Predicting incidence of Crimean-Congo hemorrhagic fever using satellite monitoring (remote sensing) data in the Stavropol Territory

Abstract

Introduction. With the epidemiological situation for Crimean-Congo hemorrhagic fever (CCHF) remaining tense in many countries worldwide, special attention should be focused on development and improvement of risk-based epidemiological prediction methods.The aim of the study was to build a prediction model for CCHF incidence dynamics (based on the Stavropol Territory) using satellite monitoring (remote sensing) data.Materials and methods. We analyzed the climate data obtained from the Space Research Institute of the Russian Academy of Sciences as well as the data of public statistics reports on CCHF incidence from 2005 to 2021. The prediction model incorporated the Bayes theorem and Wald sequential analysis. The information content of the factors was assessed using the Kullback method.Results. Predictions for each of 26 districts were made stepwise (compared to threshold levels) to predict whether there will be at least one case of CCHF, whether the relative incidence per 100,000 population will exceed the median level (0.9 cases) or the average rate (3.5 cases) or the third quartile rate (4.7 cases). The highest values of information coefficients were obtained for soil temperature and moisture content (at depths of 10 and 40 cm), normalized relative vegetation index, relative humidity, maximum and average air temperature, relative air humidity. During the testing of the model in 2021, false-negative (erroneous) prediction was made for 2 districts.Discussion. The model proved to be most efficient in prediction of occurrence or absence of cases. More accurate quantitative prediction may be difficult due to subjective factors (including misdiagnosing CCHF cases without hemorrhagic manifestations and administering treatment for other conditions with similar symptoms).Conclusion. The tests of the model demonstrate its potential. The practical application of the prediction will make healthcare workers more alert when screening and detecting CCHF cases.