Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran

Submitted: 25 December 2021
Accepted: 19 April 2022
Published: 8 June 2022
Abstract Views: 619
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This study aimed at detecting space-time clusters of COVID-19 cases in Fars Province, Iran and at investigating their potential association with meteorological factors, such as temperature, precipitation and wind velocity. Time-series data including 53,554 infected people recorded in 26 cities from 18 February to 30 September 2020 together with 5876 meteorological records were subjected to the analysis. Applying a significance level of P<0.05, the analysis of space-time distribution of COVID-19 resulted in nine significant outbreaks within the study period. The most likely cluster occurred from 27 March to 13 July 2020 and contained 11% of the total cases with eight additional, secondary clusters. We found that the COVID-19 incidence rate was affected by high temperature (OR=1.64; 95% CI: 1.44-1.87), while precipitation and wind velocity had less effect (OR=0.84; 95% CI: 0.75-0.89 and OR=0.27; 95% CI: 0.14-0.51), respectively.

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Ahmadi M, Sharifi A, Dorosti S, Ghoushchi SJ, Ghanbari N, 2020. Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Sci Total Environ 729:138705. DOI: https://doi.org/10.1016/j.scitotenv.2020.138705
Akoglu H, 2018. User’s guide to correlation coefficients. Turk J Emerg Med 18:91-93. DOI: https://doi.org/10.1016/j.tjem.2018.08.001
Briz-Redón Á, Serrano-Aroca Á, 2020. A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. Sci Total Environ 728:138811. DOI: https://doi.org/10.1016/j.scitotenv.2020.138811
Carlson CJ, Gomez AC, Bansal S, Ryan, SJ, 2020. Misconceptions about weather and seasonality must not misguide COVID-19 response. Nat Commun 11:1-4. DOI: https://doi.org/10.1038/s41467-020-18150-z
Coccia M, 2020. How do low wind speeds and high levels of air pollution support the spread of COVID-19? Atmos Pollut Res 12:437-45. DOI: https://doi.org/10.1016/j.apr.2020.10.002
Desjardins M, Hohl A, Delmelle E, 2020. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Appl Geogr 118:102202. DOI: https://doi.org/10.1016/j.apgeog.2020.102202
Gupta S, Raghuwanshi GS, Chanda A, 2020. Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020. Sci Total Environ 728:138860. DOI: https://doi.org/10.1016/j.scitotenv.2020.138860
Haque SE, Rahman M, 2020. Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh. Environ Sci Policy 114:253-5. DOI: https://doi.org/10.1016/j.envsci.2020.08.012
Jamil T, Alam I, Gojobori T, Duarte CM, 2020. No evidence for temperature-dependence of the COVID-19 epidemic. Front Public Health 8:436. DOI: https://doi.org/10.3389/fpubh.2020.00436
Kan Z, Kwan MP, Wong MS, Huang J, Liu D, 2021. Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong. Sci Total Environ 772:145379. DOI: https://doi.org/10.1016/j.scitotenv.2021.145379
Kasraeian M, Zare M, Vafaei H, Kasraeian M, Asadi N, 2020. COVID-19 pneumonia and pregnancy; a systematic review and meta-analysis. J Matern Fetal Neonatal Med 1-8. DOI: https://doi.org/10.1080/14767058.2020.1763952
Kulldorff M, 2015. SaTScan - software for the spatial, temporal, and space-time scan statistics. 2016. Boston: Harvard Medical School and Harvard Pilgrim Health Care. Available from http:www.satscan.org
Nelder JA,Wedderburn RW, 1972. Generalized linear models. J R Stat Soc Ser A Stat Soc 135:370-84. DOI: https://doi.org/10.2307/2344614
Notari A, 2021. Temperature dependence of COVID-19 transmission. Sci Total Environ 763:144390. DOI: https://doi.org/10.1016/j.scitotenv.2020.144390
Pawar S, Stanam A, Chaudhari M, Rayudu D, 2020. Effects of temperature on COVID-19 transmission. Medrxiv. DOI: https://doi.org/10.1101/2020.03.29.20044461
