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Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19)

Year 2021, Volume: 14 Issue: 4, 934 - 943, 01.10.2021

Ömer Bariş İnce Murat Şevik Ahmet Sait

https://doi.org/10.31362/patd.852259

Abstract

Dünyayı etkisi altına alan şiddetli akut solunum yolu sendromu coronavirusu 2 (SARS-CoV-2) salgını, pek çok ülkede ölümcül sonuçlara neden olan önemli bir halk sağlığı sorunudur. Pandemiye yol açacak hastalık yayılımlarının erken dönemde tespit edilebilmesi hastalık kontrol ve eradikasyonunun önemli bir bileşenidir. Hastalık verilerinin ve mekânsal analiz yöntemlerinin birlikte kullanılması, daha etkili hastalık kontrolü ve çözüm stratejileri geliştirmek için büyük bir fırsat sunmaktadır. Bu derlemede coğrafi bilgi sistemlerinin (CBS) epidemiyolojideki uygulamalarını ve salgın hastalıkların kontrolü ve eradikasyonundaki ilişkisini değerlendirmek için özelde COVID-19’u içeren literatüre dayalı bir inceleme yapılmıştır. Epidemiyoloji alanındaki araştırmalarda, araştırılan hastalık verilerinin nasıl bir dağılım ve kümelenme gösterdiği, kısa, orta ve uzun vadede yapılacak kontrol ve eradikasyon müdahalelerini planlama açısından CBS temelli analizler ve modeller giderek önem kazanmaktadır. COVID-19'un kontrol ve eradikasyonunda yaşanan zorluklar, güçlü bulaşıcılık özelliği, uzun bir kuluçka dönemi, nüfus akış ve hareketliliği ve diğer faktörlerle birleştiğinde, hastalığın yayılmasını kontrol etmek ve önlemek için bilimsel ve teknolojik desteğe gereksinim duyulmaktadır. Bu derlemenin amacı, CBS temelli araçların gelişimini anlamak ve COVID-19 pandemisi yönetiminde CBS kullanımı hakkında güncel bilgiler vermektir.

