The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran
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 t...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2025-05-01
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| Series: | International Journal of Environmental Health Engineering |
| Subjects: | |
| Online Access: | https://journals.lww.com/10.4103/ijehe.ijehe_36_24 |
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| author | Yeganeh Azhdary Moghaddam Reza Ahmadi Ali Alami Laleh R. Kalankesh |
| author_facet | Yeganeh Azhdary Moghaddam Reza Ahmadi Ali Alami Laleh R. Kalankesh |
| author_sort | Yeganeh Azhdary Moghaddam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9d48c4c8c1fe4466bca45ab730e5eef7 |
| institution | Kabale University |
| issn | 2277-9183 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | International Journal of Environmental Health Engineering |
| spelling | doaj-art-9d48c4c8c1fe4466bca45ab730e5eef72025-08-20T03:26:34ZengWolters Kluwer Medknow PublicationsInternational Journal of Environmental Health Engineering2277-91832025-05-01142121210.4103/ijehe.ijehe_36_24The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of IranYeganeh Azhdary MoghaddamReza AhmadiAli AlamiLaleh R. KalankeshAim: 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.https://journals.lww.com/10.4103/ijehe.ijehe_36_24autoregressive integrated moving average modelclimatic conditionscovid-19time-series analysis |
| spellingShingle | Yeganeh Azhdary Moghaddam Reza Ahmadi Ali Alami Laleh R. Kalankesh The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran International Journal of Environmental Health Engineering autoregressive integrated moving average model climatic conditions covid-19 time-series analysis |
| title | The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran |
| title_full | The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran |
| title_fullStr | The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran |
| title_full_unstemmed | The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran |
| title_short | The Impact of Meteorological Factors on COVID-19 Trends: Evidence from Time-series Modeling in East of Iran |
| title_sort | impact of meteorological factors on covid 19 trends evidence from time series modeling in east of iran |
| topic | autoregressive integrated moving average model climatic conditions covid-19 time-series analysis |
| url | https://journals.lww.com/10.4103/ijehe.ijehe_36_24 |
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