Imperial College London
Portrait Picture

Dr Dante Kalise

Faculty of Natural Sciences, Department of Mathematics

Reader in Computational Optimisation and Control
 
 
 
//

Contact

 

d.kalise-balza Website CV

 
 
//

Location

 

742Huxley BuildingSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Dutta:2021:10.1371/journal.pcbi.1009236,
author = {Dutta, R and Gomes, S and Kalise, D and Pacchiardi, L},
doi = {10.1371/journal.pcbi.1009236},
journal = {PLoS Computational Biology},
title = {Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic},
url = {http://dx.doi.org/10.1371/journal.pcbi.1009236},
volume = {17},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A mathematical model for the COVID-19 pandemic spread, which integratesage-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with realmobile phone data accounting for the population mobility, is presented.  The dynamicalmodel adjustment is performed via Approximate Bayesian Computation.  Optimallockdown and exit strategies are determined based on nonlinear model predictivecontrol, constrained to public-health and socio-economic factors.  Through an extensivecomputational validation of the methodology, it is shown that it is possible to computerobust exit strategies with realistic reduced mobility values to inform public policymaking, and we exemplify the applicability of the methodology using datasets fromEngland and France.
AU  - Dutta,R
AU  - Gomes,S
AU  - Kalise,D
AU  - Pacchiardi,L
DO  - 10.1371/journal.pcbi.1009236
PY  - 2021///
SN  - 1553-734X
TI  - Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic
T2  - PLoS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1009236
UR  - http://hdl.handle.net/10044/1/90890
VL  - 17
ER  -
Download