Lelys Bravo de Guenni

The paper uses Bayesian hierarchical spatial models to predict the relative abundance of three tick species in Illinois based on environmental and spatial data.

The paper develops a Bayesian spatial model to predict tick abundance in livestock systems using heterogeneous surveillance data, showing improved model fit and identifying areas needing more data collection.

The paper uses machine learning to impute missing mosquito data and improve malaria incidence predictions in Bolivar State, Venezuela, but finds it ineffective for P. falciparum.