A hybrid identification method for mathematical models for Zika virus

This paper studies some deterministic mathematical models that seek to explain the expansion of Zika virus, as a viral epidemic, using published data for Brazil. Three SIR type models considering several aspects in the spread of the disease are considered with 3, 6 and 10 unknown parameters respectively. The parameter identification is carried through a search algorithm based on a combination of a stochastic domain exploration and a heuristic calculation of a descent direction, in order to avoid stopping the algorithm at a local optimum. The models are validated using the epidemic data found. Finally, it was confirmed that the basic reproductive ratio ℜ 0 is consistent with those previously reported in the literature. We conclude that the proposed optimization method improves computation time with respect to a genetic algorithm or an exhaustive search in the parameter space.