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    The Effect of Seasonal Factors on the Spread and Mortality of COVID-19: Retrospective Multicenter Study
    (Hacettepe University, 2023) Arıkan, Cüneyt; Yamanoğlu, Adnan; Tekindal, Mustafa Agah; Siliv, Neslihan; Bora, Ejder Saylav; Şener, Aslı; Kanter, Efe
    Objectives: The impact of seasonal factors on the spread of Coronavirus-19 disease (COVID-19) is not yet clear. The aim of this study is to determine the effect of seasonal factors on the spread of COVID-19. Methods: This multicenter retrospective study was performed by collecting 284-day COVID-19 data from two university hospitals in a metropolitan center. Correlations between the seasonal parameters of temperature, humidity, wind, and rainfall and the spread of COVID-19 and its clinical outcomes were evaluated using Spearman’s correlation test. Since no linear relationship was determined between variables exhibiting correlation, all models were tested using non-linear curve estimation regression models. The most powerful of the curve estimation regression models, capable of explaining more than 20% of the changes in COVID-19 parameters, was formulated to explain the expected number of events. Results: A total of 24 2Objectives: The impact of seasonal factors on the spread of Coronavirus-19 disease (COVID-19) is not yet clear. The aim of this study is to determine the effect of seasonal factors on the spread of COVID-19. Methods: This multicenter retrospective study was performed by collecting 284-day COVID-19 data from two university hospitals in a metropolitan center. Correlations between the seasonal parameters of temperature, humidity, wind, and rainfall and the spread of COVID-19 and its clinical outcomes were evaluated using Spearman’s correlation test. Since no linear relationship was determined between variables exhibiting correlation, all models were tested using non-linear curve estimation regression models. The most powerful of the curve estimation regression models, capable of explaining more than 20% of the changes in COVID-19 parameters, was formulated to explain the expected number of events. Results: A total of 24 225 patients were included in the study. The most powerful correlation was between mean daily temperature and daily case numbers (r:-0.643, p<0.00), with case numbers being highest on days when the mean temperature was 7-18℃. Mean temperate was capable of explaining 57% of COVID-19 case numbers (R-Square:0.571, p<0.00), the relationship between them being best explained in the ’S’ curve regression model. The formula ‘’Y=exp(2.07+31.34/x)’’ was obtained for the number of patients expected from the model according to mean temperature. Conclusions: Temperature may be the most effective factor in the spread of COVID-19 and the number of cases may be predicted based on temperature. patients were included in the study. The most powerful correlation was between mean daily temperature and daily case numbers (r:-0.643, p<0.00), with case numbers being highest on days when the mean temperature was 7-18℃. Mean temperate was capable of explaining 57% of COVID-19 case numbers (R-Square:0.571, p<0.00), the relationship between them being best explained in the ’S’ curve regression model. The formula ‘’Y=exp(2.07+31.34/x)’’ was obtained for the number of patients expected from the model according to mean temperature. Conclusions: Temperature may be the most effective factor in the spread of COVID-19 and the number of cases may be predicted based on temperature.