The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran

Abstract Aim: This study investigates the association between meteorological factors (pressure, humidity, and temperature) with COVID-19 hospitalization and mortality in Gonabad city of Iran, from April 2019 to January 2021. This study aimed to prediction of weather pattern’s impact on the COVID-19 monthly trend in a region of Iran. Methods: COVID-19 cases and meteorological factors data were collected over the Gonabad University of Medical Science registration bank and Meteorological Organization of Gonabad from April 2019 to January 2021, respectively. Multivariable time series autoregressive integrated moving average (ARIMA) models and correlation in multiplicative was used for forecast COVID-19 incidence and combines both a long-term upward trend and a pronounced seasonal pattern in COVID-19 cases, respectively. Results: Descriptive analysis revealed that the highest temperature and humidity were recorded in the summer and autumn 2020, respectively. Moreover, coronary hospitalization and mortality peaks were occurrence at 2020 in autumn (1059 cases) and summer (133 deaths), respectively. Time-series analysis showed. Seasonal amplitude indicates regular rise and fall in the number of cases over distinct periods (~10.73). The ARIMA model highlighted fluctuations in COVID-19 cases, closely tied to changes in humidity and temperature. Residual standard deviation (1.62) highlighting the presence of unaccounted for factors influencing case variability. Conclusion: Meteorological factors significantly influence on coronary mortality trends that are highlighting to need public health strategies to consider climatic conditions. Future research should integrate more detailed meteorological data and explore additional environmental factors to enhance predictive models for healthcare planning.