Elham Ghehsareh Ardestani1, Azam Mokhtari2. 1. Department of Natural Resources and Earth Sciences, Faculty of Rangeland and Watershed Management, Shahrekord University, Shahrekord- Iran, Central Laboratory, Shahrekord University, Shahrekord, Iran. 2. Department of Pathobiology, Faculty of Veterinary Medicine, Central Laboratory, Shahrekord University, Shahrekord, Iran. Electronic address: a.mokhtari@alumni.ut.ac.ir.
Abstract
BACKGROUND: New Lumpy Skin Disease (LSD) outbreaks are currently circulating in Chaharmahal and Bakhtiari province, (western Iran, in central Zagros Mountains). OBJECTIVES: The aim of this study was to model the risk probability of LSD in this area. METHOD: Data were collected from veterinary organizations between 2012 and 2016. During this period, 290 outbreaks were registered. The herds were grazing and zero grazing. The average size of herds was 2958. We analyzed the potential for an outbreak of LSD in this area basing on the grid maps with the resolution of 1 km. In this study, 22 environmental variables (19 bioclimatic and 3 topography variables) were used to explore the environmental influences on LSD outbreak by maximum entropy ecological niche modeling (MaxEnt). RESULTS: The results showed that mainly the central, northern, northeast and southern parts of Charmahal and Bakhtiari were the most very high risk areas for LSD. The MaxEnt model performed, with an area under the receiver operating characteristic curve (AUC) of 0.977 and 0.972 for training and test data, respectively. It showed high accuracy for predicting the prevalence of LSD (AUC close to 1). The precipitation of coldest season, isothermality and mean temperature of wettest season made the greatest contribution to the model (68.1%). Evaluating the importance of environmental variables, which were derived from the jackknife test, stated the precipitation of the wettest period and the coldest season and isothermality as the bioclimatic variables in explaining LSD prevalence compared to the other variables. CONCLUSION: The MaxEnt model could be applied to predict the LSD risk probability of occurrence in central Zagros Mountains of Iran based on the current prevalence data of the disease. Also, the model confirmed that coexistence of weather conditions including defined humidity and temperature is necessary for the disease occurrence.
BACKGROUND: New Lumpy Skin Disease (LSD) outbreaks are currently circulating in Chaharmahal and Bakhtiari province, (western Iran, in central Zagros Mountains). OBJECTIVES: The aim of this study was to model the risk probability of LSD in this area. METHOD: Data were collected from veterinary organizations between 2012 and 2016. During this period, 290 outbreaks were registered. The herds were grazing and zero grazing. The average size of herds was 2958. We analyzed the potential for an outbreak of LSD in this area basing on the grid maps with the resolution of 1 km. In this study, 22 environmental variables (19 bioclimatic and 3 topography variables) were used to explore the environmental influences on LSD outbreak by maximum entropy ecological niche modeling (MaxEnt). RESULTS: The results showed that mainly the central, northern, northeast and southern parts of Charmahal and Bakhtiari were the most very high risk areas for LSD. The MaxEnt model performed, with an area under the receiver operating characteristic curve (AUC) of 0.977 and 0.972 for training and test data, respectively. It showed high accuracy for predicting the prevalence of LSD (AUC close to 1). The precipitation of coldest season, isothermality and mean temperature of wettest season made the greatest contribution to the model (68.1%). Evaluating the importance of environmental variables, which were derived from the jackknife test, stated the precipitation of the wettest period and the coldest season and isothermality as the bioclimatic variables in explaining LSD prevalence compared to the other variables. CONCLUSION: The MaxEnt model could be applied to predict the LSD risk probability of occurrence in central Zagros Mountains of Iran based on the current prevalence data of the disease. Also, the model confirmed that coexistence of weather conditions including defined humidity and temperature is necessary for the disease occurrence.