We implements approaches such as:

  • Dynamic Time Series Model applying State Space modelling, Bayesian statistics and Kalman filter.

  • Logit Multinomial PLS (Partial Least Square) to improve robustness of multinomial models.

  • MCMC (Monte Carlo Markov Chain), which allows to estimate "posterior" expectations in complex problem.

  • Agent Based Model which allows to reproduce characteristics of real outbreaks and make their predictions easy to understand.