https://doi.org/10.4081/gh.2023.1183
Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand
Submitted: 8 January 2023
Accepted: 26 February 2023
Published: 25 May 2023
Accepted: 26 February 2023
Abstract Views: 703
PDF: 344
Supplementary Materials: 50
HTML: 3
Supplementary Materials: 50
HTML: 3
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
A study of 2,569,617 Thailand citizens diagnosed with COVID-19 from January 2020 to March 2022 was conducted with the aim of identifying the spatial distribution pattern of incidence rate of COVID-19 during its five main waves in all 77 provinces of the country. Wave 4 had the highest incidence rate (9,007 cases per 100,000) followed by the Wave 5, with 8,460 cases per 100,000. We also determined the spatial autocorrelation between a set of five demographic and health care factors and the spread of the infection within the provinces using Local Indicators of Spatial Association (LISA) and univariate and bivariate analysis with Moran’s I. The spatial autocorrelation between the variables examined and the incidence rates was particularly strong during the waves 3-5. All findings confirmed the existence of spatial autocorrelation and heterogenicity of COVID-19 with the distribution of cases with respect to one or several of the five factors examined. The study identified significant spatial autocorrelation with regard to the COVID-19 incidence rate with these variables in all five waves. Depending on which province that was investigated, strong spatial autocorrelation of the High-High pattern was observed in 3 to 9 clusters and of the Low-Low pattern in 4 to 17 clusters, whereas negative spatial autocorrelation was observed in 1 to 9 clusters of the High-Low pattern and in 1 to 6 clusters of Low-High pattern. These spatial data should support stakeholders and policymakers in their efforts to prevent, control, monitor and evaluate the multidimensional determinants of the COVID-19 pandemic.
Alcântara E, Mantovani J, Rotta L, Park E, Rodrigues T, Carvalho FC, ad Filho CRS. 2020. Investigating spatiotemporal patterns of the COVID-19 in São Paulo State, Brazil. Geospat Health 15:201–9.
DOI: https://doi.org/10.4081/gh.2020.925
Al-Kindi, Khalifa M, Alkharusi A, Alshukaili D, Al Nasiri N, Al-Awadhi T, Charabi Y, El Kenawy AM. 2020. Spatiotemporal assessment of COVID-19 spread over Oman using GIS techniques. Earth Systems and Environment 4:797–811.
DOI: https://doi.org/10.1007/s41748-020-00194-2
Anselin L. 2020. Local Spatial Autocorrelation (1). 2020. https://geodacenter.github.io/workbook/6a_local_auto/lab6a.html.
Anselin L, Bao S. 1997. Exploratory spatial data analysis linking SpaceStat and ArcView. pp. 35–59.
DOI: https://doi.org/10.1007/978-3-662-03499-6_3
Anselin L, Syabri I, Kho Y. 2006. GeoDa: An introduction to spatial data analysis. Geographical Analysis 38:5–22.
DOI: https://doi.org/10.1111/j.0016-7363.2005.00671.x
Bag R, Ghosh M, Biswas B, Chatterjee M. 2020. Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India’s response in managing the pandemic. Regional Sci Policy Pract 12:1063–03.
DOI: https://doi.org/10.1111/rsp3.12359
Cliff AD, Ord JK. 1981. Spatial Processes: Models and Applications. Pion Limited. Vol. 13. London: Taylor & Francis Group 1981.
Coşkun H, Yıldırım N, Gündüz S. 2021. The spread of COVID-19 virus through population density and wind in Turkey cities. Science of the Total Environment 751.
DOI: https://doi.org/10.1016/j.scitotenv.2020.141663
Deeb OE. 2021. Spatial autocorrelation and the dynamics of the mean center of COVID-19 infections in Lebanon. Front Appl Math Stat 6:1–10.
DOI: https://doi.org/10.3389/fams.2020.620064
Dutta I, Tirthankar B, Arijit D, 2021. Spatial analysis of COVID-19 incidence and its determinants using spatial modeling: a study on India. Environmental Challenges 4:100096.
