We study properties of SEIRV (Susceptible-Exposed-Infectious- Recovered-Vaccinated) model of the epidemic spread. We assume that the infection spreads locally by interactions with the nearest neighbors. In addition to the local spread of epidemic there is a nonlocal spread produced by vectors.
Traditionally, epidemiological modelling focuses on the construction of the optimal eradication and containment strategies. These strategies should be able to stop or to prevent a potential epidemic outbreak at the lowest cost possible, reducing the number of causalities or wastage. Nevertheless, due to the fact that the exact state of individuals and detailed information about the structure of interaction is not known, optimal strategies, as we have showed previously, are not always the most efficient ones. Furthermore, presence of mobile vectors which can contribute to nonlocal epidemic spreads makes control actions even less efficient.
Here, contrary to traditional epidemiological modelling, we focus on a study of vector properties and the interplay between vector characteristics and their dispersal pattern. More precisely, we study a SEIRV model from a vector perspective asking questions about the shape of an optimal dispersal pattern of vectors. The optimal dispersal pattern is understood as the one which maximizes a given vector's characteristic. As an exemplary measures we use: time for which vectors are in the infectious state, the number of individuals infected by vectors or a total duration of an epidemic.
From detailed numerical simulations we can conclude that different measures are not always equivalent and, consequently, they can lead to different types of the optimal shape of vector dispersal. From the whole class of considered random walks some of them are favored over others. However, the exact shape of the optimal random walk depends on a considered measure. The biggest discrepancies are produced by measures derived from the number of casualties and time.