Volume 11, Issue 1 (3-2022)
JCHR 2022, 11(1): 36-44 |
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Madadizadeh F, Ghelmani S Y, Fallah Tafti T. Spatial analysis of the COVID-19 prevalence pattern in Yazd province, Central part of Iran (February 2020 to January 2021). JCHR 2022; 11 (1) :36-44
URL: http://jhr.ssu.ac.ir/article-1-865-en.html
URL: http://jhr.ssu.ac.ir/article-1-865-en.html
1- Center for healthcare Data modeling, Departments of biostatistics and Epidemiology, School of public health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2- Assistant Professor of Internal Medicine, Department of Internal Medicine, School of Medicine, Shahid Sadoughi University of Medical Sciences Yazd, Iran
3- MSc student of Biostatistics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
2- Assistant Professor of Internal Medicine, Department of Internal Medicine, School of Medicine, Shahid Sadoughi University of Medical Sciences Yazd, Iran
3- MSc student of Biostatistics, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Abstract: (1438 Views)
Abstract
Introduction: . Yazd province is the center of Iran and the highway for travelers to other cities. This province is susceptible to disease transmission in Iran. this study aimed to spatial analysis of corona virus prevalence, predicting the spread and determination of hot spot areas in Yazd province, central part of Iran.
Methods: This analytical Cross-sectional study was conducted in Yazd province from February 2020 to January 2021. Patients with COVID-19 admitted to hospitals in Yazd province were selected by census. Required information includes the number of patients as well as their place of residence were collected through the hospital information system (HIS) of Shahid Sadoughi Hospital in Yazd, Iran. The inclusion criteria were positive polymerase chain reaction (PCR) test for COVID-19 and registration of patient information in the hospital emergency department. After collecting the data, it was entered into the ArcGIS software is 9.3.1. software. Moran's I measure and chi square test were used to data analysis. Significant level were considered 5 %.
Results: Overall disease prevalence in Yazd province was equal to 0.0053. The prevalence of disease was higher in men (55.7% , 3412 cases). The highest prevalence of the disease occurred in Yazd city (0.0096) and the highest death occurred in Meybod city (20.8%). Bahabad city also had the highest number of transfer (2.7%). Areas one (15.2% and 932 patients) and two (15.9% and 975 patients) of Yazd city were the most infected areas. There was no significant spatial pattern between the prevalence of the disease in the cities (Moran's Index: 0.18, P-value = 0.58).
Conclusion: There was no spatial pattern in the prevalence of the disease and only in the city of Yazd, regions one and two need the special attention of policymakers.
Covid-19 disease, geographical distribution, Yazd, Iran.
Introduction: .
Methods: This analytical Cross-sectional study was conducted in Yazd province from February 2020 to January 2021. Patients with COVID-19 admitted to hospitals in Yazd province were selected by census. Required information includes the number of patients as well as their place of residence were collected through the hospital information system (HIS) of Shahid Sadoughi Hospital in Yazd, Iran. The inclusion criteria were positive polymerase chain reaction (PCR) test for COVID-19 and registration of patient information in the hospital emergency department. After collecting the data, it was entered into the ArcGIS software is 9.3.1. software. Moran's I measure and chi square test were used to data analysis. Significant level were considered 5 %.
Results: Overall disease prevalence in Yazd province was equal to 0.0053. The prevalence of disease was higher in men (55.7% , 3412 cases). The highest prevalence of the disease occurred in Yazd city (0.0096) and the highest death occurred in Meybod city (20.8%). Bahabad city also had the highest number of transfer (2.7%). Areas one (15.2% and 932 patients) and two (15.9% and 975 patients) of Yazd city were the most infected areas. There was no significant spatial pattern between the prevalence of the disease in the cities (Moran's Index: 0.18, P-value = 0.58).
Conclusion: There was no spatial pattern in the prevalence of the disease and only in the city of Yazd, regions one and two need the special attention of policymakers.
Covid-19 disease, geographical distribution, Yazd, Iran.
