Parallel Programming Approaches for an Agent-based Simulation of Concurrent Pandemic and Seasonal Influenza Outbreaks

Soto-Ferrari, Milton; Holvenstot, Peter; Prieto, Diana; Doncker, Elise de; Kapenga, John A.

doi:10.1016/j.procs.2013.05.389

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…We have discussed copiously the two main streams of methods: EDS and TDS. There are also ad-hoc or agent-based methods of epidemic simulation, such as [1,21,[27][28][29]. Many of these ad-hoc methods do employ parallel computation.…”

Section: Related Workmentioning

confidence: 99%

Scalable parallel and distributed simulation of an epidemic on a graph

Dou

2023

PLoS ONE

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We propose an algorithm to simulate Markovian SIS epidemics with homogeneous rates and pairwise interactions on a fixed undirected graph, assuming a distributed memory model of parallel programming and limited bandwidth. This setup can represent a broad class of simulation tasks with compartmental models. Existing solutions for such tasks are sequential by nature. We provide an innovative solution that makes trade-offs between statistical faithfulness and parallelism possible. We offer an implementation of the algorithm in the form of pseudocode in the Appendix. Also, we analyze its algorithmic complexity and its induced dynamical system. Finally, we design experiments to show its scalability and faithfulness. In our experiments, we discover that graph structures that admit good partitioning schemes, such as the ones with clear community structures, together with the correct application of a graph partitioning method, can lead to better scalability and faithfulness. We believe this algorithm offers a way of scaling out, allowing researchers to run simulation tasks at a scale that was not accessible before. Furthermore, we believe this algorithm lays a solid foundation for extensions to more advanced epidemic simulations and graph dynamics in other fields.

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Section: Related Workmentioning

confidence: 99%

Scalable parallel and distributed simulation of an epidemic on a graph

Dou

2023

PLoS ONE

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“…Among the models that have applied mathematical modeling to influenza epidemics, Refs. [32,33] used agent-based simulation models to explore the progression of influenza in a population. The SIR model has been combined with a dynamic small world network [30] to model the spreading process of randomly contagious diseases, such as influenza.…”

Section: Previous Researchmentioning

confidence: 99%

SEIR-SW, simulation model of influenza spread based on the Small World network

Younsi

Bounnekar

Hamdadou

et al. 2015

Tinshhua Sci. Technol.

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This study modeled the spread of an influenza epidemic in the population of Oran, Algeria. We investigated the mathematical epidemic model, SEIR (Susceptible-Exposed-Infected-Removed), through extensive simulations of the effects of social network on epidemic spread in a Small World (SW) network, to understand how an influenza epidemic spreads through a human population. A combined SEIR-SW model was built, to help understand the dynamics of infectious disease in a community, and to identify the main characteristics of epidemic transmission and its evolution over time. The model was also used to examine social network effects to better understand the topological structure of social contact and the impact of its properties. Experiments were conducted to evaluate the combined SEIR-SW model. Simulation results were analyzed to explore how network evolution influences the spread of desease, and statistical tests were applied to validate the model. The model accurately replicated the dynamic behavior of the real influenza epidemic data, confirming that the susceptible size and topological structure of social networks in a human population significantly influence the spread of infectious diseases. Our model can provide health policy decision makers with a better understanding of epidemic spread, allowing them to implement control measures. It also provides an early warning of the emergence of influenza epidemics.

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“…We evaluated the changes in the prevalence of infection, the total attack rate in population at risk, hospitalizations, and transmission of infections in hospitals along with the resources needed to operate the clinics for different periods of time. Agent-based simulations have been widely used to model the spread of influenza in prior studies [8][9][10][11][12][13]; however, these models have not captured disease transmission occurring specifically in health facilities. A key feature of our study is that dedicated clinics may take time before they can be open, and they may not be open throughout the disease spread or across all locations.…”

Section: Introductionmentioning

confidence: 99%

The impact of opening dedicated clinics on disease transmission during an influenza pandemic

et al. 2020

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Dedicated clinics can be established in an influenza pandemic to isolate people and potentially reduce opportunities for influenza transmission. However, their operation requires resources and their existence may attract the worried-well. In this study, we quantify the impact of opening dedicated influenza clinics during a pandemic based on an agent-based simulation model across a time-varying social network of households, workplaces, schools, community locations, and health facilities in the state of Georgia. We calculate performance measures, including peak prevalence and total attack rate, while accounting for clinic operations, including timing and location. We find that opening clinics can reduce disease spread and hospitalizations even when visited by the worried-well, open for limited weeks, or open in limited locations, and especially when the clinics are in operation during times of highest prevalence. Specifically, peak prevalence, total attack rate, and hospitalization reduced 0.07-0.32%, 0.40-1.51%, 0.02-0.09%, respectively, by operating clinics for the pandemic duration.

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Parallel Programming Approaches for an Agent-based Simulation of Concurrent Pandemic and Seasonal Influenza Outbreaks

Cited by 6 publications

References 4 publications

Scalable parallel and distributed simulation of an epidemic on a graph

Scalable parallel and distributed simulation of an epidemic on a graph

SEIR-SW, simulation model of influenza spread based on the Small World network

The impact of opening dedicated clinics on disease transmission during an influenza pandemic

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