The Temporal and Spatial Analysis of Corona Pandemic in Jordan using the Geographic Information System: An Applied Geographical Study
Ayed Taran(1*), AbedAlhameed Alfanatseh(2), Shatha Rawashdeh(3), Faisal Almayouf(4)
(1) Al al-Bayt University, Department of Applied Geography, Jordan, Almafraq
(2) Al-hussein Bin Talal University, Department of Geography, Jordan, Ma'an
(3) Al-hussein Bin Talal University, Department of Geography, Jordan, Ma'an
(4) Al al-Bayt University, Department of Applied Geography, Jordan, Almafraq
(*) Corresponding Author
Abstract
The coronavirus disease which results from severe acute respiratory syndrome (SARS-COV-2), is considered a global challenge affecting millions of people and leading to a global increase in mortality, including in Jordan. Therefore, this study aims to analyze the temporal and spatial patterns of the prevalence and outbreak of coronavirus in Jordan during six periods, from 1, October 2020 until 31, March 2021 by applying geographical information systems. The Moran coefficient was applied in addition to the G* test and location quotient (LQ). The results showed the overall pattern for the distribution of cases affected by the virus was random since most governorates' experience increased the focus and prevalence of the pandemic. Furthermore, four hot spots were revealed, namely Amman, Irbid, Zarqa, and Balqa'. This study introduced new insights into the statistical analysis of the distribution and prevalence of coronavirus in Jordan using geographical information systems. This will help planners and decision-makers to predict the dynamics of the temporal and spatial transfer of the virus in the future. It will also explain the current situation to set the appropriate policies or measures to face the pandemic, as well as reduce its prevalence. Therefore, monitoring, evaluating, and planning the usage of geospatial analysis are essential for controlling the spread of COVID-19 in the country.
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Ahmadi, M., Sharifi, A., Dorosti, S., Jafarzadeh, S., andGhanbari, N. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Science of the total environment. 729: 138705.https://doi.org/10.1016/j.scitotenv. 2020.138705.
Al-ahmadi, A. (2021). The impact of the Corona pandemic on the temporal and spatial characteristics of traffic accidents in the Kingdom of Saudi Arabia (2019-2020) (The city of Medina as a model). The Arab Journal of Geographical Studies, 4(8), 1-36.
Alves, J., Abade, A., Peres, W., Borges, J., Santos, S. andScholze, A. (2021). Impact of COVID-19 on the indigenous population of Brazil: A geo-epidemiological study. Epidemiology & Infection new, 149(e185), 1-11, https://doi.org/10.1017/S095026 8821001849.
Chen, Z.L., Zhang, Q., Lu, Y., Guo, Z.M., Zhang, X., Zhang, W.J. Guo, C., Liao, C.H., Li, Q.L., Han, X.H., andLu, J.H. (2020). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China. Chinese Medical Journal. 133(9), 1044-1050. https://doi.org/10.1097/ CM9.0000000000000782.
Comber, A.J., Brunsdon, C.H., andRadburn, R. (2011). A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions. International Journal of Health Geographics, 10(44), 1-11, doi:10.1186/1476-072X-10-44.
Department of Statistics, (2021). Published data, estimated population numbers for the Kingdom by governorate, community, gender and families for the year 2020.
Dickson, M.M., Espa, G., Giuliani, D., Santi, F., andSavadori, L. (2020). Assessing the effect of containment measures on the spatiotemporal dynamic of COVID-19 in Italy. Nonlinear Dynamics 101, 1833–1864. https://doi.org/10.1007/s11071-020-05853-7.
Elzeeny, S. (2020). Temporal and Spatial Analysis of the sequence of coronavirus infection cases in the world: A study in medical geography, The Egyptian journal of Environmental change, 12(4), 31-44. 10.21608/EGJEC.2020.120041.
Garchitorena, A., Sokolow, S.H., Roche, B., Ngonghala, C.N., Jocque, M., Lund, A., Barry, M., Mordecai, E.A., Daily, G.C., Jones, J.H., Andrews, J.R., Bendavid, E., Luby, S.P., LaBeaud, A.D., Seetah, K., Guégan, J.F., Bonds, M.H., andDe Leo, G.A. (2017). Disease ecology, health and the environment: a framework to account for ecological and socio-economic drivers in the control of neglected tropical diseases. Philos Trans R SocLondSer B BiolSci, 372(1722). https://doi.org/10.1098/rstb.2016.0128.
Getis, A., andOrd, J.K. (1992). The Analysis of Spatial Association by Use of Distance Statistics." Geographical Analysis, 24(3), 189-206. http://dx.doi.org/10.1111/j.1538-4632.1992.tb00261.x.
Ord, J.K.andGetis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 27(4), 286-306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
Ghosh, P., andCartone, A. (2020). A Spatio-temporal analysis of COVID-19 outbreak in Italy. Regional Science Policy & Practice, 12(6), 1047-1062. doi.org/10.1111/rsp3. 12376.
