Volume 23, Issue 2 (March-April 2024)
Payesh 2024, 23(2): 271-287 |
Back to browse issues page
Ethics code: IR.UMSHA.REC.1399.886
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Moradi A, Mirzaei M, Ameri P, Zangeneh M, Moradi A, Darabi F. Spatial Analysis of Covid-19 Disease in Hamadan Province. Payesh 2024; 23 (2) :271-287
URL: http://payeshjournal.ir/article-1-2111-en.html
URL: http://payeshjournal.ir/article-1-2111-en.html
Ali Moradi1 
, Mohammad Mirzaei2 , Pegah Ameri3 , Moharram Zangeneh4 , Abbas Moradi5 , Fateme Darabi 6
, Mohammad Mirzaei2 , Pegah Ameri3 , Moharram Zangeneh4 , Abbas Moradi5 , Fateme Darabi 6
1- Occupational Health and Safety Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
2- Department of Epidemiology, School Health, Hamadan University of Medical Sciences, Hamadan, Iran
3- Department of Epidemiology, School Health, Hamadan University of Medical Sciences, Hamadan, Hamadan, Iran
4- Health Services Management Department, School of Medical Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
5- Department of community medicine, Hamadan University of Medical Sciences, Hamadan, Iran
6- Department of Public Health, Asadabad school of Medical Sciences, Asadabad, Iran
2- Department of Epidemiology, School Health, Hamadan University of Medical Sciences, Hamadan, Iran
3- Department of Epidemiology, School Health, Hamadan University of Medical Sciences, Hamadan, Hamadan, Iran
4- Health Services Management Department, School of Medical Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
5- Department of community medicine, Hamadan University of Medical Sciences, Hamadan, Iran
6- Department of Public Health, Asadabad school of Medical Sciences, Asadabad, Iran
Abstract: (5738 Views)
Objective(s): With the spread of COVID-19 pandemic, different countries have implemented various intervention to control the disease. Providing information on how the disease spreads and local risk factors can help policymakers and managers to control the disease. The aim of this study was to analyze the spatial factors of Covid-19 disease in Hamadan province, Iran.
Methods: All cases of Covid-19 in Hamadan province from the beginning of the epidemic (Jan 2020) to the end of March 2021 with a confirmed diagnosis of Covid-19 disease based on PCR test were included in the study. The required information was collected through the Covid-19 case registration system in Hamadan University of medical science, other provincial institutions and the Statistics Center of Iran. Statistical analyzes were performed in two parts: descriptive and analytical. Zoning maps were used to show the distribution of disease cases. Getis-Ord G statistic was used in the analytical analysis to analyze the hot spots.
Results: A total of 25197 patients suspected of Covid-19 were studied. Of these 10366 were positive based on PCR test. There were 1510 confirmed positive cases of the disease who died during the period of investigation. Population density, employment rate, development index, illiteracy rate, distance from the provincial center, air temperature, number of frosty days, average air humidity showed a significant relationship with the incidence of positive cases of Covid-19 in Hamadan province.
Conclusion: Based on the findings, the most important variables related to the increase in the death of covid-19 included the increase in population density and the level of development. The preparation of risk maps using GIS can help to plan a rapid response to the COVID-19 epidemic, focus on prevention programs in high-risk areas, and plan the necessary strategies appropriate to the epidemic trend to control COVID-19 and similar epidemics in the future.
Methods: All cases of Covid-19 in Hamadan province from the beginning of the epidemic (Jan 2020) to the end of March 2021 with a confirmed diagnosis of Covid-19 disease based on PCR test were included in the study. The required information was collected through the Covid-19 case registration system in Hamadan University of medical science, other provincial institutions and the Statistics Center of Iran. Statistical analyzes were performed in two parts: descriptive and analytical. Zoning maps were used to show the distribution of disease cases. Getis-Ord G statistic was used in the analytical analysis to analyze the hot spots.
Results: A total of 25197 patients suspected of Covid-19 were studied. Of these 10366 were positive based on PCR test. There were 1510 confirmed positive cases of the disease who died during the period of investigation. Population density, employment rate, development index, illiteracy rate, distance from the provincial center, air temperature, number of frosty days, average air humidity showed a significant relationship with the incidence of positive cases of Covid-19 in Hamadan province.
