Lucie Bonnetain

LICIT UMR_T9401, University of Lyon, ENTPE, University Gustave Eiffel, Lyon, France.

2 publications 2018 – 2021
CognitionAdaptation, PhysiologicalArmFeedback, SensoryHealthy Volunteers

What does Lucie Bonnetain research?

Lucie Bonnetain studies the dynamics of disease transmission, particularly concerning contagious diseases like COVID-19. She has developed a novel model called the Mobility-based SIR model, which factors in how people move and interact within their environments. This approach allows for a more accurate prediction of disease spread by considering geographical distribution and actual travel patterns. Essentially, her research helps policymakers and health officials understand where and how fast an outbreak might occur, enabling them to make informed decisions to mitigate its impact.

Key findings

  • The Mobility-based SIR model successfully predicted COVID-19 case counts using real data from Estonia and France.
  • The model accounts for geographical population distribution and movement patterns, improving accuracy in predictions.
  • Local and regional predictions of COVID-19 spread were validated with real-world data, enhancing the preparedness of health agencies.

Frequently asked questions

Does Dr. Bonnetain study COVID-19?
Yes, she has developed a model specifically for predicting the spread of COVID-19.
What methods has Dr. Bonnetain researched for pandemic preparedness?
She has created a computer model that accounts for human mobility and geographical factors to predict disease outbreaks.
How can Dr. Bonnetain's work benefit public health?
Her model helps governments and health agencies prepare for pandemics by predicting where and how fast diseases will spread.

Publications in plain English

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

2021

Social network analysis and mining

Goel R, Bonnetain L, Sharma R, Furno A

Plain English
Researchers created a new computer model that predicts how diseases like COVID-19 spread between people and across regions by accounting for two things existing models ignore: how populations are distributed geographically and how people actually travel and connect with each other. They tested their model using real COVID-19 data from Estonia and France, and it successfully predicted case counts at local and regional levels. This model helps governments and health agencies prepare for pandemics by showing exactly where outbreaks will happen and how fast they'll spread based on real-world human movement patterns.

PubMed

Force field adaptation does not alter space representation.

2018

Scientific reports

Michel C, Bonnetain L, Amoura S, White O

Plain English
This study examined how people adapt to new environments and whether this affects their understanding of space. Researchers found that when healthy individuals adapted to a new force field, it only changed their movement skills but didn't alter their perception of where things are. This is important because it helps us understand how different types of adaptation work and ensures that future studies on motor skills won't unintentionally impact spatial thinking. Who this helps: This helps researchers and clinicians working with patients who need to preserve their spatial awareness while undergoing motor skill training.

PubMed

Publication data sourced from PubMed . Plain-English summaries generated by AI. Not medical advice.