Plants disease epidemiology provides us with some information about the spread of diseases in different regions with various climates and helps us conduct suitable managing operations and predictions about the spread of disease to other areas. Geographic Information System (GIS) has been widely used as an important tool in epidemiological studies. Wetwood disease is one of the most important bacterial diseases on elm trees found in the Northwest of Iran. The disease has spread in different regions of Tabriz (located in the Northwest of Iran), which has become terribly epidemic. Geographic Information System as an appropriate tool in epidemiological examination of plant disease is useful in various ways. In this study, the epidemiology of bacterial wetwood disease on elm trees in Tabriz was investigated using GIS databases. The results indicate that the disease has become epidemic in different areas of Tabriz. According to the results, although the disease was not found in some regions, its severity was very high in some other areas. Based on the distribution map, the wetwood disease most highly exists in the central regions and some parts of the northern regions of the city, but eastern regions are least affected.
Farrukh Jamal, M. H. Tahir, Morad Alizadeh and M. A. Nasir
Generalizing distributions is important for applied statisticians and recent literature has suggested several ways of extending well-known distributions. We propose a new class of distributions called the Marshall-Olkin Burr X family, which yields exible shapes for its density such as symmetrical, left-skewed, right-skewed and reversed-J shaped, and can have increasing, decreasing,constant, bathtub and upside-down bathtub hazard rates shaped. Some of its structural properties including quantile and generating functions, ordinary and incomplete moments, and mean deviations are obtained. One special model of this family, the Marshall- Olkin-Burr-Lomax distribution, is investigated in details. We also derive the density of the order statistics. The model parameters are estimated by the maximum likelihood method. For illustrative purposes, three applications to real life data are presented.