Crimean-Congo Hemorrhagic Fever Modeling Based on Climate Variables in Iran, Iraq, Kazakhstan, Russia and Turkey (1999-2022) - Dr. Irina Lizinfeld; Dr. Mohammad Fereidouni; Dr. Natalia Pshenichnaya; Dr. Gulzhan Abuova; Dr. Mohammad Reza Shirzadi; Dr. Hanan Abdulghafoor Khaleel; Dr. Riyadh Al Hilfi; Dr. Maryam Keshtkar-Jahromi (International Journal of Infectious Diseases)
Introduction: Crimean-Congo Hemorrhagic Fever (CCHF) is a tick-borne viral disease endemic in Africa, Asia and Europe. Variable numbers of CCHF cases with occasional significant rise have been observed in endemic regions. Global warming has impacted epidemiology of infectious diseases. Studying the impact of climate variables on geographic distribution of CCHF helps in designing predictive models fоr outbreak preparedness. We aimed to study the impact of climate variables on CCHF incidence in Middle east endemic regions (1999 to 2022). Methods: In this retrospective study, we collected data on climate variables (Annual Average Temperature (AAT), Annual Highest Average Temperature (AHAT), Annual Lowest Average Temperature (ALAT), and Average Annual Precipitation (AAP)) in Russia (6 endemic regions: Astrakhan, Dagestan, Kalmykia, Rostov, Stavropol, and Volgograd), Kazakhstan (3 endemic regions: Jambul, Kyzylorda, and Turkestan), Turkey, Iran, and Iraq (climate change knowledge portal: https://climateknowledgeportal.worldbank.org/). Annual number of CCHF cases were obtained from published, unpublished validated data, and surveillance systems through local public health authorities. Statistical analyses were performed using fixed effects regression with panel by dummy variables for each country over time (IBM SPSS Statistics 29.0, results significant at p < 0.05). Results: Fixed effects regression model indicates significant associations between CCHF cases over time and ALAT (b = -61.109, se = 20.410, p < 0.004), AHAT (b = 55.885, se = 15.250, p < 0.001), and AAP (b = 1.332, se = 0.140, p < 0.001). No statistically significant correlation was observed for AAT. A 1°C decrease in ALAT was associated with a decrease of approximately 61 CCHF cases. A 1°C increase in AHAT was associated with an increase of approximately 56 CCHF cases. A 1 mm increase in AAP was associated with approximately 1 additional CCHF case. The model exhibited a strong correlation (rxy = 0.766, p < 0.001) between climate variables and CCHF cases (substantial connection on the Chaddock scale). The 58.7% of the variability in CCHF cases was explained by its correlation with considered climate variables. Discussion: The focus of this study was on only five countries in Middle east and four climate variables. A significant positive correlation was found between temperature and precipitation and CCHF incidence. The result supports the growing evidence of global warming impact on infectious diseases epidemiology including CCHF. To design a more precise predictive model for CCHF, other climate factors, tick& animal population, human behavior, environmental exposure, agriculture, and locally practiced tick exposure preventive measures should be considered. Conclusion: Climate variables (temperature and precipitation) are considered variables in CCHF predictive model. A holistic understanding of complex interactions of other variables is critical to develop targeted and effective strategies to mitigate CCHF in the face of ongoing climate change.