavantzas

Pham

Morin

et al. 2019

Concurrency and Computation

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The cloud has emerged as an attractive platform for resource-intensive scientific applications, such as epidemic simulators. However, building and executing such applications in the cloud presents multiple challenges, including exploiting elasticity, handling failures, and simplifying multi-cloud deployment. To address these challenges, this paper proposes a novel service-based framework called DiFFuSE that enables simulation applications with a bag-of-tasks structure to fully exploit cloud platforms. This paper describes how the framework is applied to restructure two legacy applications, simulating the spread of bovine viral diarrhea virus and Mycobacterium avium subspecies paratuberculosis, into elastic cloud-native applications. Experimental results show that the framework enhances application performance and allows exploring different cost-performance trade-offs while supporting automatic failure handling and elastic resource acquisition from multiple clouds. KEYWORDScloud computing, epidemic simulation, high performance computing, simulation models INTRODUCTIONAs global transport networks continue to expand, the risk of infectious disease transmission grows, making research in pathogen spread on humans and animals increasingly important. An essential tool for studying pathogen spread is computer simulation based on mechanistic modeling. However, running epidemic simulations at large scale is time consuming for multiple reasons. First, the simulation models can capture fine-grained dynamics, considering different population scales (individual, population, and meta-population) and accounting for multiple layouts (eg, combining population and infection dynamics with farm management decisions). 1 Second, some processes can be data-driven and exploit large amounts of real-life observed data, mostly concerning the population dynamics of hosts. Third, understanding the underlying biological system requires performing and comparing many simulation scenarios, eg, through a model sensitivity analysis. Finally, producing reliable results demands large numbers of simulation runs when models are stochastic.Speeding up the execution of large-scale simulations requires parallelism. Parallelism can be implemented at the single-computer level using multiple cores and processors as well as the distributed level using multiple computers interconnected through a network. There are currently three main options for obtaining the necessary computing resources for running simulations in parallel. The first option is using dedicated high-end multicore systems or clusters. The limitation is that such infrastructures are typically shared among many local users, resulting in long waiting times. At the same time, extending such infrastructures to increase user satisfaction is time consuming and costly. The second option is grid computing, which allows sharing computers from multiple clusters in multiple sites. The main limitation of grids is the heterogeneity of the software stacks in each cluster, which makes application deployment di...

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“…Table 1 shows the use cases' characteristics. EpiGraph [9] is a parallel and stochastic simulator of the propagation of the flu virus. It is currently configured to carry out the simulation in Bilbao, Spain, using an un-directed weighted graph of 703,258 nodes and 8,806,520 edges that corresponds to the individual-connections in the simulation.…”

Section: Experimental Environmentmentioning

confidence: 99%

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

2019

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This work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform’s compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.

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Towards efficient large scale epidemiological simulations in EpiGraph

Cited by 13 publications

References 27 publications

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area

Simulation of COVID-19 Propagation Scenarios in the Madrid Metropolitan Area

A service‐based framework for building and executing epidemic simulation applications in the cloud

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

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