Conclusion: Based on the findings, the most important variables related to the increase in the death of covid-19 included the increase in population density and the level of development. The preparation of risk maps using GIS can help to plan a rapid response to the COVID-19 epidemic, focus on prevention programs in high-risk areas, and plan the necessary strategies appropriate to the epidemic trend to control COVID-19 and similar epidemics in the future.
type of study: Descriptive |
Subject:
Epidemiologhy
Received: 2023/03/31 | Accepted: 2023/12/5 | ePublished ahead of print: 2024/01/3 | Published: 2024/03/10
Received: 2023/03/31 | Accepted: 2023/12/5 | ePublished ahead of print: 2024/01/3 | Published: 2024/03/10
References
1. Hawkins RB, Charles EJ, Mehaffey JH. Socio-economic status and COVID-19-related cases and fatalities. Public Health 2020;189:129-34 [DOI:10.1016/j.puhe.2020.09.016]
2. Mollalo A, Vahedi B, Rivera K. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the Total Environment 2020; 728: 1-8 [DOI:10.1016/j.scitotenv.2020.138884]
3. Collivignarelli MC, Abbà A, Bertanza G, Pedrazzani R, Ricciardi P, Miino MC. Lockdown for CoViD-2019 in Milan: What are the effects on air quality? Science of The Total Environment 2020;732:139280 [DOI:10.1016/j.scitotenv.2020.139280]
4. Strang P, Fürst P, Schultz T. Excess deaths from COVID-19 correlate with age and socio-economic status. A database study in the Stockholm region. Upsala Journal of Medical Sciences 2020;125:297-304 [DOI:10.1080/03009734.2020.1828513]
5. Chan JF-W, Yuan S, Kok K-H, To KK-W, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet 2020;395:514-23 [DOI:10.1016/S0140-6736(20)30154-9]
6. Kent AJ. Mapping and Counter-Mapping COVID-19: From Crisis to Cartocracy. The Cartographic Journal 2020; 57:187-95 [DOI:10.1080/00087041.2020.1855001]
7. L. R. de A. Morais, G. S. da S. Gomes. Applying Spatio -temporal Scan Statistics and Spatial Autocorrelation Statistics to identify Covid-19 clusters in the world - A Vaccination Strategy?. Spatial and Spatio -temporal Epidemiology 2021;39:100461 [DOI:10.1016/j.sste.2021.100461]
8. Eskandari N, Raisi M, Abbasi M. Explanation of the Management Challenges of Health System Reform in Health Care Domain city QOM: A Qualitative Study (Iran). Qom University of Medical Sciences Journal 2019;13:78-89 [Persian] [DOI:10.29252/qums.13.2.78]
9. Liu J, Liao X, Qian S, Yuan J, Wang F, Liu Y, et al. Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020. Emerging Infectious Diseases 2020;26: 1320-1323 [DOI:10.3201/eid2606.200239]
10. Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Annals of Internal Medicine 2020; 172:577-82 [DOI:10.7326/M20-0504]
11. Diao B, Wang C, Tan Y, Chen X, Liu Y, Ning L, Chen L, Li M, Liu Y, Wang G, Yuan Z. Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19). Frontiers in immunology 2020:3:827 [DOI:10.1101/2020.02.18.20024364]
12. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The lancet 2020;395:1054-62 [DOI:10.1016/S0140-6736(20)30566-3]
13. You C, Deng Y, Hu W, Sun J, Lin Q, hou F, et al. Estimation of the time-varying reproduction number of COVID-19 outbreak in China. International Journal of Hygiene and Environmental Health 2020; 228:113555 [DOI:10.1016/j.ijheh.2020.113555]
14. Zhang S, Diao M, Yu W, Pei L, Lin Z, Chen D. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis. International Journal of Infectious Diseases 2020;93:201-4 [DOI:10.1016/j.ijid.2020.02.033]
15. Gosoniu L, Msengwa A, Lengeler C, Vounatsou P. Spatially explicit burden estimates of malaria in Tanzania: bayesian geostatistical modeling of the malaria indicator survey data. PLoS ONE 2012;7: e23966 [DOI:10.1371/journal.pone.0023966]
16. Siddik, M, Mohamed SM, Thowiba E.A, Rania M, Elmustafa SA, Rashid AS. Development of Health Digital GIS Map for Tuberculosis Disease Distribution Analysis in Sudan. Journal of Healthcare Engineering 2023: 6479187 [DOI:10.1155/2023/9780528]
17. Jijón, A F, Costa R, Nicova K, iDATA, PLANTS, Furnari G. Review of the Use of GIS in Public Health and Food Safety. EFSA supporting publication 2022; 19: 7639E [DOI:10.2903/sp.efsa.2022.EN-7639]
18. Causey K, Burkart K, Collman G, Hu H, Brauer M, London SJ. Increasing the Impact of Environmental Epidemiology in the Global Burden of Disease Project. Epidemiology. 2021; 3:1-5 [DOI:10.1097/EDE.0000000000001273]