Keywords

COVID-19 SARS CoV-2 CBS mekânsal epidemiyoloji

Supporting Institution

Derleme

Project Number

-

Thanks

-

References

  • 1. Siddell SG, Walker PJ, Lefkowitz EJ, et al. Additional changes to taxonomy ratified in a special vote by the international committee on taxonomy of viruses. Arch Virol 2019;164:943-946. https://doi.org/10.1007/s00705-018-04136-2
  • 2. Gorbalenya A, Baker S, Baric R, et al. The species severe acute respiratory syndrome related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536-544. https://doi.org/10.1038/s41564-020-0695-z
  • 3. Nieto Torres JL, DeDiego ML, Verdiá Báguena C, et al. Severe acute respiratory syndrome coronavirus envelope protein ion channel activity promotes virus fitness and pathogenesis. PLoS Pathogens 2014;10:e1004077. https://doi.org/10.1371/journal.ppat.1004077
  • 4. Harapan H, Itoh N, Yufika A, et al. Coronavirus disease 2019 (COVID-19): a literature review. J Infect Public Health 2020;13:667-673. https://doi.org/10.1016/j.jiph.2020.03.019
  • 5. Zhou P, Yang XL, Wang XG, et al. Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin. Nature 2020;579:270-273. https://doi.org/10.1101/2020.01.22.914952
  • 6. Yi Y, Lagniton PNP, Ye S, Li E, Xu RH. COVID-19: what has been learned and to be learned about the novel coronavirus disease. Int J Biol Sci 2020;16:1753-1766. https://doi.org/10.7150/ijbs.45134
  • 7. Luk HK, Li X, Fung J, Lau SKP, Woo PCY. Molecular epidemiology, evolution and phylogeny of SARS coronavirus. Infect Genet Evol 2019;71:21-30. https://doi.org/10.1016/j.meegid.2019.03.001
  • 8. Badawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERSCoV): a systematic review and meta-analysis. Int J Infect Dis 2016;49:129-133. https://doi.org/10.1016/j.ijid.2016.06.015
  • 9. De Wit E, van Doremalen N, Falzarano D, Munster VJ. SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol 2016;14:523-534. https://doi.org/10.1038/nrmicro.2016.81
  • 10. Tu YF, Chien CS, Yarmishyn AA, et al. A review of SARS-CoV-2 and the ongoing clinical trials. Int J Mol Sci 2020;21:2657. https://doi.org/10.3390/ijms21072657
  • 11. Ministry of Health 2019. Covid 19 Information page. Available at: https://covid19.saglik.gov.tr/. Accessed December 12, 2020.
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  • 13. Frerichs RR. History, maps and the internet: UCLA’s John Snow site. Soc Bulletin Available at: https://www.ph.ucla.edu/epi/snow/socbulletin34(2)3_7_2001.pdf. Accessed September 10, 2020.
  • 14. Carpenter T. The spatial epidemiologic (r)evolution: a look back in time and forward tothe future. Spat Spatiotemporal Epidemiol 2011;2:119-124. https://doi.org/10.1016/j.sste.2011.07.002
  • 15. Prates MO, Kulldorff M, Assunção RM. Relative risk estimates from spatial and space-time scan statistics: are they biased?. Stat Med 2014;33:2634-2644. https://doi.org/10.1002/sim.6143
  • 16. Bailey TC. Spatial statistical methods in health. Cad Saude Publica 2001;217:1083-1098. https://doi.org/10.1590/s0102-311x2001000500011
  • 17. Uluğtekin N, Doğru AÖ. Coğrafi Bilgi Sistemi ve Harita: Kartografya, Ege CBS Sempozyumu, 27-29 Nisan 2005, İzmir, Erişim adresi: https://web.itu.edu.tr/~dogruahm/Cografi%20Bilgi%20Sistemi%20Ve%20Harita_Kartografya.pdf Erişim tarihi 18 Aralık 2020.
  • 18. Karabulut E, Alpar R, Özayar E. Hastalıkların yere göre kümelenmesinde kullanılan yöntemler. İnö Üni Tıp Fak Der 2006;13:37-43.
  • 19. Tonini M, Tuia D, Ratle F. Detection of clusters using space–time scan statistics. Int J of Wildland Fire 2009;18:830-836. https://doi.org/10.1071/WF07167
  • 20. Quantum Geographic Information Systems™ (QGIS). QGIS home page. Avaible at: https://www.qgis.org/tr/site/. Accessed December 07, 2020.
  • 21. Anselin L, Nancy LG, Julia K. Rate transformations and smoothing-2006. Technical Report. Urbana, Spatial Analysis Laboratory, Department of Geography, University of Illinois Avaible at: https://pdfs.semanticscholar.org/88d8/b02de84f97f556cfe0ef5a91a7df229cf363.pdf. Accessed May 06, 2020.
  • 22. Gayır B, Arslan O. Orman yangınlarının CBS tabanlı konumsal istatistik analizi: 2011-2015 Yılları arasında Muğla orman bölge sınırları içerisinde çıkan yangınlar. Anadolu Orman Araşt Derg, 2018;4:44-60.
  • 23. Kulldorff M. SaTScan™ user guide for version 9.6- 2018. SatscanTM Home Page. Avaible at: https://www.satscan.org/cgi-bin/satscan/register.pl/ SaTScan_Users_Guide.pdf?todo=process_userguide_download. Accessed October 11, 2020.
  • 24. Çelik Ş. Zaman serileri analizi ve trafik kazası verilerine uygulanması. Yayınlanmamış Doktora Tezi Ankara Üniversitesi Fen Bilimleri Enstitüsü, Zootekni Anabilim Dalı Ankara, 2013.
  • 25. Kirkup L, Data analysis with excel: an introduction for physical scientists. UK Cambridge: Cambridge University Press. 2002;6-35. Available at: http://libgen.rs/book/index.php?md5=DE89297B02744DBBF8EC0E3DB2345F2B Accessed December 3, 2020.
  • 26. Koehler AB, Snyder RD, Ord JK. Forecasting models and prediction intervals for the multiplicative Holt-Winters Method. Int J Forecast 2001;17:269-286. https://doi.org/10.1016/S0169-2070(01)00081-4
  • 27. Thrusfield M. Veterinary Eepidemiology. third ed. UK Oxford: Blackwell Science. 2005;15-93. Available at: https://dvmbooks.weebly.com/uploads/2/2/3/6/22365786/1._veterinary_epidemiology_thrush_filled.pdf Accessed April 4, 2020.
  • 28. Keeling MJ, Rohani P. Modeling infectious diseases in humans and animals. USA New Jersey: Princeton University Press, 2008;124-132. Available at: http://libgen.rs/book/index.php?md5=26031366BC66D83BB908A955A650E6DC Accessed Novamber 7, 2020.
  • 29. Grassly NC, Fraser C. Mathematical models of infectious disease transmission, Nat Rev Microbiol 2008;6:477-487. https://doi.org/10.1038/nrmicro1845
  • 30. Rezaei M, Nouri AA, Park GS, Kim DH. Application of geographic information system in monitoring and detecting the COVID-19 outbreak. Iran J Public Health 2020;49:114-116. https://doi.org/10.18502/ijph.v49iS1.3679
  • 31. Franch Pardo I, Napoletano BM, Rosete Verges F, Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ 2020;739:140033. https://doi.org/10.1016/j.scitotenv.2020.140033
  • 32. Saha A, Gupta K, Patil M, Urvashi. Monitoring and epidemiological trends of coronavirus disease (COVID-19) around the world. Matrix Sci Med 2020;4:121-126 Available at: https://www.matrixscimed.org/text.asp?2020/4/4/121/297630 Accessed October 12, 2020.
  • 33. Baker RE, Yang W, Vecchi GA, Metcalf CE, Grenfell BT. Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science 2020;369:315-319. https://doi.org/10.1126/science.abc2535
  • 34. Xun L, Qinyun L, Marynia K, et al. GeoDaCenter/covid: beta 2020 (Version beta). Zenodo. Avaible at: http://doi.org/10.5281/zenodo.4081869 Accessed September 7, 2020.
  • 35. Desjardins MR, Hohl A, Delmelle EM. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Appl Geogr 2020;118:102202. https://doi.org/10.1016/j.apgeog.2020.102202
  • 36. Tang Y, Wang S. Mathematic modeling of COVID-19 in the United States. Emerging Microbes Infec 2020;9:827-829. https://doi.org/10.1080/22221751.2020.1760146
  • 37. Lakhani A. Introducing the percent, number, availability, and capacity (PNAC) spatial approach to identify priority rural areas requiring targeted health support in light of COVID‐19: a commentary and application. J Rural Health 2020;37:149-152. https://doi.org/10.1111/jrh.12436
  • 38. Dagnino R, Weber EJ, Panitz LM. Monitoramento do Coronavirus (Covid-19) nos municípios do Rio Grande do Sul, Brasil. SocArXiv 2020:1-8. https://doi.org/10.31235/osf.io/3uqn5
  • 39. Rex FE, Borges CAS, Käfer PS. Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil. Ciência & Saúde Coletiva 2020;25:3377-3384.
  • 40. Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020. https://doi.org/10.1056/NEJMoa2002032
  • 41. Zhang X, Rao H, Wu Y, Huang Y, Dai H. Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China. BMC Infect Dis 2020;20:805. https://doi.org/10.1186/s12879-020-05537-y
  • 42. Kucharski AJ, Russell TW, Diamond C, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 2020;20:553-558. https://doi.org/10.1016/S1473-3099(20)30144-4
  • 43. Tang T, Huipeng L, Gifty M, et al. The changing patter of COVID-19 in China: a tempo-geographic analysis of the SARS-CoV-2 epidemic. Clin Infect Dis 2020;71:818-824. https://doi.org/10.1093/cid/ciaa423
  • 44. Roy S, Bhunia GS, Shit PK. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Model Earth Syst Environ 2020. https://doi.org/10.1007/s40808-020-00890-y
  • 45. Orea L, Álvarez IC. How effective has the Spanish lockdown been to battle COVID-19? A spatial analysis of the coronavirus propagation across provinces. Documento de Trabajo 2020;3:1-33.
  • 46. Rossman H, Keshet A, Shilo S, et al. A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys. Nat Med 2020;26:634-638. https://doi.org/10.1038/s41591-020-0857-9
  • 47. Giuliani D, Dickson MM, Espa G, Santi F. Modelling and predicting the spatio-temporal spread of Coronavirus Disease 2019 (COVID-19) in Italy. 2020. SSRN Electron J https://doi.org/10.2139/ssrn.3559569
  • 48. Karako K, Song P, Chen Y, Tang W. Analysis of COVID-19 infection spread in Japan based on stochastic transition model. Biosci Trends 2020;14:134-138. https://doi.org/10.5582/bst.2020.01482

SARS CoV-2 (COVID-19) in the framework of GIS-spatial epidemiology

Year 2021, Volume: 14 Issue: 4, 934 - 943, 01.10.2021

Ömer Bariş İnce Murat Şevik Ahmet Sait

https://doi.org/10.31362/patd.852259

Abstract

Project Number

-

References

  • 1. Siddell SG, Walker PJ, Lefkowitz EJ, et al. Additional changes to taxonomy ratified in a special vote by the international committee on taxonomy of viruses. Arch Virol 2019;164:943-946. https://doi.org/10.1007/s00705-018-04136-2
  • 2. Gorbalenya A, Baker S, Baric R, et al. The species severe acute respiratory syndrome related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536-544. https://doi.org/10.1038/s41564-020-0695-z
  • 3. Nieto Torres JL, DeDiego ML, Verdiá Báguena C, et al. Severe acute respiratory syndrome coronavirus envelope protein ion channel activity promotes virus fitness and pathogenesis. PLoS Pathogens 2014;10:e1004077. https://doi.org/10.1371/journal.ppat.1004077
  • 4. Harapan H, Itoh N, Yufika A, et al. Coronavirus disease 2019 (COVID-19): a literature review. J Infect Public Health 2020;13:667-673. https://doi.org/10.1016/j.jiph.2020.03.019
  • 5. Zhou P, Yang XL, Wang XG, et al. Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin. Nature 2020;579:270-273. https://doi.org/10.1101/2020.01.22.914952
  • 6. Yi Y, Lagniton PNP, Ye S, Li E, Xu RH. COVID-19: what has been learned and to be learned about the novel coronavirus disease. Int J Biol Sci 2020;16:1753-1766. https://doi.org/10.7150/ijbs.45134
  • 7. Luk HK, Li X, Fung J, Lau SKP, Woo PCY. Molecular epidemiology, evolution and phylogeny of SARS coronavirus. Infect Genet Evol 2019;71:21-30. https://doi.org/10.1016/j.meegid.2019.03.001
  • 8. Badawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERSCoV): a systematic review and meta-analysis. Int J Infect Dis 2016;49:129-133. https://doi.org/10.1016/j.ijid.2016.06.015
  • 9. De Wit E, van Doremalen N, Falzarano D, Munster VJ. SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol 2016;14:523-534. https://doi.org/10.1038/nrmicro.2016.81
  • 10. Tu YF, Chien CS, Yarmishyn AA, et al. A review of SARS-CoV-2 and the ongoing clinical trials. Int J Mol Sci 2020;21:2657. https://doi.org/10.3390/ijms21072657
  • 11. Ministry of Health 2019. Covid 19 Information page. Available at: https://covid19.saglik.gov.tr/. Accessed December 12, 2020.
  • 12. Kirby RS, Delmelle E, Eberth JM. Advances in spatial epidemiology and geographic information systems. Ann Epidemiol 2017;27:1-9. https://doi.org/10.1016/j.annepidem.2016.12.001
  • 13. Frerichs RR. History, maps and the internet: UCLA’s John Snow site. Soc Bulletin Available at: https://www.ph.ucla.edu/epi/snow/socbulletin34(2)3_7_2001.pdf. Accessed September 10, 2020.
  • 14. Carpenter T. The spatial epidemiologic (r)evolution: a look back in time and forward tothe future. Spat Spatiotemporal Epidemiol 2011;2:119-124. https://doi.org/10.1016/j.sste.2011.07.002
  • 15. Prates MO, Kulldorff M, Assunção RM. Relative risk estimates from spatial and space-time scan statistics: are they biased?. Stat Med 2014;33:2634-2644. https://doi.org/10.1002/sim.6143
  • 16. Bailey TC. Spatial statistical methods in health. Cad Saude Publica 2001;217:1083-1098. https://doi.org/10.1590/s0102-311x2001000500011
  • 17. Uluğtekin N, Doğru AÖ. Coğrafi Bilgi Sistemi ve Harita: Kartografya, Ege CBS Sempozyumu, 27-29 Nisan 2005, İzmir, Erişim adresi: https://web.itu.edu.tr/~dogruahm/Cografi%20Bilgi%20Sistemi%20Ve%20Harita_Kartografya.pdf Erişim tarihi 18 Aralık 2020.
  • 18. Karabulut E, Alpar R, Özayar E. Hastalıkların yere göre kümelenmesinde kullanılan yöntemler. İnö Üni Tıp Fak Der 2006;13:37-43.
  • 19. Tonini M, Tuia D, Ratle F. Detection of clusters using space–time scan statistics. Int J of Wildland Fire 2009;18:830-836. https://doi.org/10.1071/WF07167
  • 20. Quantum Geographic Information Systems™ (QGIS). QGIS home page. Avaible at: https://www.qgis.org/tr/site/. Accessed December 07, 2020.
  • 21. Anselin L, Nancy LG, Julia K. Rate transformations and smoothing-2006. Technical Report. Urbana, Spatial Analysis Laboratory, Department of Geography, University of Illinois Avaible at: https://pdfs.semanticscholar.org/88d8/b02de84f97f556cfe0ef5a91a7df229cf363.pdf. Accessed May 06, 2020.
  • 22. Gayır B, Arslan O. Orman yangınlarının CBS tabanlı konumsal istatistik analizi: 2011-2015 Yılları arasında Muğla orman bölge sınırları içerisinde çıkan yangınlar. Anadolu Orman Araşt Derg, 2018;4:44-60.
  • 23. Kulldorff M. SaTScan™ user guide for version 9.6- 2018. SatscanTM Home Page. Avaible at: https://www.satscan.org/cgi-bin/satscan/register.pl/ SaTScan_Users_Guide.pdf?todo=process_userguide_download. Accessed October 11, 2020.
  • 24. Çelik Ş. Zaman serileri analizi ve trafik kazası verilerine uygulanması. Yayınlanmamış Doktora Tezi Ankara Üniversitesi Fen Bilimleri Enstitüsü, Zootekni Anabilim Dalı Ankara, 2013.
