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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 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.
References
World Health Organization: Department of Communicable Disease Surveillance and Response. Consensus document on the epidemiology of severe acute respiratory syndrome (SARS). https://www.who.int/csr/sars/WHOconsensus.pdf?ua=1. Accessed 12 Aug 2020
Petersen, E., et al.: Comparing SARS-CoV-2 with SARS-CoV and influenza pandemics (2020). https://doi.org/10.1016/S1473-3099(20)30484-9. https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30484-9/fulltext#back-bib. Accessed 20 Aug 2020
United Nations Department of Economic and Social Affairs: World Urbanization Prospects Revision (2018). https://population.un.org/wup/Publications/Files/WUP2018-Highlights.pdf
Paradox Engineeering: When smart technologies combat Covid-19 and contribute to urban. https://www.pdxeng.ch/2020/03/31/smart-technologies-Covid-19-urban-resilience/. Accessed 20 Aug 2020
Barnett, D.J., Rosenblum, A.J., Strauss-Riggs, K., Kirsch, T.D.: Readying for a post–COVID-19 world. The case for concurrent pandemic disaster response and recovery efforts in public health. J. Public Health Manag. Pract. 26(4), 310–313 (2020). https://doi.org/10.1097/PHH.0000000000001199
Collins, A., Florin, M.-V., Renn, O.: COVID-19 risk governance: drivers, responses and lessons to be learned. J. Risk Res. (2020). https://doi.org/10.1080/13669877.2020.1760332
Jordà , Ò., Singh, S.R., Taylor, A.M.: Longer-run economic consequences of pandemics. Federal Reserve Bank of San Francisco Working Paper 2020-09. https://doi.org/10.24148/wp2020-09
The Economist: Economist Intelligence Unit 2020: The long recovery to 2019 GDP levels, 18 June 2020. https://www.eiu.com/n/the-long-recovery-to-2019-gdp-levels/. Accessed 21 July 2020
Hodicky, J., Prochazka, D.: Modelling and simulation paradigms to support autonomous system operationalization. In: Mazal, J., Fagiolini, A., Vasik, P. (eds.) MESAS 2019. LNCS, vol. 11995, pp. 361–371. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43890-6_29. ISBN 978-3-030-14984-0
Hodicky, J., Prochazka, D., Prochazka, J.: Automation in experimentation with constructive simulation. In: Mazal, J. (ed.) MESAS 2018. LNCS, vol. 11472, pp. 566–576. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14984-0_42. ISBN 978-303014983-3.2
Douglas, J.V., et al.: STEM: an open source tool for disease (2019). https://doi.org/10.1089/hs.2019.0018. Accessed 19 Aug 2019
IBM: Public Health Research - The Spatiotemporal Epidemiological Modeler (STEM) Overview. https://researcher.watson.ibm.com. Accessed 15 July 2020
Baldassi, F., et al.: Testing the accuracy ratio of the Spatio-Temporal Epidemiological Modeler (STEM) through Ebola haemorrhagic fever outbreaks. Epidemiol. Infect. 144(7), 1463–1472 (2015). https://doi.org/10.1017/S0950268815002939
Ined: Institut national d'études démographiques. https://www.ined.fr. Accessed 04 July 2020
Ndairou, F., Area, I., Nieto, J.J., Torres, D.F.M.: Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals 135 (2020). https://doi.org/10.1016/j.chaos.2020.109846
Chu, D.K., Akl, E.A., Duda, S., Solo, K., Yaacoub, S., Schunemann, H.J.: Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis 395 (2020). https://doi.org/10.1016/S0140-6736(20)31142-9. www.thelancet.com
Anderson, W.R., Husain, A., Rosner, M.: The OODA loop: why timing is everything. Cognitive Times, December 2017. https://www.europarl.europa.eu/cmsdata/155280/WendyRAnderson_CognitiveTimes_OODA%20LoopArticle.pdf. Accessed 15 June 2020
David, W., et al.: Giving life to the map can save more lives. Wildfire scenario with interoperable simulations. Adv. Cartogr. GIScience Int. Cartogr. Assoc. 1, 4 (2019). https://doi.org/10.5194/ica-adv-1-4-2019
Expert System: https://expertsystem.com/products/medical-intelligence-platform/. Accessed 04 Aug 2020
Grumelard, S., Bisca, P.M.: Can humanitarians, peacekeepers, and development agencies work together to fight epidemics? 24 April 2020. https://blogs.worldbank.org/dev4peace/can-humanitarians-peacekeepers-and-development-agencies-work-together-fight-epidemics. Accessed 15 June 2020
Hooda, D.-S., in General's Jottings: Lessons from pandemic: robotics and readiness for info warfare in Times of India, 19 April 2020. https://timesofindia.indiatimes.com/blogs/generals-jottings/lessons-from-pandemic-robotics-and-readiness-for-info-warfare/. Accessed 18 June 2020
Howard, A., Borenstein, J.: AI, Robots, and Ethics in the Age of COVID-19, 12 May 2020. https://sloanreview.mit.edu/article/ai-robots-and-ethics-in-the-age-of-Covid-19/. Accessed 13 July 2020
Ting, D.S.W., Carin, L., Dzau, V., Wong, T.Y.: Digital technology and COVID-19. Nat. Med. 26, 459–461 (2020). https://doi.org/10.1038/s41591-020-0824-5
David, W., Pappalepore, P., Stefanova, A., Sarbu, B.A.: AI-powered lethal autonomous weapon systems in defence transformation. Impact and challenges. In: Mazal, J., Fagiolini, A., Vasik, P. (eds.) MESAS 2019. LNCS, vol. 11995, pp. 337–350. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43890-6_27
Murphy, R.R., Adams, J., Gandudi, V.B.M.: Robots are playing many roles in the coronavirus crisis – and offering lessons for future disasters, 22 April 2020. https://theconversation.com/robots-are-playing-many-roles-in-the-coronavirus-crisis-and-offering-lessons-for-future-disasters-135527. Accessed 13 June 2020
International Committee of the Red Cross (ICRC): Autonomy, artificial intelligence and robotics: technical aspects of human control, Geneva, August 2019 (2019). https://www.icrc.org/en/document/autonomy-artificial-intelligence-and-robotics-technical-aspects-human-control. Accessed 13 June 2020
US Army: The U.S. Army robotic and autonomous systems strategy. https://www.tradoc.army.mil/Portals/14/Documents/RAS_Strategy.pdf. Accessed 14 Aug 2020
US Army: Army Techniques Publication (ATP) 3-90.5 (2016). https://armypubs.army.mil/ProductMaps/PubForm/Details.aspx?PUB_ID=106018. Accessed 17 Aug 2020
FT Editorial Board: The role of robots in a post-pandemic world, 21 May 2020. https://www.ft.com/content/291f3066-9b53-11ea-adb1-529f96d8a00b. Accessed 15 June 2020
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-70740-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70739-2
Online ISBN: 978-3-030-70740-8
eBook Packages: Computer ScienceComputer Science (R0)