This paper introduces significant advancements in the GreatMod modeling framework, enhancing its capacity to simulate systems characterized by non-Markovian dynamics accurately. These enhancements include the definition of a novel graphical formalism tailored to represent such complex models, alongside an extension of the Stochastic Simulation Algorithm to accommodate their simulation efficiently. Moreover, we validate the robustness of these improvements through two case studies: the Susceptible-Infected-Recovered model and the Parallel-Producer-Consumer model.

Extension of the GreatMod Modeling Framework to Simulate Non-Markovian Processes with General-Distributed Events

Terrone, Irene
First
;
Volpatto, Daniela;Pernice, Simone
;
Amparore, Elvio;Sirovich, Roberta;Cordero, Francesca;Beccuti, Marco
Last
2025-01-01

Abstract

This paper introduces significant advancements in the GreatMod modeling framework, enhancing its capacity to simulate systems characterized by non-Markovian dynamics accurately. These enhancements include the definition of a novel graphical formalism tailored to represent such complex models, alongside an extension of the Stochastic Simulation Algorithm to accommodate their simulation efficiently. Moreover, we validate the robustness of these improvements through two case studies: the Susceptible-Infected-Recovered model and the Parallel-Producer-Consumer model.
2025
18th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2023
Padova, Italy
2023
Lecture Notes in Computer Science
Springer Science and Business Media Deutschland GmbH
14513 LNBI
177
191
9783031907135
9783031907142
Non Markovian models; SIR; Stochastic simulation
Terrone, Irene; Volpatto, Daniela; Pernice, Simone; Amparore, Elvio; Sirovich, Roberta; Cordero, Francesca; Beccuti, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/2078465
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