  • 25. Kirkup L, Data analysis with excel: an introduction for physical scientists. UK Cambridge: Cambridge University Press. 2002;6-35. Available at: http://libgen.rs/book/index.php?md5=DE89297B02744DBBF8EC0E3DB2345F2B Accessed December 3, 2020.
  • 26. Koehler AB, Snyder RD, Ord JK. Forecasting models and prediction intervals for the multiplicative Holt-Winters Method. Int J Forecast 2001;17:269-286. https://doi.org/10.1016/S0169-2070(01)00081-4
  • 27. Thrusfield M. Veterinary Eepidemiology. third ed. UK Oxford: Blackwell Science. 2005;15-93. Available at: https://dvmbooks.weebly.com/uploads/2/2/3/6/22365786/1._veterinary_epidemiology_thrush_filled.pdf Accessed April 4, 2020.
  • 28. Keeling MJ, Rohani P. Modeling infectious diseases in humans and animals. USA New Jersey: Princeton University Press, 2008;124-132. Available at: http://libgen.rs/book/index.php?md5=26031366BC66D83BB908A955A650E6DC Accessed Novamber 7, 2020.
  • 29. Grassly NC, Fraser C. Mathematical models of infectious disease transmission, Nat Rev Microbiol 2008;6:477-487. https://doi.org/10.1038/nrmicro1845
  • 30. Rezaei M, Nouri AA, Park GS, Kim DH. Application of geographic information system in monitoring and detecting the COVID-19 outbreak. Iran J Public Health 2020;49:114-116. https://doi.org/10.18502/ijph.v49iS1.3679
  • 31. Franch Pardo I, Napoletano BM, Rosete Verges F, Billa L. Spatial analysis and GIS in the study of COVID-19. A review. Sci Total Environ 2020;739:140033. https://doi.org/10.1016/j.scitotenv.2020.140033
  • 32. Saha A, Gupta K, Patil M, Urvashi. Monitoring and epidemiological trends of coronavirus disease (COVID-19) around the world. Matrix Sci Med 2020;4:121-126 Available at: https://www.matrixscimed.org/text.asp?2020/4/4/121/297630 Accessed October 12, 2020.
  • 33. Baker RE, Yang W, Vecchi GA, Metcalf CE, Grenfell BT. Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science 2020;369:315-319. https://doi.org/10.1126/science.abc2535
  • 34. Xun L, Qinyun L, Marynia K, et al. GeoDaCenter/covid: beta 2020 (Version beta). Zenodo. Avaible at: http://doi.org/10.5281/zenodo.4081869 Accessed September 7, 2020.
  • 35. Desjardins MR, Hohl A, Delmelle EM. Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: detecting and evaluating emerging clusters. Appl Geogr 2020;118:102202. https://doi.org/10.1016/j.apgeog.2020.102202
  • 36. Tang Y, Wang S. Mathematic modeling of COVID-19 in the United States. Emerging Microbes Infec 2020;9:827-829. https://doi.org/10.1080/22221751.2020.1760146
  • 37. Lakhani A. Introducing the percent, number, availability, and capacity (PNAC) spatial approach to identify priority rural areas requiring targeted health support in light of COVID‐19: a commentary and application. J Rural Health 2020;37:149-152. https://doi.org/10.1111/jrh.12436
  • 38. Dagnino R, Weber EJ, Panitz LM. Monitoramento do Coronavirus (Covid-19) nos municípios do Rio Grande do Sul, Brasil. SocArXiv 2020:1-8. https://doi.org/10.31235/osf.io/3uqn5
  • 39. Rex FE, Borges CAS, Käfer PS. Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil. Ciência & Saúde Coletiva 2020;25:3377-3384.
  • 40. Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020. https://doi.org/10.1056/NEJMoa2002032
  • 41. Zhang X, Rao H, Wu Y, Huang Y, Dai H. Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China. BMC Infect Dis 2020;20:805. https://doi.org/10.1186/s12879-020-05537-y
  • 42. Kucharski AJ, Russell TW, Diamond C, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 2020;20:553-558. https://doi.org/10.1016/S1473-3099(20)30144-4
  • 43. Tang T, Huipeng L, Gifty M, et al. The changing patter of COVID-19 in China: a tempo-geographic analysis of the SARS-CoV-2 epidemic. Clin Infect Dis 2020;71:818-824. https://doi.org/10.1093/cid/ciaa423
  • 44. Roy S, Bhunia GS, Shit PK. Spatial prediction of COVID-19 epidemic using ARIMA techniques in India. Model Earth Syst Environ 2020. https://doi.org/10.1007/s40808-020-00890-y
  • 45. Orea L, Álvarez IC. How effective has the Spanish lockdown been to battle COVID-19? A spatial analysis of the coronavirus propagation across provinces. Documento de Trabajo 2020;3:1-33.
  • 46. Rossman H, Keshet A, Shilo S, et al. A framework for identifying regional outbreak and spread of COVID-19 from one-minute population-wide surveys. Nat Med 2020;26:634-638. https://doi.org/10.1038/s41591-020-0857-9
  • 47. Giuliani D, Dickson MM, Espa G, Santi F. Modelling and predicting the spatio-temporal spread of Coronavirus Disease 2019 (COVID-19) in Italy. 2020. SSRN Electron J https://doi.org/10.2139/ssrn.3559569
  • 48. Karako K, Song P, Chen Y, Tang W. Analysis of COVID-19 infection spread in Japan based on stochastic transition model. Biosci Trends 2020;14:134-138. https://doi.org/10.5582/bst.2020.01482
There are 48 citations in total.