19. Wu F ZS, Bin Y, Chen YM, Wang W, Song ZG, Hu Y, et al. A new coronavirus associated with human respiratory disease in China. Nature 2020; 579:265-9 [DOI:10.1038/s41586-020-2008-3]
20. Gatrell AC, Bailey TC. Interactive spatial data analysis in medical geography. Social science & medicine 1996; 42:843-55 [DOI:10.1016/0277-9536(95)00183-2]
21. Liu Y, Li B. Epidemic Situation of Novel Coronavirus Pneumonia in China mainland. Medrxiv 2020; 2020-02 [DOI:10.1101/2020.02.17.20024034]
22. Joyce K. To me it's just another tool to help understand the evidence: Public health decisionmakers' perceptions of the value of geographical information systems (GIS). Health Place 2009; 15: 831-840 [DOI:10.1016/j.healthplace.2009.01.004]
23. Leah Rosenkrantz. Leveraging geographic information systems (GIS) for environmental public health practice. Environmental Health Review 2022; 65: 31-36 [DOI:10.5864/d2022-013]
24. Waller LA, Carol A, Gotway (2020) Applied spatial statistics for public health data. 2004; 368 [DOI:10.1002/0471662682]
25. Mathur M. Spatial autocorrelation analysis in plant population: an overview. J Appl and Nat Sci 2015;7:501-13 [DOI:10.31018/jans.v7i1.639]
26. Ren Z, Wang D, Ma A, Hwang J, Bennett A, Sturrock HJ, Fan J, Zhang W, Yang D, Feng X, et al. Predicting malaria vector distribution under climate change scenarios in China: challenges for malaria elimination. Sci Rep 2016;6:20604 [DOI:10.1038/srep20604]
27. Zambrano LI, Sierra M, Lara B, Rodríguez-Núñez I, Medina MT, Lozada-Riascos CO, Rodríguez-Morales AJ. Estimating and mapping the incidence of dengue and chikungunya in Honduras during 2015 using Geographic Information Systems (GIS). Journal of infection and public health 2017; 10:446-56 [DOI:10.1016/j.jiph.2016.08.003]
28. using Geographic Information Systems (GIS). J Infect Public Health 2017. 2015;10:446-56 [DOI:10.1016/j.jiph.2016.08.003]
29. Musso D, Rodriguez-Morales AJ, Levi JE, Cao-Lormeau VM, Gubler DJ. Unexpected outbreaks of arbovirus infections: lessons learned from the Pacific and tropical America. Lancet Infect Dis 2018;18:e355-61 [DOI:10.1016/S1473-3099(18)30269-X]
30. Bazargan M, Amirfakhriyan M. Geographical Analysis of COVID-19 Epidemiology in Iran with Exploratory Spatial Data Analysis Approach (ESDA). Journal of Military Medicine 2020; 22:542-552 [Persian]
31. Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of The Total Environment 2020; 28:138884 [DOI:10.1016/j.scitotenv.2020.138884]
32. 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]
33. Hilbe JM. Negative binomial regression: 1st Edition, Cambridge University Press; Cambridge University Press, Cambridge: UK, 2011
34. Kanga S, Sudhanshu, Meraj G, Farooq M, Nathawat MS, Singh SK. Reporting the management of COVID-19 threat in India using remote sensing and GIS based approach. Geocarto International 2020;17:1-8 [DOI:10.1080/10106049.2020.1778106]
35. Ahasan R, Alam MS, Chakraborty T, Hossain MM. Applications of GIS and geospatial analyses in COVID-19 research: a systematic review. F1000Research 2020 27;9:1379 [DOI:10.12688/f1000research.27544.1]
36. Mollalo A, Vahedi B, Rivera KM. GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of The Total Environment 2020;728:138884 [DOI:10.1016/j.scitotenv.2020.138884]
37. Ahmed F, Ahmed Ne, Pissarides C, Stiglitz J. Why inequality could spread COVID-19. The Lancet Public Health 2020;5:e240 [DOI:10.1016/S2468-2667(20)30085-2]
38. Ma Y, Zhao Y, Liu J, He X, Wang B, Fu S, et al. Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Science of The Total Environment 2020;724:138226 [DOI:10.1016/j.scitotenv.2020.138226]
39. Hasan NA, Haque MM. Predict the next moves of COVID-19: reveal the temperate and tropical countries scenario. MedRxiv 2020;2020-04 [DOI:10.1101/2020.04.04.20052928]
40. Rahman MH, Zafri NM, Ashik FR, Waliullah M. GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh. medRxiv 2020:2020-08 [DOI:10.1101/2020.08.16.20175976]
41. 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]
42. Shakiba M, Nazari SSH, Mehrabian F, Rezvani SM, Ghasempour Z, Heidarzadeh A. Seroprevalence of COVID-19 virus infection in Guilan province, Iran. MedRxiv 2020. Available at: https://www.medrxiv.org/content/10.1101/2020.04.26.20079244V1.full [Access date 01/03/2024] [DOI:10.1101/2020.04.26.20079244]
Send email to the article author
| Rights and Permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |