Spatio-temporal analysis of the COVID-19 pandemic in Türkiye: results of the controlled normalization

World Health Organization. (2020). Coronavirus disease 2019 (COVID-19) situation Report-55. March 15. https://www.who.int/docs/default-source/coronaviruse/situationreports/20200315-sitrep-55-covid-19.pdf?sfvrsn=33daa5cb_8

Hui, D. S., Azhar, E. I., Madani, T. A., Ntoumi, F., Kock, R., Dar, O., Ippolito, G., Mchugh, T. D., Memish, Z. A., Drosten, C., Zumla, A., & Petersen, E. (2020). The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health—The latest 2019 novel coronavirus outbreak in Wuhan, China. International Journal of Infectious Diseases, 91, 264–266. https://doi.org/10.1016/j.ijid.2020.01.009

Zawbaa, H. M., El-Gendy, A., Saeed, H., Osama, H., Ali, A. M. A., Gomaa, D., Abdelrahman, M., Harb, H. S., Madney, Y. M., & Abdelrahim, M. E. A. (2021). A study of the possible factors affecting COVID-19 spread, severity and mortality and the effect of social distancing on these factors: Machine learning forecasting model. International Journal of Clinical Practice, 75, e14116. https://doi.org/10.1111/ijcp.14116

Committee for the Coordination of Statistical Activities. (2020). How COVID-19 is changing the world: A statistical perspective Volume II. https://unstats.un.org/unsd/ccsa/documents/covid19-report-ccsa_vol2.pdf

Christidis, P., & Christodoulou, A. (2020). The predictive capacity of air travel patterns during the global spread of the COVID-19 pandemic: Risk, uncertainty and randomness. International Journal of Environmental Research and Public Health, 17, 3356. https://doi.org/10.3390/ijerph17103356

Ma, Q., Gao, J., Zhang, W., Wang, L., Li, M., Shi, J., Zhai, Y., Sun, D., Wang, L., Chen, B., Jiang, S., & Zhao, J. (2021). Spatio-temporal distribution characteristics of COVID-19 in China: A city-level modeling study. BMC Infectious Diseases, 21, 816. https://doi.org/10.1186/s12879-021-06515-8

Ning, J., Chu, Y., Liu, X., Zhang, D., Zhang, J., Li, W., & Zhang, H. (2021). Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: A case study of the mainland China. Environmental Science and Pollution Research, 28, 48298–48311. https://doi.org/10.1007/s11356-021-14092-1

Bhunia, G. S., Roy, S., & Shit, P. K. (2021). Spatio-temporal analysis of COVID-19 in India—A geostatistical approach. Spatial Information Research, 29, 661–672. https://doi.org/10.1007/s41324-020-00376-0

Pavan Kumar, S. T., Lahiri, B., & Alvarado, R. (2021). Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions. Spatial Statistics. https://doi.org/10.1016/j.spasta.2021.100538

Maiti, A., Zhang, Q., Sannigrahi, S., Pramanik, S., Chakraborti, S., Cerda, A., & Pilla, F. (2021). Exploring spatio-temporal effects of the driving factors on COVID-19 incidences in the contiguous United States. Sustainable Cities and Society, 68, 102784. https://doi.org/10.1016/j.scs.2021.102784

Wang, Y., Liu, Y., Struthers, J., & Lian, M. (2021). Spatio-temporal characteristics of the COVID-19 epidemic in the United States. Clinical Infectious Diseases, 72, 643–651. https://doi.org/10.1093/cid/ciaa934

Martines, M. R., Ferreira, R. V., Toppa, R. H., Assunção, L. M., Desjardins, M. R., & Delmelle, E. M. (2021). Detecting space–time clusters of COVID-19 in Brazil: Mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities. Journal of Geographical Systems, 23, 7–36. https://doi.org/10.1007/s10109-020-00344-0

Ghosh, P., & Cartone, A. (2020). A spatio-temporal analysis of COVID-19 outbreak in Italy. Regional Science Policy and Practice, 12, 1047–1062. https://doi.org/10.1111/rsp3.12376

Sartorius, B., Lawson, A. B., & Pullan, R. L. (2021). Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England. Scientific Reports, 11, 5378. https://doi.org/10.1038/s41598-021-83780-2

Kim, S., & Castro, M. C. (2020). Spatio-temporal pattern of COVID-19 and government response in South Korea (as of May 31, 2020). International Journal of Infectious Diseaes, 98, 328–333. https://doi.org/10.1016/j.ijid.2020.07.004

Kim, S., Kim, M., Lee, S., & Lee, Y. J. (2021). Discovering spatio-temporal patterns of COVID-19 pandemic in South Korea. Scientific Reports, 11, 24470. https://doi.org/10.1038/s41598-021-03487-2

Xu, F., & Beard, K. (2021). A comparison of prospective space-time scan statistics and spatio-temporal event sequence based clustering for COVID-19 surveillance. PLoS ONE, 16, e0252990. https://doi.org/10.1371/journal.pone.0252990

Hohl, A., Delmelle, E. M., Desjardins, M. R., & Lan, Y. (2020). Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States. Spatail and Spatio-Temporal Epidemiology, 34, 100354. https://doi.org/10.1016/j.sste.2020.100354

Study of Scientific Advisory Board. (2020). Guidance to COVID-19 outbreak management and working. The Republic of Türkiye Ministry of Health. https://hsgm.saglik.gov.tr/depo/covid19/Ingilizce/Salgin_Yonetimi_ve_Calisma_Rehberi/COVID19-SALGIN-YONETIMI-VE-CALISMA-REHBERI-ENG.pdf. Retrieved: 31 January 2021.

İçöz, C. (2021). Türkiye’deki İl Bazında Gerçekleşen Covid Vakaları için Bir Raporlama ve Karşılaştırma Uygulaması: R Shiny Örneği. Veri Bilimi, 4, 9–18.

Wang, Q., Dong, W., Yang, K., Ren, Z., Huang, D., Zhang, P., & Wang, J. (2021). Temporal and spatial analysis of COVID-19 transmission in China and its influencing factors. International Journal of Infectious Diseases, 105, 675–685.

Aral, N., & Bakir, H. (2022). Spatio-temporal analysis of Covid-19 in Türkiye. Sustainable Cities and Society, 76, 103421. https://doi.org/10.1016/j.scs.2021.103421

Kantar, Y. M., Yildirim, V., & Yeni̇lmez, İ. (2019). Spatıal dıstrıbutıon of poverty threshold ın Türkiye. Nicel Bilim Dergisi, 1, 52–61.

Scrucca, L. (2005). Clustering multivariate spatial data based on local measures of spatial autocorrelation. Università di Perugia, Dipartimento Economia.

Aktaş, S. G., Kumtepe, E. G., Mert, K. Y., Ulukan, İC., Aydın, S., Aksoy, T., & Er, F. (2019). Improving gender equality in higher education in Türkiye. Applied Spatial Analysis and Policy. https://doi.org/10.1007/s12061-017-9235-5

Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27, 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x

Cho, G. (1983). Spatial processes: Models and applications by A.D. Cliff and J.K. Ord. 16 by 24 em, 266 pages, maps, diags., index and bibliography. london: Pion Limited, 1981. (ISBN 08-85086-081-4). Cartography, 13, 59–60. https://doi.org/10.1080/00690805.1983.10438243

Boots, B. (2002). Local measures of spatial association. Écoscience, 9, 168–176. https://doi.org/10.1080/11956860.2002.11682703

Yildirim, V., & Mert Kantar, Y. (2020). Robust estimation approach for spatial error model. Journal of Statistical Computation and Simulation, 90, 1618–1638. https://doi.org/10.1080/00949655.2020.1740223

Yidirim, V. (2018). Spatial econometric models: Robust estımation for spatial error model. Doctoral dissertation thesis, Anadolu University, Eskisehir, Türkiye

Kulldorff, M., & Nagarwalla, N. (1995). Spatial disease clusters: Detection and inference. Statistics in Medicine, 14, 799–810. https://doi.org/10.1002/sim.4780140809

Kulldorff, M., Heffernan, R., Hartman, J., Assunção, R., & Mostashari, F. (2005). A space-time permutation scan statistic for disease outbreak detection. PLOS Medicine, 2, e59. https://doi.org/10.1371/journal.pmed.0020059

Kulldorff, M. (2001). Prospective time periodic geographical disease surveillance using a scan statistic. Journal of the Royal Statistical Society Series A (Statistics in Society), 164, 61–72. https://doi.org/10.1111/1467-985X.00186

Kulldorff, M. (2018). SatscanTM user guide for version 9.6.

Kulldorff, M., Athas, W. F., Feurer, E. J., Miller, B. A., & Key, C. R. (1998). Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health, 88, 1377–1380. https://doi.org/10.2105/AJPH.88.9.1377

Kulldorff, M. (1997). A spatial scan statistic. Communication in Statistics - Theory Methods, 26, 1481–1496. https://doi.org/10.1080/03610929708831995

Tennekes, M. (2018). tmap: Thematic maps in R. Journal of Statistical Software, 84, 1–39. https://doi.org/10.18637/jss.v084.i06

Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa : An introduction to spatial data analysis. Geographical Analysis, 38, 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x

QGIS.org. (2022). QGIS Geographic Information System. QGIS Association.

Leal Filho, W., Brandli, L. L., Lange Salvia, A., Rayman-Bacchus, L., & Platje, J. (2020). COVID-19 and the UN sustainable development goals: Threat to solidarity or an opportunity? Sustainability, 12(13), 5343. https://doi.org/10.3390/su12135343