Details

Primary Language Turkish
Subjects Medical Microbiology
Journal Section Collection
Authors

Ömer Bariş İnce 0000-0001-8302-9607

Murat Şevik 0000-0002-9604-3341

Ahmet Sait 0000-0001-7658-8793

Project Number -
Publication Date October 1, 2021
Submission Date January 2, 2021
Acceptance Date March 3, 2021
Published in Issue Year 2021 Volume: 14 Issue: 4

Cite

APA İnce, Ö. B., Şevik, M., & Sait, A. (2021). Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19). Pamukkale Medical Journal, 14(4), 934-943. https://doi.org/10.31362/patd.852259
AMA İnce ÖB, Şevik M, Sait A. Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19). Pam Med J. October 2021;14(4):934-943. doi:10.31362/patd.852259
Chicago İnce, Ömer Bariş, Murat Şevik, and Ahmet Sait. “Coğrafi Bilgi Sisitemleri-mekânsal Epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19)”. Pamukkale Medical Journal 14, no. 4 (October 2021): 934-43. https://doi.org/10.31362/patd.852259.
EndNote İnce ÖB, Şevik M, Sait A (October 1, 2021) Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19). Pamukkale Medical Journal 14 4 934–943.
IEEE Ö. B. İnce, M. Şevik, and A. Sait, “Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19)”, Pam Med J, vol. 14, no. 4, pp. 934–943, 2021, doi: 10.31362/patd.852259.
ISNAD İnce, Ömer Bariş et al. “Coğrafi Bilgi Sisitemleri-mekânsal Epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19)”. Pamukkale Medical Journal 14/4 (October 2021), 934-943. https://doi.org/10.31362/patd.852259.
JAMA İnce ÖB, Şevik M, Sait A. Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19). Pam Med J. 2021;14:934–943.
MLA İnce, Ömer Bariş et al. “Coğrafi Bilgi Sisitemleri-mekânsal Epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19)”. Pamukkale Medical Journal, vol. 14, no. 4, 2021, pp. 934-43, doi:10.31362/patd.852259.
Vancouver İnce ÖB, Şevik M, Sait A. Coğrafi Bilgi Sisitemleri-mekânsal epidemiyoloji çerçevesinde SARS CoV-2 (COVID-19). Pam Med J. 2021;14(4):934-43.

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