Kandwal, R., Garg, P., andGarg, R.D. (2009). Health GIS and HIV/AIDS studies: Perapective and retrospective. Journal of biomedical information. 42(4), 748-755. https://doi.org/10.1016/j.jbi.2009.04.008.
Kelvin, D. J., andRubino, S. (2020). Fear of the novel coronavirus. The Journal of Infection in Developing Countries (JIDC). 14(1), 1-2. doi: 10.3855/jidc.12496.
Liu, Z., Bing, X., andZhi, X.Z. (2020). The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Chinese Center for Disease Control and Prevention, 41(2), 145–151. https://doi.org/10.3760/cma.j.issn.0254-6450.2020.02.003.
Lyseen, A.K., Nohr, C., Sorensen, E.M., Gudes, O., Geraghty, E.M., Shaw, N.T., andBivona-Tellez, C. (2014). A review and framework for categorizing current research and development in health related Geographical Information Systems (GIS) studies. Yearb Med Inform, 23(1), 110–124.https://doi.org/10.15265/IY-2014-0008.
Ma, Y., Zhao, Y., Liu, J., He, X., Wang, B., Fu, S., Yan, J., Niu, J., Zhou, J., andLuo, B. (2020). Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Science of the Total Environment, 724:138226.https://doi.org/10.1016/j.scitotenv.2020.138226.
Mackey, T.K., Liang, B.A., Cuomo, R., Hafen, R., Brouwer, K.C., andLee, D.E. (2014). Emerging and reemerging neglected tropical diseases: a review of key characteristics, risk factors, and the policy and innovation environment. Clinical Microbiology Rev, 27(4), 949–979. https://doi.org/10.1128/CMR.00045 -14.
Mollalo, A., Vahedi, B., andRivera, K.M. (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Sci Total Environ, 728:138884, 1-8. DOI: 10.1016/j.scitotenv.2020.138884.
Murugesan, B., Karuppannan, S., Mengistie, A.T., Ranganathan, M., andGopalakrishnan, G. (2020). Distribution and trend analysis of COVID-19 in India: geospatial approach. Journal of Geographical Studies, 4(1-2), 1–9.https://doi.org/10.21523/gcj5.20040101.
Ramírez-Aldana R., Gomez-Verjan, J.C., andBello-Chavolla, O.Y. (2020). Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level. PLoSNegl Trop Dis, 14(11): e0008875. https://doi.org/10.1371/journal.pntd.0008875.
Sanson, R.L., Liberona, H., andMorris, R.S. (1991). The use of a geographical information system in the management of a foot-andmouth disease epidemic. Preventive Veterinary Medicine, 11, 309-313. doi:10.1016/S0167-5877(05)80017-0.
Sithiprasasna, R., Patpoparn, S., Attatippaholkun, W., Suvannadabba, S., andSrisuphanunt, M. (2004). The geographic information system as an epidemiological tool in the surveillance of dengue virus-infected Aedes mosquitos. Southeast Asian J Tropical Med Public Health, 35(4), 918–926.
Wagner, D.M., Klunk, J., Harbeck, M., Devault, A., Waglechner, N., Sahl, J.W., Enk, J., Birdsell, D.N., Kuch, M., Lumibao, C., Poinar, D., Pearson, T., Fourment, M., Golding, B., Riehm, J.M., Earn, D.J.D., DeWitte, S., Rouillard, J.M., Grupe, G., Wiechmann, I., Bliska, J.B., Keim, P.S., Scholz, H.C., Holmes, E.C., andPoinar, H. (2014). Yersinia pestis and the plague of Justinian 541-543 AD: a genomic analysis. The Lancet Infect Dis, 14(4), 319–326. https://doi.org/10.1016/S1473-3099(13)70323-2.
World Health Organization (WHO). (2021). Coronavirus disease (COVID-19) situation reports. Accessed 31 March 2021. https://www.who.int/ar/emergencies/diseases/ novel-coronavirus-2019.
Xu, N., Cheng, Y., andXu, X. (2018). Using Location Quotients to Determine Public–Natural Space Spatial Patterns: A Zurich Model. Sustainability, 10(10), 3462. https://doi.org/10.3390/su10103462.
Yahya, M., Safian, E., andBurhan, B. (2020). The Trend Distribution and Temporal Pattern Analysis of COVID-19 Pandemic using GIS framework in Malaysia, AIJR Preprints, version 1, 1-14, https://preprints.aijr.org/index.php/ap/preprint/view/174.
Ye., L., andHu, L. (2020). Spatiotemporal distribution and trend of COVID-19 in the Yangtze River Delta region of the People’s Republic of China. Geospatial Health, 15(1), 25-32. doi:10.4081/gh.2020.889.
DOI: https://doi.org/10.22146/ijg.73663
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