DOI: https://doi.org/10.1016/j.envc.2021.100096
Ehlert A. 2021. The socio-economic determinants of COVID-19: A spatial analysis of German county level data. Socio-Econ Plan Sci 78:101083.
DOI: https://doi.org/10.1016/j.seps.2021.101083
Kunno J, Busaba S, Chavanant S, Budsaba W, Sathit K, Chuthamat K, 2021. Comparison of different waves during the COVID-19 pandemic: retrospective descriptive study in Thailand. Adv Prev Medi 2021:1–8.
DOI: https://doi.org/10.1155/2021/5807056
Kwok CYT, Wong MS, Chan KL, Kwan MP, Nichol JE, Liu CH, Wong JYH, Wai AKC, Chan LWC, Xu Y, Li H, Huang J, Kan Z, 2021. Spatial analysis of the impact of urban geometry and socio-demographic characteristics on COVID-19, a study in Hong Kong. Sci Total Environ. 764:144455.
DOI: https://doi.org/10.1016/j.scitotenv.2020.144455
Liu Q, Sha D, Liu W, Houser P, Zhang L, Hou R, Lan H, Flynn C, Lu M, Hu T, Yang C. 2020. Spatiotemporal patterns of COVID-19 impact on human activities and environment in mainland China using nighttime light and air quality data. Remote Sensing 12:1–14.
DOI: https://doi.org/10.3390/rs12101576
Liu W, Wang D, Hua S, Xie C, Wang B, Qiu W, Xu T, Ye Z, Yu L, Yang M, Xiao Y, Feng X, Shi T, Li M, Chen W. 2021. Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China. Sci Rep 11:13648.
DOI: https://doi.org/10.1038/s41598-021-93020-2
Lovett DA, Poots AJ, Clements JT, Green SA, Samarasundera E, Bell D. 2014. Using geographical information systems and cartograms as a health service quality improvement tool. Spat Spatiotemporal Epidemiol 10: 67–74.
DOI: https://doi.org/10.1016/j.sste.2014.05.004
Mansour S, Kindi AA, Al-Said A, Al-Said A, Atkinson P. 2021. Sociodemographic determinants of COVID-19 incidence rates in Oman: geospatial modelling using multiscale geographically weighted regression (MGWR). Sust Cities Soc 65:102627.
DOI: https://doi.org/10.1016/j.scs.2020.102627
Maroko AR, Nash D, Pavilonis BT. 2020. COVID-19 and inequity: a comparative spatial analysis of New York City and Chicago hot spots. J Urban Health 97:461–70.
DOI: https://doi.org/10.1007/s11524-020-00468-0
National-Statistical-Office. 2020. Key Statistical Data. 2020. Available from: http://statbbi.nso.go.th/staticreport/page/sector/en/01.aspx.
Pedorsa NL, de Albuquerque NLS. 2020. Spatial analysis of COVID-19 cases and intensive care beds in the state of Ceará, Brazil. Ciência & Saúde Coletiva 25:2461–68.
DOI: https://doi.org/10.1590/1413-81232020256.1.10952020
Ramírez-Aldana R, Gomez-Verjan JC, Bello-Chavolla OY. 2020. Spatial analysis of COVID-19 spread in Iran: insights into geographical and structural transmission determinants at a province level. PLoS Negl Trop Dis 14:e0008875.
DOI: https://doi.org/10.1371/journal.pntd.0008875
Ribeiro HV, Sunahara AS, Sutton J, Perc M, Hanley QS, 2020. City size and the spreading of COVID-19 in Brazil. PLoS ONE 15:e0239699.
DOI: https://doi.org/10.1371/journal.pone.0239699
Sangkasem K, Puttanapong N. 2020. Analysis of spatial inequality using DMSP-OLS nighttime-light satellite imageries: a case study of Thailand. Regional Science Policy & Practice. John Wiley & Sons, Ltd. https://doi.org/10.1111/RSP3.12386.
DOI: https://doi.org/10.1111/rsp3.12386
Sarkar SK, Ekram KMM, Das PC. 2021. Spatial modeling of COVID-19 transmission in Bangladesh. Spatial Inf Res 29:715–26.
