TY  - JOUR
AU  - Carpio Rodríguez, Ana María
AU  - Pierret, Emile
PY  - 2022
DO  - 10.1016/j.rinp.2022.105375
SN  - 2211-3797
UR  - https://hdl.handle.net/20.500.14352/72640
T2  - Results in physics
AB  - We develop a Bayesian inference framework to quantify uncertainties in epidemiological models. We use SEIJR and SIJR models involving populations of susceptible, exposed, infective, diagnosed, dead and recovered individuals to infer from Covid-19 data...
LA  - eng
PB  - Elsevier
KW  - SEIJR models
KW  - Covid-19
KW  - Numerical simulation
KW  - Bayesian inference. Uncertainty quantification
TI  - Uncertainty quantification in Covid-19 spread: Lockdown effects
TY  - journal article
VL  - 35
ER  -