Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)
Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and cont...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-09-01
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| Series: | International Journal of Infectious Diseases |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1201971220304938 |
| _version_ | 1811328399133638656 |
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| author | Hamid Reza Pourghasemi Soheila Pouyan Bahram Heidari Zakariya Farajzadeh Seyed Rashid Fallah Shamsi Sedigheh Babaei Rasoul Khosravi Mohammad Etemadi Gholamabbas Ghanbarian Ahmad Farhadi Roja Safaeian Zahra Heidari Mohammad Hassan Tarazkar John P. Tiefenbacher Amir Azmi Faezeh Sadeghian |
| author_facet | Hamid Reza Pourghasemi Soheila Pouyan Bahram Heidari Zakariya Farajzadeh Seyed Rashid Fallah Shamsi Sedigheh Babaei Rasoul Khosravi Mohammad Etemadi Gholamabbas Ghanbarian Ahmad Farhadi Roja Safaeian Zahra Heidari Mohammad Hassan Tarazkar John P. Tiefenbacher Amir Azmi Faezeh Sadeghian |
| author_sort | Hamid Reza Pourghasemi |
| collection | DOAJ |
| description | Objectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran’s fatality rate (deaths/0.1 M pop) is 10.53. Other countries’ fatality rates were, for comparison, Belgium – 83.32, UK – 61.39, Spain – 58.04, Italy – 56.73, Sweden – 48.28, France – 45.04, USA – 35.52, Canada – 21.49, Brazil – 20.10, Peru – 19.70, Chile – 16.20, Mexico– 12.80, and Germany – 10.58. The fatality rate for China is 0.32 (deaths/0.1 M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran’s shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran’s provinces. It is worth noting that using the LASSO MLT to evaluate variables’ importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. Conclusions: We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces. |
| first_indexed | 2024-04-13T15:25:24Z |
| format | Article |
| id | doaj.art-4ece2822a0454452a3744883af05f9d7 |
| institution | Directory Open Access Journal |
| issn | 1201-9712 |
| language | English |
| last_indexed | 2024-04-13T15:25:24Z |
| publishDate | 2020-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Infectious Diseases |
| spelling | doaj.art-4ece2822a0454452a3744883af05f9d72022-12-22T02:41:32ZengElsevierInternational Journal of Infectious Diseases1201-97122020-09-019890108Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)Hamid Reza Pourghasemi0Soheila Pouyan1Bahram Heidari2Zakariya Farajzadeh3Seyed Rashid Fallah Shamsi4Sedigheh Babaei5Rasoul Khosravi6Mohammad Etemadi7Gholamabbas Ghanbarian8Ahmad Farhadi9Roja Safaeian10Zahra Heidari11Mohammad Hassan Tarazkar12John P. Tiefenbacher13Amir Azmi14Faezeh Sadeghian15Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran; Corresponding author.Research Assistant, Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Plant Production and Genetics, School of Agriculture, 7144165186, Shiraz University, Shiraz, IranDepartment of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Horticultural Science, School of Agriculture, Shiraz University, Shiraz, IranDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Molecular Medicine, School of Advanced Medical Sciences and Technologies, Shiraz University of Medicinal Sciences, Shiraz, IranDepartment of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, IranDepartment of Geography, Texas State University, San Marcos, TX 78666, United StatesD.D.S, Msc in Dental Laser, Shiraz, IranShiraz Endocrinology and Metabolism Research Center, Shiraz University of Medical Sciences, Shiraz, IranObjectives: Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods: This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. Results: The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran’s fatality rate (deaths/0.1 M pop) is 10.53. Other countries’ fatality rates were, for comparison, Belgium – 83.32, UK – 61.39, Spain – 58.04, Italy – 56.73, Sweden – 48.28, France – 45.04, USA – 35.52, Canada – 21.49, Brazil – 20.10, Peru – 19.70, Chile – 16.20, Mexico– 12.80, and Germany – 10.58. The fatality rate for China is 0.32 (deaths/0.1 M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran’s shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran’s provinces. It is worth noting that using the LASSO MLT to evaluate variables’ importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. Conclusions: We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.http://www.sciencedirect.com/science/article/pii/S1201971220304938Spatial modelingRisk mapOutbreak trendHeatmapRegression modelIran |
| spellingShingle | Hamid Reza Pourghasemi Soheila Pouyan Bahram Heidari Zakariya Farajzadeh Seyed Rashid Fallah Shamsi Sedigheh Babaei Rasoul Khosravi Mohammad Etemadi Gholamabbas Ghanbarian Ahmad Farhadi Roja Safaeian Zahra Heidari Mohammad Hassan Tarazkar John P. Tiefenbacher Amir Azmi Faezeh Sadeghian Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020) International Journal of Infectious Diseases Spatial modeling Risk map Outbreak trend Heatmap Regression model Iran |
| title | Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020) |
| title_full | Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020) |
| title_fullStr | Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020) |
| title_full_unstemmed | Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020) |
| title_short | Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020) |
| title_sort | spatial modeling risk mapping change detection and outbreak trend analysis of coronavirus covid 19 in iran days between february 19 and june 14 2020 |
| topic | Spatial modeling Risk map Outbreak trend Heatmap Regression model Iran |
| url | http://www.sciencedirect.com/science/article/pii/S1201971220304938 |
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