The Impact of Temperature, Humidity, and Precipitation on COVID-19 Cases: A Study Across National and Subnational Levels in Pakistan

Ishtiaq Ahmad (), Mustajab Ali, Hadiya Asghar, Miyoko Okamoto, Yoshihisa Shirayama, Zoofa Talha, Aida Uzakova, Hafiz Sultan Ahmad and Motoyuki Yuasa
Additional contact information
Ishtiaq Ahmad: Department of Global Health Research, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
Mustajab Ali: Department of Civil Engineering, Mirpur University of Science and Technology (MUST), Mirpur 10250, Pakistan
Hadiya Asghar: Department of Health and Medical Sciences, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
Miyoko Okamoto: Department of Global Health Research, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
Yoshihisa Shirayama: Department of Global Health Research, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
Zoofa Talha: Health Services Academy, Islamabad 44000, Pakistan
Aida Uzakova: Central Campus, International Higher School of Medicine, Bishkek 720054, Kyrgyzstan
Hafiz Sultan Ahmad: Faculty of Science and Technology, University of Central Punjab, Lahore 54000, Pakistan
Motoyuki Yuasa: Department of Global Health Research, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan

J, 2025, vol. 8, issue 3, 1-11

Abstract: Meteorological variables play a significant role in the transmission of viruses such as influenza and the coronavirus pandemic (COVID-19). Previous studies have identified the relationship between changes in meteorological variables, humidity, rainfall, and temperature, and the infection rate of COVID-19 at the national level in Pakistan. However, the current study applied the logistic regression analysis technique to determine such a relationship on a more detailed scale, that is, subnational levels in addition to the national level in Pakistan, using a long-term analysis of two years of COVID-19 data. At the subnational level, the logistic regression analysis technique was applied, with infection rate as the predictive variable. The results showed an increase in the infection rate of COVID-19 with increasing humidity levels. In contrast, an increase in temperature has slowed the spread of COVID-19 cases at both the national and subnational levels. The minimum temperature was statistically significant ( p < 0.001) for provinces, KPK and Sindh. Also, two federal territories, AJK and Islamabad, showed statistically significant p -values. At the national level, both maximum temperature and humidity showed such values that is, p < 0.001. We believe that this is the first study conducted in Pakistan to explore the direct and indirect relationship between variables such as temperature (min and max), humidity, and rainfall as predictive parameters for COVID-19 infection rates at a detailed level. The pattern observed in this study can help us predict the future spread of COVID-19, subject to climatic parameters in Pakistan at both the national and subnational levels.

Keywords: coronavirus cases; humidity; rainfall; statistical modeling; temperature; public health; Pakistan (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-8800/8/3/21/pdf (application/pdf)
https://www.mdpi.com/2571-8800/8/3/21/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjopen:v:8:y:2025:i:3:p:21-:d:1685668

Access Statistics for this article

J is currently edited by Ms. Angelia Su

More articles in J from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().