We attempted to do this using M igrate- n to choose among migration models on the basis of their ln marginal likelihood, incorporating the known time since infestation into the model.
To determine whether the linear relationship between force and displacement was influenced by scale infestation duration, a GLM model was fitted in SAS GLM Procedure with force as a dependent variable and displacement, replications and the interaction of displacement and replications as independent variables.
Using the resulting regressions, we developed a transition matrix to describe changes in vegetation types under varying levels of spruce beetle infestations, and applied the model to the vegetation map.
To investigate whether pest infestations predicted home pesticide use, we constructed logistic regression models with infestation as the independent variable and home pesticide use as the dependent variable.
Assuming that households did not become infested between the 2 treatments, we jointly estimated c, s, and n I/II by modeling the observed infestation as a system of 3 equations (observed infested twice, infested only treatment I, and infested only during treatment II) (Table 3) that we solved analytically.
In the second stage, we investigated rodent-level factors of species, sex, age category, and mass as explanatory variables for tick infestations in multivariable models that accounted for any seasonal variation in tick abundance deduced in the first stage.
We made 300 replicate simulations from 1982 to 2005, and we calculated the invasion probability in each cell as the number of times the model predicted the infestation of the cell, divided by 300.
Known risk factors of COPD, diabetes mellitus, tobacco use, and infection or infestation were proposed in the model even though they were not significant.
Our model comparing the infestation observed during the first and second insecticide treatments suggested that a single treatment was successful in 98.7% (95% CI 98.4%98.9%%) of infested households (Table 2) (http://www.spatcontrol.net/articles/Barbu2014/suppMet.pdf).net/articles/Barbu2014/suppMet.pdf
Model testing is accomplished by simulating MPB infestations using both the ForestSimMPB model and a Random ABM model that serves as a null model.