DOI: https://doi.org/10.1007/s41324-021-00387-5
Sornlorm K, Roshan KM, Withaya P, Krissana A. 2022. Spatial association of land-use areas and disease occurred by pesticide poisoning in Thailand. F1000 Research 11:1386.
DOI: https://doi.org/10.12688/f1000research.126554.1
Steiniger S, Hunter AJS. 2013. The 2012 free and open source GIS software map - a guide to facilitate research, development, and adoption. Computers Environ Urban Systems 39:136–50.
DOI: https://doi.org/10.1016/j.compenvurbsys.2012.10.003
Su D, Chen Y, He K, Zhang T, Tan M, Zhang Y, Zhang X. 2020. Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide. MedRxiv, 1–35.
DOI: https://doi.org/10.1101/2020.04.23.20077545
Sun F, Matthews SA, Yang TC, Hu MH. 2020. A spatial analysis of the COVID-19 period prevalence in U.S. Counties through June 28, 2020: Where Geography Matters? Ann Epidemiol 52:54-59.e1.
DOI: https://doi.org/10.1016/j.annepidem.2020.07.014
Wetchayont P. 2021. Investigation on the impacts of COVID-19 lockdown and influencing factors on air quality in greater Bangkok, Thailand. Adv Meteorol 2021:6697707.
DOI: https://doi.org/10.1155/2021/6697707
Wetchayont P, Waiyasusri K. 2021. Using Moran’s I for detection and monitoring of the Covid-19 spreading stage in Thailand during the third wave of the pandemic. Geogr Environ Sustain 14:155–67.
DOI: https://doi.org/10.24057/2071-9388-2021-090
WHO. 2020. Naming the Coronavirus Disease (COVID-19) and the Virus That Causes It. 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.
WHO. 2022. WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data. 2022. https://covid19.who.int/.
WHO-Thailand. 2022a. COVID-19 Situation Update No.227, Thailand (16 March 2022).
WHO-Thailand. 2022b. COVID-19 Situation Update No.235, Thailand (11 May 2022).
Wu X, Nethery RC, Sabath BM, Braun D, Dominici F. 2020. Exposure to air pollution and COVID-19 mortality in the United States: a nationwide cross-sectional study. MedRxiv 7:2020.04.05.20054502.
DOI: https://doi.org/10.1289/isee.2020.virtual.O-OS-638
You H, Wu X, Guo X. 2020. Distribution of Covid-19 morbidity rate in association with social and economic factors in Wuhan, China: implications for urban development. Int J Environ Res Public Health 17:3417.
DOI: https://doi.org/10.3390/ijerph17103417
Zhang H, Suepa T, Hong L, Nayelin P, Mot L, Chakpor A. 2021. Geospatial analysis of Covid-19 to respond to pandemic outbreaks: a case study in Bangkok Metropolitan Region, Thailand. In J Geoinformatics 17:68–80.
DOI: https://doi.org/10.52939/ijg.v17i5.2013
Zhang X, Rao H, Wu Y, Huang Y, Dai H. 2020. Comparison of spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China. BMC Infect Dis 20:1–7.
DOI: https://doi.org/10.1186/s12879-020-05537-y
How to Cite
Sandar U, E. ., Laohasiriwong, W., & Sornlorm, K. . (2023). Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1183
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
List of Cited By :
Similar Articles
- Ricardo Ramìrez-Aldana , Juan Carlos Gomez-Verjan, Omar Yaxmehen Bello-Chavolla , Lizbeth Naranjo, A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
- I Gede Nyoman Mindra Jaya, Yudhie Andriyana, Bertho Tantular, Post-pandemic COVID-19 estimated and forecasted hotspots in the Association of Southeast Asian Nations (ASEAN) countries in connection to vaccination rate , Geospatial Health: Vol. 17 No. s1 (2022): Special issue on COVID-19
- Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian, Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda , Geospatial Health: Vol. 18 No. 1 (2023)
You may also start an advanced similarity search for this article.
