Temporal modeling of Crimean-Congo hemorrhagic fever in eastern Iran

Abstract

The model predicted the number of cases 1 month in advance with more or less acceptable accuracy. Therefore, it appears that the model might be useful as part of an early warning system.

Crimean-Congo hemorrhagic fever and its relationship with climate factors in southeast Iran: a 13-year experience

“…The effect of climate factors on tick activity and occurrence of CCHF have been discussed in other studies [2,4,5,16,27,28]. Indeed, understanding the relationship between climate factors and the occurrence of CCHF could be helpful in the establishment of early warning and even forecasting systems in the surveillance of this disease.…”

“…Several studies have discussed the relationship between the climatic factors and the vector's lifecycle, ecological conditions, and, consequently, the occurrence of CCHF in human populations [1,[4][5][6]. In addition to climate variables, there are some other epidemiologic factors that influence the occurrence of CCHF that should be considered in controlling the disease [7][8][9].…”

“…There are several reports regarding different epidemiological aspects of the disease in the province [5,[12][13][14][15][16][17]; however, none of them have considered the climate or climatic changes as one of the important variables in the epidemiology of CCHF.…”

Risk-oriented model for predicting epidemiological situation with crimean-congo hemorrhagic fever (on the example of Stavropol region)

“…This model functioning is based exceptionally on analyzing stages in infectious disease development and reflects only probabilistic nature of an epidemic process [11]. Other mathematical procedures make it possible to draw up forecasts for the general CHF mortality dynamics depending on climatic factors for the whole territory of an examined region [12][13][14][15]. So, at present we don't have a risk-oriented model based on multifactor analysis of predictors and applied for quantitative predicting whether CHF cases will occur next year on a territory of each administrative district in a region.…”

“…Air humidity and precipitations (in spring and summer), as well as snow mantle size in February-March, were the most informative and significant risk factors for prediction out of all the examined climatic parameters in the stated period. It can be explained by their direct influence on number and activity of Hyalommamarginatum mites and other mites which are the main CCHF infectious agents; this influence was proved in some works [6,7,14]. Such factors as wind speed, population density in administrative districts, and results of laboratory tests performed on mites in order to detect CCHF virus markers, turned out to be not informative.…”