Prata DN, Rodrigues W, Bermejo PH, 2020. Temperature significantly changes COVID-19 transmission in (sub) tropical cities of Brazil. Sci Total Environ 729:138862. DOI: https://doi.org/10.1016/j.scitotenv.2020.138862
Rendana M, 2020. Impact of the wind conditions on COVID-19 pandemic: A new insight for direction of the spread of the virus. Urban Clim 34:100680. DOI: https://doi.org/10.1016/j.uclim.2020.100680
Rezaianzadeh A, Zare M, Aliakbarpoor M, Faramarzi H, Ebrahimi M, 2020. Space-Time Cluster Analysis of Malaria in Fars Province-Iran. Int. J Infect 7:e107238. DOI: https://doi.org/10.5812/iji.107238
Rezaianzadeh A, Zare M, Tabatabaee H, Ali-Akbarpour M, Faramarzi H, Ebrahimi M, 2018. Does prospective permutation scan statistics work well with cutaneous leishmaniais as a high-frequency or malaria as a low-frequency infection in Fars province, Iran? Asian Pac J Trop Biomed 8:478-484. DOI: https://doi.org/10.4103/2221-1691.244138
Rosario DK, Mutz YS, Bernardes PC, Conte-Junior CA, 2020. Relationship between COVID-19 and weather: Case study in a tropical country. Int J Hyg Environ Health 229:113587. DOI: https://doi.org/10.1016/j.ijheh.2020.113587
Şahin M, 2020. Impact of weather on COVID-19 pandemic in Turkey. Sci Total Environ 728:138810. DOI: https://doi.org/10.1016/j.scitotenv.2020.138810
Shi P, Dong Y, Yan H, Zhao C, Li XY, Liu W, He M, Tang SX, Xi SH, 2020. Impact of temperature on the dynamics of the COVID-19 outbreak in China. Sci Total Environ 728:138890. DOI: https://doi.org/10.1016/j.scitotenv.2020.138890
Srivastava A, 2021. COVID-19 and air pollution and meteorology-an intricate relationship: a review. Chemosphere 263:128297. DOI: https://doi.org/10.1016/j.chemosphere.2020.128297
Tosepu R, Gunawan J, Effendy DS, Ahmad OAI, Lestari H, Bahar H, Asfian P, 2020. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci Total Environ 725:138436. DOI: https://doi.org/10.1016/j.scitotenv.2020.138436
Wang J, Tang K, Feng K, Lin X, Weifeng L, Chen K, Wang F, 2020. High temperature and high humidity reduce the transmission of COVID-19. Available from: https://ssrn.com/abstract=3551767 2020 DOI: https://doi.org/10.2139/ssrn.3551767
WHO, 2020. Director-General’s opening remarks at the media briefing on COVID-19-11 March 2020. Available from: https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 Accessed: 10 April 2020.
Wu Y, Jing W, Liu J, Ma Q, Yuan J, Wang YP, Du M, Liu M, 2020. Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries. Sci Total Environ 729:139051. DOI: https://doi.org/10.1016/j.scitotenv.2020.139051
Xie J, Zhu Y. 2020. Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ 724:138201. DOI: https://doi.org/10.1016/j.scitotenv.2020.138201
Xie M, Chen Q, 2020. Insight into 2019 novel coronavirus - An updated interim review and lessons from SARS-CoV and MERS-CoV. Int J Infect Dis 94:119-24. DOI: https://doi.org/10.1016/j.ijid.2020.03.071
Zare M, Rezaianzadeh A, Tabatabaee H, Mohsen A, Faramarzi H, Ebrahimi M, 2017. Spatiotemporal clustering of cutaneous leishmaniasis in Fars province, Iran. Asian Pac J Trop Biomed 7:862-9. DOI: https://doi.org/10.1016/j.apjtb.2017.09.011
Zare M, Rezaianzadeh A, Tabatabaee H, Faramarzi H, Mohsen A, Ebrahimi M, 2019a. Determining endemic values of cutaneous leishmaniasis in Iranian Fars province by retrospectively detected clusters and receiver operating characteristic curve analysis. Asian Pac J Trop Biomed 9:359-64. DOI: https://doi.org/10.4103/2221-1691.267636
Zare M, Rezaianzadeh A, Tabatabaee H, Faramarzi H, Mohsen A, Ebrahimi M, 2019b. Establishment of an early warning system for cutaneous leishmaniasis in Fars province, Iran. Asian Pac J Trop Biomed 9:232-9. DOI: https://doi.org/10.4103/2221-1691.260395

How to Cite

Zare, M. ., Semati, A., Mirahmadizadeh, A., Hemmati, A. ., & Ebrahimi, M. . (2022). Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran. Geospatial Health, 17(s1). https://doi.org/10.4081/gh.2022.1065