Acquaintance-immunization-in-SIR-scale-free-graph-for-COVID-19
Slides + Demo
Please look at the slides for more infos
Worst case scenario: temproal results
Comments :
1- If nothing is done, according this modelisation (imperfect of course), we will finish with 69% of the population removed. That’s perfectly match with the worst case scenario proposed by the NYT: 224 millions Americans can be infected by the virus vs 226 millions with this estimation.
2- With no exterior agent, the epidemic should stop a time 18, which represent 60% of the population removed. That's means, if 60% of our population is removed, the endemic state is over.
Answer to the question: If a vaccine against CO-VID is found tomorrow, which vaccination strategy leads to the suppression of the endemic state for a lowest immunization rate ?
Comparaison strategies
| Random | Targeted | Acquitance K = 20% | |
|---|---|---|---|
| Percentage of the population vaccinacte in order to stop the endemic state | 85%` | 5% | 30% |
Comments :
1- Random immunisation is not an efficient strategy
2- Targeted the hubs of the networks are super efficient strategy, but implies that we know the graph (which is not true)
3- Acquitance strategy gives good results and this strategy is purely local, requiring minimal information about randomly selected nodes and their immediate environment.
References
@article{cohen2003structural,
title={Structural properties of scale free networks},
author={Cohen, Reuven and Havlin, Shlomo and Ben-Avraham, Daniel},
journal={Handbook of graphs and networks},
volume={4},
publisher={Wiley Online Library}
}
@article{cohen2000resilience,
title={Resilience of the internet to random breakdowns},
author={Cohen, Reuven and Erez, Keren and Ben-Avraham, Daniel and Havlin, Shlomo},
journal={Physical review letters},
volume={85},
number={21},
pages={4626},
year={2000},
publisher={APS}
}
@article{cohen2003efficient,
title={Efficient immunization strategies for computer networks and populations},
author={Cohen, Reuven and Havlin, Shlomo and Ben-Avraham, Daniel},
journal={Physical review letters},
volume={91},
number={24},
pages={247901},
year={2003},
publisher={APS}
}
@article{madar2004immunization,
title={Immunization and epidemic dynamics in complex networks},
author={Madar, Nilly and Kalisky, Tomer and Cohen, Reuven and Ben-avraham, Daniel and Havlin, Shlomo},
journal={The European Physical Journal B},
volume={38},
number={2},
pages={269--276},
year={2004},
publisher={Springer}
}