Review: Research |
Subject:
General
Received: 2022/01/30 | Accepted: 2022/03/28 | Published: 2022/03/30
Received: 2022/01/30 | Accepted: 2022/03/28 | Published: 2022/03/30
References
1. 1. Bahariniya S, Madadizadeh F, Ezati Asar M. Using Face Masks as a Cheap and Critical Tool in Reducing COVID-19 Spread. Critical Comments in Biomedicine. 2021;2(2). [DOI:10.18502/ccb.v2i2.7398]
2. Bahariniya S, Madadizadeh F. Alcohol: A Double-Edged Sword in the Fight Against COVID-19. Health Scope. 2021:2-. [DOI:10.5812/jhealthscope.113136]
3. Murad A, Khashoggi BF. Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia. ISPRS International Journal of Geo-Information. 2020;9(5):328. [DOI:10.3390/ijgi9050328]
4. Franch-Pardo BN. Spatial analysis and GIS in the study of COVID-19. A review Ivan Franch-Pardo, Brian M. Napoletano b, Fernando Rosete-Verges, Lawal Billa c.
5. Sarwar S, Waheed R, Sarwar S, Khan A. COVID-19 challenges to Pakistan: Is GIS analysis useful to draw solutions? Science of the Total Environment. 2020;730:139089. [DOI:10.1016/j.scitotenv.2020.139089]
6. Chen Y. Scaling, fractals and the spatial complexity of cities. Handbook on Cities and Complexity: Edward Elgar Publishing; 2021. [DOI:10.4337/9781789900125.00017]
7. Arab-Mazar Z, Sah R, Rabaan AA, Dhama K, Rodriguez-Morales AJ. Mapping the incidence of the COVID-19 hotspot in Iran-Implications for Travellers. Travel Medicine and Infectious Disease. 2020;34:101630. [DOI:10.1016/j.tmaid.2020.101630]
8. Poirier C, Luo W, Majumder MS, Liu D, Mandl KD, Mooring TA, et al. The role of environmental factors on transmission rates of the COVID-19 outbreak: an initial assessment in two spatial scales. Scientific reports. 2020;10(1):1-11. [DOI:10.1038/s41598-020-74089-7]
9. Stopka TJ, Goulart MA, Meyers DJ, Hutcheson M, Barton K, Onofrey S, et al. Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach. BMC infectious diseases. 2017;17(1):1-11. [DOI:10.1186/s12879-017-2400-2]
10. Peng L, Yang W, Zhang D, Zhuge C, Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint arXiv:200206563. 2020. [DOI:10.1101/2020.02.16.20023465]
11. Park JY. Spatial visualization of cluster-specific Covid-19 transmission network in South Korea during the early epidemic phase. medRxiv. 2020. [DOI:10.1101/2020.03.18.20038638]
12. Andersen LM, Harden SR, Sugg MM, Runkle JD, Lundquist TE. Analyzing the spatial determinants of local Covid-19 transmission in the United States. Science of the Total Environment. 2021;754:142396. [DOI:10.1016/j.scitotenv.2020.142396]
13. Bliddal S, Banasik K, Pedersen OB, Nissen J, Cantwell L, Schwinn M, et al. Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients. Scientific reports. 2021;11(1):1-11. [DOI:10.1038/s41598-021-92045-x]
14. Jin J-M, Bai P, He W, Wu F, Liu X-F, Han D-M, et al. Gender differences in patients with COVID-19: focus on severity and mortality. Frontiers in public health. 2020:152. [DOI:10.1101/2020.02.23.20026864]
15. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The lancet. 2020;395(10229):1054-62. [DOI:10.1016/S0140-6736(20)30566-3]
16. Wu JT, Leung K, Bushman M, Kishore N, Niehus R, de Salazar PM, et al. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nature medicine. 2020;26(4):506-10. [DOI:10.1038/s41591-020-0822-7]
17. Mirjalili MR, Namayandeh SM, Lotfi MH, Dehghani MR, Mirzaei M, Talebi AR, et al. COVID-19 Seroepidemiology Study of Yazd Province, First Peak, Spring 2020: A Population-Based Cross-Sectional Study. Journal of Shahid Sadoughi University of Medical Sciences. 2020. [DOI:10.18502/ssu.v28i12.5780]
18. Saffary T, Adegboye OA, Gayawan E, Elfaki F, Kuddus MA, Saffary R. Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities. Frontiers in public health. 2020:728. [DOI:10.3389/fpubh.2020.579190]
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