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Combining Epidemiological and Constructive Simulations for Robotics and Autonomous Systems Supporting Logistic Supply in Infectious Diseases Affected Areas

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Modelling and Simulation for Autonomous Systems (MESAS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12619))

Abstract

It is very likely that the post-Covid-19 world will be significantly different from today. From the experience in fighting the pandemic we can identify lessons on the vulnerability of humans, logistics, and supply chain of vital strategic assets (e.g. medical equipment). This require to think about how to conduct operations in the future, investigate robotics and autonomous systems (RAS) to reduce the exposure while achieving operational improvement, and to assess if current doctrines need to undergo a review. Modelling and simulation play a significant role in analysis and training for scenarios that might include reacting and anticipating the unexpected, challenging our agility and resilience. Available constructive simulations have been designed primarily for training commanders and staff but often lack the ability to exploit the outcomes from predictive systems. The authors propose a novel approach considering the Spatiotemporal Epidemiological Modeler (STEM) for computing the epidemic trend. This tool has been linked with the MASA SWORD constructive simulation. STEM computed data enable the creation in SWORD of highly realistic scenarios in the context of infectious diseases, outbreaks, bioterrorism and biological defence where to model RAS, run the simulation, and analyse doctrine and courses of actions.

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Notes

  1. 1.

    Based on its knowledge of a broader environment, the system can initiate automatically a mission the system gathers, filters, and prioritizes data. The system integrates, interprets data and makes predictions. The system performs final ranking. No information is ever displayed to the human. The system executes automatically and does not allow any human interaction.

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Author information

Authors and Affiliations

  1. Italian Army Training Specialization and Doctrine Command, 00143, Rome, Italy

    Walter David

  2. Italian Joint NBC Defence School, 02100, Rieti, Italy

    Federico Baldassi

  3. University of Padua, 35122, Padua, Italy

    Silvia Elena Piovan

  4. MASA Group, 75002, Paris, France

    Antony Hubervic & Erwan Le Corre

Authors
  1. Walter David
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  2. Federico Baldassi
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  3. Silvia Elena Piovan
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  4. Antony Hubervic
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  5. Erwan Le Corre
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Corresponding author

Correspondence to Walter David .

Editor information

Editors and Affiliations

  1. NATO M&S COE, Rome, Italy

    Jan Mazal

  2. University of Palermo, Palermo, Italy

    Adriano Fagiolini

  3. Brno University of Technology, Brno, Czech Republic

    Petr Vasik

  4. NATO M&S COE, Rome, Italy

    Michele Turi

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David, W., Baldassi, F., Piovan, S.E., Hubervic, A., Le Corre, E. (2021). Combining Epidemiological and Constructive Simulations for Robotics and Autonomous Systems Supporting Logistic Supply in Infectious Diseases Affected Areas. In: Mazal, J., Fagiolini, A., Vasik, P., Turi, M. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2020. Lecture Notes in Computer Science(), vol 12619. Springer, Cham. https://doi.org/10.1007/978-3-030-70740-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-70740-8_6

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