The Prediction of Low and High-Risk Zones of Tehran during COVID-19 by Using the Random Forest Algorithm | ||
| The International Journal of Humanities | ||
| Article 2, Volume 29, Issue 4, 2022, Pages 23-35 PDF (2.25 M) | ||
| Document Type: Original Research | ||
| Authors | ||
| Najmeh Neysani Samani* 1; Mehdi Farokh Anari2 | ||
| 1Associate Professor, Department of Remote Sensing and Geographical Information System, Faculty of Geography, University of Tehran, Iran | ||
| 2Department of Remote Sensing and Geographical Information System, Faculty of Geography, University of Tehran, Iran | ||
| Abstract | ||
| The Coronavirus disease (Covid-19) is one of the infectious and contagious ones called 2019-nCoV acute respiratory disease. Its outbreak was first reported on December 31, 2019, in the Chinese city of Wuhan that quickly spread throughout the country within a few weeks and spread to several other countries, including Italy, the United States, and Germany, within a month. This disease was officially reported in Iran on February 19, 2020. It is important to detect and analyze high risk zones and establish regulations according to the data and the analyses of Geographic Information System (GIS) in epidemiological situations. Meanwhile, the GIS, with its location nature, can be effective in preventing the breakdown of Covid-19 by displaying and analyzing the dangerous zones where people infected with the disease. In fact, recognizing regions based on the risk of getting the disease can influence social restriction policies and urban movement rules in order to prepare daily and weekly plans in different urban regions. In this applied and analytical research, high and low risk zones of Tehran have been identified by using the random forest algorithm which is used for both classification and regression. The algorithm builds decision trees on data samples and then predicts data from each of them, and finally chooses the best solution. In this research, 7 effective criteria have been used in the level of risk of regions toward Covid-19 virus, which is: subway paths and bus for rapid transits, hospitals, administrative and commercial complexes, passageways, population densities and urban traffic. After providing the map of high-risk zones of Covid-19, the Receiver Operating Characteristic curve (ROC) has been used for evaluation. The area under the curve (AUC) obtained from ROC shows an accuracy of 98.8%, which means the high accuracy of this algorithm in predicting high and low zones toward getting the Covid-19 disease. | ||
| Keywords | ||
| Covid-19; Location Analysis; random forest algorithm; Epidemiology | ||
| References | ||
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