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. 2020 Sep:98:90-108.
doi: 10.1016/j.ijid.2020.06.058. Epub 2020 Jun 20.

Spatial 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 Pourghasemi  1 , Soheila Pouyan  2 , Bahram Heidari  3 , Zakariya Farajzadeh  4 , Seyed Rashid Fallah Shamsi  5 , Sedigheh Babaei  6 , Rasoul Khosravi  7 , Mohammad Etemadi  8 , Gholamabbas Ghanbarian  9 , Ahmad Farhadi  10 , Roja Safaeian  11 , Zahra Heidari  12 , Mohammad Hassan Tarazkar  13 , John P Tiefenbacher  14 , Amir Azmi  15 , Faezeh Sadeghian  16
Affiliations

Spatial 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 Pourghasemi et al. Int J Infect Dis. 2020 Sep.

Abstract

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/0.1M 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/0.1M 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.

Keywords: Heatmap; Iran; Outbreak trend; Regression model; Risk map; Spatial modeling.

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Figures

Figure 1
Figure 1
Trend of COVID-19 outbreak in the provinces of Iran since February 19, 2020.
Figure 2
Figure 2
The flowchart of methodologies for risk mapping coronavirus in Iran.
Figure 3
Figure 3
Predictor variables used for COVID-19 risk mapping in Iran.
Figure 3
Figure 3
Predictor variables used for COVID-19 risk mapping in Iran.
Figure 3
Figure 3
Predictor variables used for COVID-19 risk mapping in Iran.
Figure 4
Figure 4
Coronavirus cases on six continents.
Figure 5
Figure 5
Distribution of the GR of total active cases in Iran.
Figure 6
Figure 6
Distribution of the GR of total death cases in Iran.
Figure 7
Figure 7
COVID-19 infected patients per 100,000 population and the cumulative curve in Iran’s provinces.
Figure 8
Figure 8
Relationship between various age groups and sex in active cases.
Figure 9
Figure 9
Relationship between various age groups and sex in death cases.
Figure 10
Figure 10
Heatmap of coronavirus outbreak and classification of Iran’s provinces.
Figure 11
Figure 11
Heatmap of coronavirus outbreak and classification of the world's countries.
Figure 12
Figure 12
Test for the best fit of regression models for the number of infected with coronavirus and days after the first day of infected cases in the 31 Iranian provinces.
Figure 13
Figure 13
Relation of the population of Iran’s provinces with the number of infected cases with coronavirus.
Figure 14
Figure 14
Actual deaths cases versus estimated cases.
Figure 15
Figure 15
Comparing trends of deaths in Iran to the world.
Figure 16
Figure 16
Relative importance of variables in outbreak trend of using the LASSO MLT.
Figure 17
Figure 17
COVID-19 risk map of active cases in Iran ; a) On March 11, and b) On March 18.
Figure 18
Figure 18
The ROC curve of RF MLT for mapping risk of COVID-19; a) On March 11, and b) On March 18.
See this image and copyright information in PMC

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