Mobility-based SIR model for complex networks: with case study Of COVID-19

Autor: Rajesh Sharma, Loïc Bonnetain, Rahul Goel, Angelo Furno
Přispěvatelé: Institute of Computer Science, University of Tartu, Tartu, Estonia, Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel, RP1-S19100 , PROMENADE, Platform for Resilient Multi-modal Mobility via Multi-layer Networks & Real-time Big-Data Processing
Jazyk: angličtina
Rok vydání: 2021
Předmět:
RHONE ALPES
Estonia
050402 sociology
Computer science
Generalization
Population
Complex networks
Globe
Distribution (economics)
PANDEMIE
TELEPHONE MOBILE
Epidemic based modeling
Mathematical proof
01 natural sciences
010305 fluids & plasmas
TECHNOLOGIE SANS FIL
0504 sociology
TELECOMMUNICATION
0103 physical sciences
Pandemic
Media Technology
medicine
education
Call data records
Mobility
education.field_of_study
business.industry
RHONE-ALPES
Communication
05 social sciences
ENREGISTREMENT DES DONNEES
COVID-19
Complex network
Data science
MODELISATION
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
3. Good health
Computer Science Applications
Human-Computer Interaction
medicine.anatomical_structure
SIMULATION
SIR
Original Article
business
Epidemic model
Rhône-Alpes
Information Systems
Zdroj: Social Network Analysis and Mining
Social Network Analysis and Mining, 2021, 11, pp1-18. ⟨10.1007/s13278-021-00814-3⟩
ISSN: 1869-5469
1869-5450
Popis: In the last decade, humanity has faced many different pandemics such as SARS, H1N1, and presently novel coronavirus (COVID-19). On one side, scientists have developed vaccinations, and on the other side, there is a need to propose models that can help in understanding the spread of these pandemics as it can help governmental and other concerned agencies to be well prepared, especially for pandemics, which spreads faster like COVID-19. The main reason for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, often worldwide. Also, the population distribution and social coherence in the different regions of the world are non-uniform. Thus, once the epidemic enters a region, then the local population distribution plays an important role. Inspired by these ideas, we propose two versions of our mobility-based SIR model, (i) fully-mixed and (ii) for complex networks, which especially takes into account real life interactions. To the best of our knowledge, this model is the first of its kind, which takes into account the population distribution, connectivity of different geographic locations across the globe, and individuals' network connectivity information. In addition to presenting the mathematical proof of our models, we have performed extensive simulations using synthetic data to demonstrate the generalization capability of our models. Finally, to demonstrate the wider scope of our model, we applied our model to forecast the COVID-19 cases at county level (Estonia) and regional level (Rhône-Alpes region in France).
Databáze: OpenAIRE
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